diff --git a/.github/stochastic_logo_dark.svg b/.github/stochastic_logo_dark.svg index 089dcbc..4755e26 100644 --- a/.github/stochastic_logo_dark.svg +++ b/.github/stochastic_logo_dark.svg @@ -1,12 +1,1144 @@ - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/.github/stochastic_logo_light.svg b/.github/stochastic_logo_light.svg index 1550508..6a387dd 100644 --- a/.github/stochastic_logo_light.svg +++ b/.github/stochastic_logo_light.svg @@ -1,12 +1,1243 @@ - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/README.md b/README.md index 5bb64ba..0056016 100644 --- a/README.md +++ b/README.md @@ -220,6 +220,36 @@ model = BaseModel.load("x/distilgpt2_lora_finetuned_alpaca")
+## Supported Models +Below is a list of all the supported models via `BaseModel` class of `xTuring` and their corresponding keys to load them. + +| Model | Key | +| -- | -- | +|Bloom | bloom| +|Cerebras | cerebras| +|DistilGPT-2 | distilgpt2| +|Falcon-7B | falcon| +|Galactica | galactica| +|GPT-J | gptj| +|GPT-2 | gpt2| +|LlaMA | llama| +|LlaMA2 | llama2| +|OPT-1.3B | opt| + +The above mentioned are the base variants of the LLMs. Below are the templates to get their `LoRA`, `INT8`, `INT8 + LoRA` and `INT4 + LoRA` versions. + +| Version | Template | +| -- | -- | +| LoRA| _lora| +| INT8| _int8| +| INT8 + LoRA| _lora_int8| + +** In order to load any model's __`INT4+LoRA`__ version, you will need to make use of `GenericLoraKbitModel` class from `xturing.models`. Below is how to use it: +```python +model = GenericLoraKbitModel('') +``` +The `model_path` can be replaced with you local directory or any HuggingFace library model like `facebook/opt-1.3b`. + ## 📈 Roadmap - [x] Support for `LLaMA`, `GPT-J`, `GPT-2`, `OPT`, `Cerebras-GPT`, `Galactica` and `Bloom` models - [x] Dataset generation using self-instruction diff --git a/docs/docs/about.md b/docs/docs/about.md deleted file mode 100644 index 4fd8bfe..0000000 --- a/docs/docs/about.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: 👤 About -description: Meet the team behind the xTuring library -sidebar_position: 9 ---- - -# About - -Welcome to xTuring, an open-source AI personalization software created by Stochastic. Our mission is to make AI more accessible to everyone, regardless of their technical background. We believe that democratizing AI will lead to a more equitable and innovative future. - -At Stochastic, we are committed to building effortless AI development, optimization and deployment to enable anyone to ship state-of-the-art AI models with production-grade performance. Our team includes top researchers and engineers who share the same vision of democratizing AI through advancements in efficient machine learning and computing. - -We are passionate about making AI accessible and approachable, and we believe that the xTuring library is an important step towards achieving that goal. With xTuring, building and controlling LLMs has never been easier. Our simple interface allows you to personalize LLMs to your own data and application, making it possible for anyone to create and deploy AI models without extensive technical expertise. - -At Stochastic, we value transparency, collaboration, and continuous improvement. We are proud to be a distributed team, working together from different parts of the world to create an open-source tool that empowers others to harness the power of AI. We believe that by working together and sharing our knowledge, we can build a more inclusive and equitable future for AI. - -Thank you for choosing xTuring. We are committed to supporting our users and ensuring that AI is accessible to all. diff --git a/docs/docs/advanced/_category_.json b/docs/docs/advanced/_category_.json new file mode 100644 index 0000000..8b1cd1e --- /dev/null +++ b/docs/docs/advanced/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "🧗🏻 Advanced Topics", + "position": 3, + "collapsed": true, + "link": { + "type": "doc", + "id": "advanced" + } +} diff --git a/docs/docs/advanced/advanced.md b/docs/docs/advanced/advanced.md new file mode 100644 index 0000000..ce1490a --- /dev/null +++ b/docs/docs/advanced/advanced.md @@ -0,0 +1,12 @@ +--- +sidebar_position: 3 +title: 🧗🏻 Advanced topics +description: Guide for people who want to customise xTuring even further. +--- + +import DocCardList from '@theme/DocCardList'; + + +# 🧗🏻 Advanced Topics + + \ No newline at end of file diff --git a/docs/docs/advanced/anymodel.md b/docs/docs/advanced/anymodel.md new file mode 100644 index 0000000..e85bfbc --- /dev/null +++ b/docs/docs/advanced/anymodel.md @@ -0,0 +1,180 @@ +--- +title: 🌦️ Work with any model +description: Use self-instruction to generate a dataset +sidebar_position: 2 +--- + + + +The `GenericModel` class makes it possible to test and fine-tune the models which are not directly available via the `BaseModel` class. Apart from the base class, we can use classes mentioned below to load the models for memory-efficient computations: + +| Class Name | Description | +| ---------- | ----------- | +| `GenericModel` | Loads the normal version of the model | +| `GenericInt8Model` | Loads the model ready to fine-tune in __INT8__ precision | +| `GenericLoraModel` | Loads the model ready to fine-tune using __LoRA__ technique | +| `GenericLoraInt8Model` | Loads the model ready to fine-tune using __LoRA__ technique in __INT8__ precsion | +| `GenericLoraKbitModel` | Loads the model ready to fine-tune using __LoRA__ technique in __INT4__ precision | + + + + + + + + +To initialize the model, simply run the following 2 commands: +```python +from xturing.models import GenericModel + +model_path = 'aleksickx/llama-7b-hf' + +model = GenericLoraModel(model_path) +``` +The _'model_path'_ can be a locally saved model and/or any model available on the HuggingFace's [Model Hub](https://huggingface.co/models). + +To fine-tune the model on a dataset, we will use the default configuration for the fine-tuning. + +```python +model.finetune(dataset=dataset) +``` + +In order to see how to load a pre-defined dataset, go [here](/overview/quickstart/prepare), and to see how to generate a dataset, refer [this](/advanced/generate) page. + +Let's test our fine-tuned model, and make some inference. + +```python +output = model.generate(texts=["Why LLM models are becoming so important?"]) +``` +We can print the `output` variable to see the results. + +Next, we need to save our fine-tuned model using the `.save()` method. We will send the path of the directory as parameter to the method to save the fine-tuned model. + +```python +model.save('/path/to/a/directory/') +``` + +We can also see our model(s) in action with a beautiful UI by launchung the playground locally. + +```python +from xturing.ui.playground import Playground + +Playground().launch() +``` + + diff --git a/docs/docs/inference/api_server.md b/docs/docs/advanced/api_server.md similarity index 53% rename from docs/docs/inference/api_server.md rename to docs/docs/advanced/api_server.md index ccca83e..5de413d 100644 --- a/docs/docs/inference/api_server.md +++ b/docs/docs/advanced/api_server.md @@ -1,19 +1,24 @@ --- -title: FastAPI server +title: ⚡️ FastAPI server description: FastAPI inference server sidebar_position: 3 --- -Once you have fine-tuned your model, you can run the inference using a FastAPI server. +# ⚡️ Running model inference with FastAPI Ssrver -### 1. Launch API server from CLI + +After successfully fine-tuning your model, you can perform inference using a FastAPI server. The following steps guide you through launching and utilizing the API server for your fine-tuned model. + +### 1. Launch API server from Command Line Interface (CLI) + +To initiate the API server, execute the following command in your command line interface: ```sh -xturing api -m "/path/to/the/model" +$ xturing api -m "/path/to/the/model" ``` :::info -Model path should be a directory containing a valid `xturing.json` config file. +Ensure that the model path you provide is a directory containing a valid xturing.json configuration file. ::: ### 2. Health check API @@ -69,3 +74,5 @@ Model path should be a directory containing a valid `xturing.json` config file. "response": ["JP Morgan is multinational investment bank and financial service headquartered in New York city."] } ``` + +By following these steps, you can effectively run your fine-tuned model for text generation through the FastAPI server, facilitating seamless inference with structured requests and responses. \ No newline at end of file diff --git a/docs/docs/advanced/generate.md b/docs/docs/advanced/generate.md new file mode 100644 index 0000000..945f773 --- /dev/null +++ b/docs/docs/advanced/generate.md @@ -0,0 +1,267 @@ +--- +title: 📚 Generate a dataset +description: Use self-instruction to generate a dataset +sidebar_position: 1 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + +To generate a dataset we will make use of **engines** that consist of third party APIs. The below ones are the currently supported ones: + + + + + OpenAI api key can be obtained [here](https://beta.openai.com/account/api-keys) + + +```python +from xturing.model_apis.openai import ChatGPT, Davinci +engine = ChatGPT("your-api-key") +# or +engine = Davinci("your-api-key") +``` + + + + + + ```python + from xturing.model_apis.cohere import Medium + engine = Medium("your-api-key") + ``` + + + + + ```python + from xturing.model_apis.ai21 import J2Grande + engine = J2Grande("your-api-key") + ``` + + + + +## From no data + +Even if we have no data, we can write a `.jsonl` file that contains the tasks/use cases we would like your model to perform well in. Continue reading to learn this file structure. + +### Write your `tasks.jsonl` + +Each line of this file needs to be a _JSON_ object with the following fields: + + + +| Name | Type | Desription | +| --- | --- | ---------- | +| __id__ | string | A unique identifier for the seed task. This can be any string that is unique within the set of seed tasks you are generating a dataset for. | +| __name__ | string | A name for the seed task that describes what it is. This can be any string that helps you identify the task. | +| __instruction__ | string | A natural language instruction or question that defines the task. This should be a clear and unambiguous description of what the task is asking the model to do. | +| __instances__ | List[Dict[str,str]] | A list of input-output pairs that provide examples of what the model should output for this task. Each input-output pair is an object with two fields: __input__ and __output__. | +| __is_classification__ | boolean | A flag that indicates whether this is a classification task or not. If this flag is set to true, the output should be a single label (e.g. a category or class), otherwise the output can be any text. The default value is false. | + +Here's an example of a task in the above mentioned format: + +```json +{ + "id": "seed_task_0", + "name": "addition", + "instruction": "Add the two numbers together", + "instances": [ + { + "input": "2 + 2", + "output": "4" + }, + { + "input": "3 + 7", + "output": "10" + } + ], + "is_classification": false +} +``` + +Here is how an sample `tasks.jsonl` file should like: + +```json +{ + "id": "seed_task_0", + "name": "breakfast_suggestion", + "instruction": "Is there anything I can eat for a breakfast that doesn't include eggs, yet includes protein, and has roughly 700-1000 calories?", + "instances": [{"input": "", "output": "Yes, you can have 1 oatmeal banana protein shake and 4 strips of bacon. The oatmeal banana protein shake may contain 1/2 cup oatmeal, 60 grams whey protein powder, 1/2 medium banana, 1tbsp flaxseed oil and 1/2 cup watter, totalling about 550 calories. The 4 strips of bacon contains about 200 calories."}], + "is_classification": false +} +{ + "id": "seed_task_1", + "name": "antonym_relation", + "instruction": "What is the relation between the given pairs?", + "instances": [{"input": "Night : Day :: Right : Left", "output": "The relation between the given pairs is that they are opposites."}], "is_classification": false +} +``` + +### Save the dataset + +In order to use the dataset we just generated and not waste time again next we need it, we can simply save our instance like shown [here](/overview/quickstart/prepare#save-a-dataset). + +### Example + +Using `.generate_dataset()` method we can generate a dataset from a list of tasks/use cases. If the generation gets interrupted, since the results are being cached, we can resume the generation just by passing the same list of tasks. If we don't want to load the cached result, then we will just delete the created folder from our working directory. + +```python +from xturing.datasets import InstructionDataset +from xturing.model_apis.openai import Davinci + +## Load the required engine +engine = Davinci("your-api-key") + +## Generate the dataset +dataset = InstructionDataset.generate_dataset(path="./tasks.jsonl", engine=engine) + +## Save the dataset instance +dataset.save('/path/to/directory') +``` + +Following parameters can be used to control the extent of generation: + +| Name | Type| Default | Desription | +| --- | --- | ----- | ---------- | +| __num_instructions_for_finetuning__ | int | 5 | The size of the generated dataset. If this number is much bigger than the number of lines in tasks.jsonl we can expect a more diverse dataset. Keep in mind that the __bigger the number__ you set, **more the credits** are going to be used from your engine. | +| __num_instructions__ | int | 10 | A cap on the size of the dataset, this can help to create a more diverse dataset. If you don't want to apply a cap, set this to the same value as *num_instructions_for_finetuning*.| + + + +## From custom data + +We can also generate a dataset from our own files. + +
+ + The files can be of one of the following formats: + + + > .csv .doc .docx .eml .epub .gif .jpg .jpeg .json .html .htm .mp3 .msg .odt .ogg .pdf .png .pptx .rtf .tiff .tif .txt .wav .xlsx .xls + +
+ +### Set up your environment +First, we need to make sure that all the necessary libraries are installed on our system. For this, we need to run the below commands: + + + + + + + This rely on you having [homebrew](http://brew.sh/) installed + + ```bash + $ brew install caskroom/cask/brew-cask + $ brew cask install xquartz + $ brew install poppler antiword unrtf tesseract swig + ``` + + + + + ```bash + $ apt-get update + $ apt-get install python-dev libxml2-dev libxslt1-dev antiword unrtf poppler-utils pstotext tesseract-ocr flac ffmpeg lame libmad0 libsox-fmt-mp3 sox libjpeg-dev swig + ``` + + + + +### Prepare the files + +Next, we just need to provide the directory path where our files are located. Files from sub-directories will also be discovered automatically. + +### Save the dataset + +In order to use the dataset we just generated and not waste time again next time we need it, we can simply save our instance like shown [here](/overview/quickstart/prepare#save-a-dataset). + + + + +### Example + +```python +from xturing.datasets import InstructionDataset +from xturing.model_apis.openai import ChatGPT + +# Load the required engine +engine = ChatGPT("your-api-key") + +## Generate the dataset +dataset = InstructionDataset.generate_dataset_from_dir(path="/path/to/directory", engine=engine) + +## Save the dataset instance +dataset.save("./my_generated_dataset") +``` + + +Following parameters can be used to customise data generation. + +| Name | Type| Default | Desription | +| --- | --- | ----- | ---------- | +| __use_self_instruct__ | bool | False| When _True_ the dataset will be augmented with self-instructions (more samples, more diverse). In this case, you also have control hover the same parameters of *generate_dataset()* method: *num_instructions*, *num_instructions_for_finetuning*. | +| __chunk_size__ | int | 8000 | The size of the chunk of text (in chars) that will be used to generate the instructions. We recommend values below 10000, but it depends on the model (engine) you are using. | +| __num_samples_per_chunk__ | int | 5 | The number of samples that will be generated for each chunk. | + + + + + + + diff --git a/docs/docs/configuration/_category_.json b/docs/docs/configuration/_category_.json new file mode 100644 index 0000000..3627c3c --- /dev/null +++ b/docs/docs/configuration/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "⚙️ Configuration", + "position": 2, + "collapsed": true, + "link": { + "type": "doc", + "id": "configuration" + } +} diff --git a/docs/docs/configuration/configuration.md b/docs/docs/configuration/configuration.md new file mode 100644 index 0000000..4818542 --- /dev/null +++ b/docs/docs/configuration/configuration.md @@ -0,0 +1,5 @@ +import DocCardList from '@theme/DocCardList'; + +# ⚙️ Configuration + + diff --git a/docs/docs/configuration/fine_tune_code.jsx b/docs/docs/configuration/fine_tune_code.jsx new file mode 100644 index 0000000..c082146 --- /dev/null +++ b/docs/docs/configuration/fine_tune_code.jsx @@ -0,0 +1,94 @@ +import React, { useEffect, useState } from 'react' +import clsx from 'clsx' +import MDXContent from '@theme/MDXContent' +import CodeBlock from '@theme/CodeBlock' + +const trainingTechniques = { + base: 'Base', + lora: 'LoRA', + lora_int8: 'LoRA INT8', + int8: 'INT8', +} + +const modelList = { + bloom: 'BLOOM', + cerebras: 'Cerebras', + distilgpt2: 'DistilGPT-2', + galactica: 'Galactica', + gptj: 'GPT-J', + gpt2: 'GPT-2', + llama: 'LLaMA', + llama2: 'LLaMA 2', + opt: 'OPT', +} + +export default function FinetuneCode( +) { + const [code, setCode] = useState({ + model: '', + technique: 'base', + }) + + let finalKey = '' + if (code.technique === 'base') { + finalKey = `${code.model}` + } else { + finalKey = `${code.model}_${code.technique}` + } + + useEffect(() => { + setCode({ + model: 'llama', + technique: 'lora' + }); + }, []); + + return ( +
+ + + + + + + +
+ ) +} \ No newline at end of file diff --git a/docs/docs/configuration/finetune_configure.md b/docs/docs/configuration/finetune_configure.md new file mode 100644 index 0000000..6f91dbf --- /dev/null +++ b/docs/docs/configuration/finetune_configure.md @@ -0,0 +1,101 @@ +--- +title: 🏋🏻‍♂️ Fine-tuning +description: Fine-tuning parameters +sidebar_position: 2 +--- + +import FinetuneCode from './fine_tune_code'; + + + +xTuring is easy to use. The library already loads the best parameters for each model by default. + +For advanced usage, you can customize the `finetuning_config` attribute of the model object. + +In this tutorial, we will be loading one of the [supported models](/overview/supported_models) and customizing it's fine-tune configuration before calibrating the model to the desired dataset. + +### Load the model and the dataset +First, we need to load the model and the dataset we want to use. + + + + +### Load the config object + +Next, we need to fetch model's fine-tune configuration using the below command. + +```python +finetuning_config = model.finetuning_config() +``` + +Print the `finetuning_config` object to check the default configuration. + +### Customize the configuration +Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters). + +```python +finetuning_config.batch_size = 64 +finetuning_config.num_train_epochs = 1 +finetuning_config.learning_rate = 1e-5 +finetuning_config.weight_decay = 0.01 +finetuning_config.optimizer_name = "adamw" +finetuning_config.output_dir = "training_dir/" +``` +### Start the fine-tuning +Now, we can run tune-up the model on our dataset to see how our set configuration works. + +```python +model.finetune(dataset=instruction_dataset) +``` + +### Parameters + + + +| Name | Type | Range | Default | Desription | +| --- | --- | ----- | ------- | ---------- | +| learning_rate | float | >0 | 1e-5 | The initial learning rate for the optimizer. | +| gradient_accumulation_steps | int | ≥1 | 1 | The number of updates steps to accumulate the gradients for, before performing a backward/update pass. | +| batch_size | int | ≥1 | 1 | The batch size per device (GPU/TPU core/CPU…) used for training. | +| weight_decay | float | ≥0 | 0.00 | The weight decay to apply to all layers except all bias and LayerNorm weights in the optimizer. | +| warmup_steps | int | ≥0 | 50 | The number of steps used for a linear warmup from 0 to learning_rate. | +| max_length | int | ≥1 | 512 | The maximum length when tokenizing the inputs. | +| num_train_epochs | int | ≥1 | 1 | The total number of training epochs to perform. | +| eval_steps | int | ≥1 | 5000 | The number of update steps between two evaluations. | +| save_steps | int | ≥1 | 5000 | The number of update steps before two checkpoint saves. | +| logging_steps | int | ≥1 | 10 | The number of update steps between two logs. | +| max_grad_norm | float | ≥0 | 2.0 | The maximum gradient norm (for gradient clipping). | +| save_total_limit | int | ≥1 | 4 | If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. | +| optimizer_name | string | N/A | adamw | The optimizer to be used. | +| output_dir | string | N/A | saved_model | The output directory where the model predictions and checkpoints will be written. | + + + + + diff --git a/docs/docs/configuration/inference_code.jsx b/docs/docs/configuration/inference_code.jsx new file mode 100644 index 0000000..6425fcd --- /dev/null +++ b/docs/docs/configuration/inference_code.jsx @@ -0,0 +1,90 @@ +import React, { useEffect, useState } from 'react' +import clsx from 'clsx' +import CodeBlock from '@theme/CodeBlock' + +const trainingTechniques = { + base: 'Base', + lora: 'LoRA', + lora_int8: 'LoRA INT8', + int8: 'INT8', +} + +const modelList = { + bloom: 'BLOOM', + cerebras: 'Cerebras', + distilgpt2: 'DistilGPT-2', + galactica: 'Galactica', + gptj: 'GPT-J', + gpt2: 'GPT-2', + llama: 'LLaMA', + llama2: 'LLaMA 2', + opt: 'OPT', +} + +export default function InferenceCode( +) { + const [code, setCode] = useState({ + model: '', + technique: 'base', + }) + + let finalKey = '' + if (code.technique === 'base') { + finalKey = `${code.model}` + } else { + finalKey = `${code.model}_${code.technique}` + } + + useEffect(() => { + setCode({ + model: 'llama', + technique: 'lora' + }); + }, []); + + return ( +
+ + + + + + + +
+ ) +} \ No newline at end of file diff --git a/docs/docs/configuration/inference_configure.md b/docs/docs/configuration/inference_configure.md new file mode 100644 index 0000000..4339d72 --- /dev/null +++ b/docs/docs/configuration/inference_configure.md @@ -0,0 +1,117 @@ +--- +title: 👨🏻‍🏫 Inference +description: Inference parameters +sidebar_position: 2 +--- + +import InferenceCode from './inference_code'; + + + +xTuring is easy to use. The library already loads the best parameters for each model by default. + +For advanced usage, you can customize the `generation_config` attribute of the model object. + + + +In this tutorial, we will be loading one of the [supported models](/overview/supported_models) and customizing it's generation configuration before running inference. + +### Load the model + +First, we need to load the model we want to use. + + + +### Load the config object +Next, we need to fetch model's generation configuration using the below command. + +```python +generation_config = model.generation_config() +``` + +We can print the `generation_config` object to check the default configuration. + +### Customize the configuration + +Now, we can customize the generation configuration as we wish. All the customizable parameters are list [below](#parameters). + +```python +generation_config.max_new_tokens = 256 +``` + +### Test the model +Lastly, we can run inference using the below command to see how our set configuration works. + +```python +output = model.generate(texts=["Why are the LLM models important?"]) +``` +We can print the `output` object to see the results. + +### Parameters + + + +| Name | Type | Range | Default | Desription | +| --- | --- | ----- | ------- | ---------- | +| max_new_tokens | int | ≥1 | 256 | The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. | +| penalty_alpha | int | [0,1) | 0.6 | For contrastive search decoding. The values balance the model confidence and the degeneration penalty. | +| top_k | float | ≥0 | 4 | For contrastive search and sampling decoding method. The number of highest probability vocabulary tokens to keep for top-k-filtering. | +| do_sample | bool | {true, false} | false | Whether or not to use sampling. | +| top_p | float | ≥0 | 0 | For sampling decoding method. If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. | + + + + diff --git a/docs/docs/contributing/_category_.json b/docs/docs/contributing/_category_.json index 4f0dcc0..bc9b01c 100644 --- a/docs/docs/contributing/_category_.json +++ b/docs/docs/contributing/_category_.json @@ -1,8 +1,9 @@ { "label": "🤝 Contributing", - "position": 8, - "link": { - "type": "generated-index" + "position": 4, + "link": { + "type": "doc", + "id": "contributing" }, "collapsed": true } diff --git a/docs/docs/contributing/contributing.md b/docs/docs/contributing/contributing.md new file mode 100644 index 0000000..e8dabd5 --- /dev/null +++ b/docs/docs/contributing/contributing.md @@ -0,0 +1,5 @@ +import DocCardList from '@theme/DocCardList'; + +# 🤝 Contributing + + \ No newline at end of file diff --git a/docs/docs/contributing/general_rules.md b/docs/docs/contributing/general_rules.md deleted file mode 100644 index eb91abf..0000000 --- a/docs/docs/contributing/general_rules.md +++ /dev/null @@ -1,59 +0,0 @@ ---- -title: Guide -description: How to contribute to xTuring? -sidebar_position: 1 ---- - -# Contributing guide - -We are excited that you are interested in contributing to our open-source project. xTuring is an open-source library that is maintained by a community of developers and researchers. We welcome contributions of all kinds, including bug reports, feature requests, and code contributions. - -### Getting started -To start contributing to xTuring, we recommend that you familiarize yourself with our library by reading through our documentation and exploring the codebase. Once you are comfortable with the library, you can start contributing by: - -1. **Adding more models and features**: We are always looking to expand the capabilities of xTuring by adding more models and features. If you have expertise in a particular area of machine learning and would like to contribute a new model, we welcome your input. If you have an idea for a new feature, we encourage you to share your ideas with us. -2. **Adding test cases**: Adding tests is an essential part of maintaining and improving the quality of our codebase. By adding test cases, you can help ensure that xTuring is robust and reliable. -3. **Maintaining code**: Maintaining code is an important part of open-source development. By helping with code maintenance, you can ensure that xTuring is up-to-date, bug-free, and user-friendly. -4. **Helping with documentation**: As an open-source project, xTuring relies on documentation to help users understand how to use the library. If you have a talent for technical writing, we welcome your contributions to our documentation. - -### How to contribute -To contribute to xTuring, follow these steps: - -1. Fork the repository on GitHub -2. Clone your forked repository to your local machine - -```bash -git clone https://github.com//xturing.git -``` - -3. Create a new branch for your changes emerging from the `dev` branch. - -```bash -git checkout dev -git checkout -b -``` - -4. Use pre-commit hooks to ensure your code is properly formatted - -```bash -pip install pre-commit -pre-commit install -pre-commit install --hook-type commit-msg -``` - -5. Make your changes and commit them - -```bash -git add -git commit -m "Commit message" -``` - -6. Push your changes to your forked repository - -```bash -git push origin -``` - -7. Create a pull request to the `dev` branch with a clear description of your changes and why they are needed. We will review your changes as soon as possible and provide feedback. Once your changes have been approved, they will be merged into the dev branch. - -We value the contributions of our community and are committed to creating an open and collaborative development environment. Thank you for considering contributing to xTuring! diff --git a/docs/docs/contributing/how_to_contribute.md b/docs/docs/contributing/how_to_contribute.md new file mode 100644 index 0000000..ce09598 --- /dev/null +++ b/docs/docs/contributing/how_to_contribute.md @@ -0,0 +1,129 @@ +--- +title: How to contribute to xTuring? +description: How to contribute to xTuring? +sidebar_position: 2 +--- + + + +We are excited that you are interested in contributing to our open-source project. `xTuring` is an open-source library that is maintained by a community of developers and researchers. We welcome contributions of all kinds, including bug reports, feature requests, and code contributions. + +## Ways to contribute +To start contributing to xTuring, we recommend that you familiarize yourself with our library by reading through our documentation and exploring the codebase. Once you are comfortable with the library, you can start contributing by: + +1. **Fixing oustanding issues with existing code** +2. **Adding new models and features** +3. **Adding test cases** +4. **Maintaining code** +5. **Helping with documentation** + +If you are skeptical where to start, here are some good [First Issues](https://github.com/stochasticai/xTuring/labels/good%20first%20issue) for you to go head on with. + +### Fixing oustanding issues +If you come across some issue with the current code and have a solution to it, feel free to [contribute](https://github.com/stochasticai/xTuring/issues) and make a [Pull Request](https://github.com/stochasticai/xTuring/compare)! + + +### Adding models and features +We are always looking to expand the capabilities of xTuring by adding more models and features. If you have expertise in a particular area of machine learning and would like to contribute a new model, we welcome your input. If you have an idea for a new feature, we encourage you to share your ideas with us by submitting an [issue](https://github.com/stochasticai/xTuring/issues)! + +### Found some interesting test cases? +Adding tests is an essential part of maintaining and improving the quality of our codebase. By adding test cases, you can help ensure that xTuring is robust and reliable. + +### Want to update the existing code? +Maintaining code is an important part of open-source development. By helping with code maintenance, you can ensure that xTuring is up-to-date, bug-free, and user-friendly. + +### Wish to add to documentation? +As an open-source project, xTuring relies on documentation to help users understand how to use the library. If you have a talent for technical writing, we welcome your contributions to our documentation. + + +## Create a pull request +Before you make a PR, make sure to search through existing PRs and issues to make sure nobody else is already working on it. If unsure, it is best to open an issue to get some feedback. + +In order to start and meet less hurdles on the way, it would be a good idea to be have _git_ proficiency to contribute to `xTuring`. If you get stuck somewhere, type _git --help_ in a shell and find your command! + +You'll need Python 3.8 or above to contribute to __`xTuring`__. To start contributing, follow the below steps: + +1. Fork the [repository](https://github.com/stochastciai/xTuring) by clicking on the [Fork](https://github.com/stochasticai/xTuring/fork) button on repository's GitHub page. This will create a copy of the main codebase under your GitHub account. + +2. Clone your forked repository to your local machine, and add the base repository as a remote. + + ```bash + $ git clone https://github.com//xTuring.git + $ cd xTuring + $ git remote add upstream https://github.com/stochastic/xTuring.git + ``` + +3. Create a new branch for your changes emerging from the `dev` branch. + + ```bash + $ git checkout dev + $ git checkout -b a-descriptive-name-for-your-changes + ``` + 🚨 **Do not** checkout from the _main_ branch or work on it. + +4. Make sure you have pre-commit hooks installed and set up to ensure your code is properly formatted before being pushed to _git_. + + ```bash + $ pip install pre-commit + $ pre-commit install + $ pre-commit install --hook-type commit-msg + ``` + +5. Set up a development environment by running the following command in a virtual environment: + + Here are the guides to setting up [virtual environment](https://www.freecodecamp.org/news/how-to-setup-virtual-environments-in-python/) and [conda environment](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html). + + ```bash + $ pip install -e . + ``` + + If `xTuring` is already installed in your virtual environment, remove it with _pip uninstall xturing_ before reinstlling it in editable mode with the _-e_ flag. + + To get a detailed version of installing the required libraries and `xTuring` for development, refer [here](/contributing/setting_up#editable-install). + +6. Develop features on your branch + As you work on your code, you should make sure the test suite passes. Run all the tests to make sure nothing breaks once your code is pushed to GitHub using the following command: + ```bash + $ pytest tests/ + ``` + + Once you are satisified with your changes and all the tests pass, add changed files with _git add_ and record your changes with _git commit_: + + ```bash + $ git add + $ git commit -m "" + ``` + Make sure to write __good commit messages__ to clearly communicate the changes you made! + + Before pushing the code to GitHub and making a PR, ensure you have your copy of code up-to-date with the main repository. To do so, rebase your branch on _upstream/branch_: + ```bash + $ git fetch upstream + $ git rebase upstream/dev + ``` + +7. Push your changes to your forked repository + + ```bash + $ git push -u origin a-descriptive-name-for-your-changes + ``` + +8. Now, you can go your fork of the repository on GitHub and click on __[Pull Request](https://github.com/stochasticai/xTuring/compare)__ to open a pull request to the __dev__ branch of the original repository with a clear description of your changes and why they are needed. We will review your changes as soon as possible and provide feedback. Once your changes have been approved, they will be merged into the _dev_ branch. + +9. Maintainers might request changes, it is fine if they do, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. + +We value the contributions of our community and are committed to creating an open and collaborative development environment. Thank you for considering contributing to xTuring! + +### Sync a forked repository with upstream dec (the original xTuring repository) + +To prevent triggering notifications and adding reference notes to each upstream pull request, adhere to these guidelines when making updates to the primary branch of a forked repository: + +1. Whenever feasible, bypass synchronizing with the upstream repository by merging changes directly into the _dev_ branch of the forked repository. Evade the use of a separate branch and pull request for upstream synchronization. + +2. In cases where a pull request is genuinely required, follow these steps once you've switched to your branch: + + ```bash + $ git checkout -b your-branch-for-syncing + $ git pull --squash --no-commit upstream dev + $ git commit -m '' + $ git push --set-upstream origin your-branch-for-syncing + ``` \ No newline at end of file diff --git a/docs/docs/contributing/model_contributing.md b/docs/docs/contributing/model_contributing.md index 57c6e7f..a64e779 100644 --- a/docs/docs/contributing/model_contributing.md +++ b/docs/docs/contributing/model_contributing.md @@ -1,100 +1,111 @@ --- -title: Adding new models +title: 🤖 Adding new model description: Guide on how to add new models to xTuring -sidebar_position: 2 +sidebar_position: 3 --- -# Adding new models +# 🤖 How to add a model to _xTuring_? We appreciate your interest in contributing new models to xTuring. This guide will help you understand how to add new models and engines to the library. ## Prerequisites -Before you start, make sure you are familiar with the xTuring codebase, particularly the structure of the models and engines folders. Familiarity with PyTorch and the Hugging Face Transformers library is also essential. +Before we start, we need to make sure that we are familiar with the `xTuring` codebase, particularly the structure of the [__models__](https://github.com/stochasticai/xTuring/tree/main/src/xturing/models) and [__engines__](https://github.com/stochasticai/xTuring/tree/main/src/xturing/engines) folders. Familiarity with _PyTorch_ and the _Hugging Face Transformers_ library is also essential. ## Steps to add a new model -1. **Create a new engine**: In the `engines` folder, create a new file with the name of your engine (e.g., `my_engine.py`). Define a new engine class that inherits from an appropriate base engine class (e.g., `CausalEngine` for causal models, for `Seq2Seq` you need to add base class first). Implement necessary methods and attributes, following the structure of existing engines (e.g., `gptj_engine.py`). +### 1. First, create a new engine +In the `engines` folder, we are required to create a new file with the name of our engine (e.g., `my_engine.py`). Define a new engine class that inherits from an appropriate base engine class (e.g., `CausalEngine` for causal models, for `Seq2Seq` you need to add base class first). Implement necessary methods and attributes, following the structure of existing engine. We can refer [gptj_engine.py](https://github.com/stochasticai/xTuring/blob/main/src/xturing/engines/gptj_engine.py). - ```python - from xturing.engines.causal import CausalEngine +```python +from xturing.engines.causal import CausalEngine - class MyEngine(CausalEngine): - config_name: str = "my_engine" +class MyEngine(CausalEngine): + config_name: str = "my_engine" - def __init__(self, model_name: str, weights_path: Optional[Union[str, Path]] = None): - super().__init__(model_name, weights_path) - ``` + def __init__(self, model_name: str, weights_path: Optional[Union[str, Path]] = None): + super().__init__(model_name, weights_path) +``` -2. **Create a new model**: In the `models` folder, create a new file with the name of your model (e.g., `my_model.py`). Define a new model class that inherits from an appropriate base model class (e.g., `CausalModel` for causal models, for `Seq2Seq` you need to add base class first). Implement necessary methods and attributes, following the structure of existing models (e.g., `gptj.py`). +### 2. Next, create a new model +In the `models` folder, we need to create a new file with the name of our model (e.g., `my_model.py`). First, define a new model class that inherits from an appropriate base model class (e.g., `CausalModel` for causal models, for `Seq2Seq` you need to add base class first). Then, implement necessary methods and attributes, following the structure of existing models. We can refer [gptj.py](https://github.com/stochasticai/xTuring/blob/main/src/xturing/models/gptj.py). - ```python - from xturing.models.causal import CausalModel - from xturing.engines.my_engine import MyEngine +```python +from xturing.models.causal import CausalModel +from xturing.engines.my_engine import MyEngine - class MyModel(CausalModel): - config_name: str = "my_model" +class MyModel(CausalModel): + config_name: str = "my_model" - def __init__(self, weights_path: Optional[str] = None): - super().__init__(MyEngine.config_name, weights_path) - ``` + def __init__(self, weights_path: Optional[str] = None): + super().__init__(MyEngine.config_name, weights_path) +``` -3. **Update the model and engine registries**: Add your new model to the `model` and `engine` registry in `xturing.{models|engines}.__init__.py`. This will allow users to create instances of your model using the `BaseModel.create('')` method. +### 3. Then, update the model and engine registries +We have to add our new model to the [`model`](https://github.com/stochasticai/xTuring/blob/main/src/xturing/models/__init__.py) and [`engine`](https://github.com/stochasticai/xTuring/blob/main/src/xturing/engines/__init__.py) registries in `xturing.{models|engines}.__init__.py`. This will allow users to create instances of your model using the `BaseModel.create('')` method. -4. **Update the config files**: In the `config` folder, add your new model key and their respective hyperparameters to be run by default in `finetuning_config.yaml` and `generation_config.yaml` files. - ```yaml - # finetuning_config.yaml - my_model: - learning_rate: 1e-4 - weight_decay: 0.01 - num_train_epochs: 3 - batch_size: 8 - max_length: 256 +### 4. Alongside, update the config files +In the `config` folder, we need to add our new model key and their respective hyperparameters to be run by default in `finetuning_config.yaml` and `generation_config.yaml` files. +```yaml +# finetuning_config.yaml +my_model: + learning_rate: 1e-4 + weight_decay: 0.01 + num_train_epochs: 3 + batch_size: 8 + max_length: 256 - # generation_config.yaml - my_model: - max_new_tokens: 256 - do_sample: false +# generation_config.yaml +my_model: + max_new_tokens: 256 + do_sample: false - ``` +``` -5. **Add tests**: If your model is small enough you can add tests for your new model in the `tests` folder. You can use existing tests as a reference. If your model is too large to be included in the tests, you can add a notebook in the `examples` folder to demonstrate how to use your model. +### 5. Do not forget to add tests +If our model is small enough we can add tests for our new model in the [tests/](https://github.com/stochasticai/xTuring/tree/main/tests/xturing) folder. We can use existing tests as a reference. If our model is too large to be included in the tests, we can add a notebook in the [examples/](https://github.com/stochasticai/xTuring/tree/main/examples) folder to demonstrate how to use our model. -6. **Update the documentation**: Update the documentation to include your new model and engine. Add a new Markdown file in the docs folder with a tutorial on how to use your model. +### 6. Update the documentation +We should not forget to update the documentation to include our new model and engine. We can do so by adding a new _Markdown_ file in the [examples/](https://github.com/stochasticai/xTuring/tree/main/examples) folder with a tutorial on how to use our model. -7. **Submit a pull request**: Once you have completed the above steps, submit a pull request to the `dev` branch. Provide a clear description of your changes and why they are needed. We will review your changes as soon as possible and provide feedback. Once your changes have been approved, they will be merged into the `dev` branch. +### 7. At last, submit a pull request +Once we have completed the above steps, we are ready to submit a pull request to the `dev` branch. For that, we should provide a clear description of our changes and why they are needed. Then the seasoned contributors will review our changes as soon as possible and provide feedback. Once our changes have been approved, they will be merged into the `dev` branch. ## Steps to add a LoRA model -1. **Create a new engine**: analogously to the steps above, create a new engine in the `engines` folder. The new engine should inherit from the `CausalLoraEngine` base class. You can use `gptj_engine.py` file as a reference. - ```python - from xturing.engines.causal import CausalLoraEngine +### 1. First, Create a new engine +Analogous to the steps above, create a new engine in the `engines` folder. The new engine should inherit from the `CausalLoraEngine` base class. We can use [`gptj_engine.py`](https://github.com/stochasticai/xTuring/blob/main/src/xturing/engines/gpt2_engine.py) file as a reference. - class MyLoraEngine(CausalLoraEngine): - config_name: str = "my_engine_lora" +```python +from xturing.engines.causal import CausalLoraEngine - def __init__(self, weights_path: Optional[Union[str, Path]] = None): - super().__init__( - model_name, - weights_path, - target_modules=[], - ) - ``` - The `target_modules` parameter is the list of identifiers used to denote attention layers of your model. For example, for *GPTJ* model, the attention layers are denoted with `q_proj` and `v_proj`. +class MyLoraEngine(CausalLoraEngine): + config_name: str = "my_engine_lora" -2. **Create a new model**: analogously to the steps above, create a new model in the `models` folder. The new model should inherit from the `CausalLoraModel` base class. You can use the `gptj.py` file as a reference. - ```python - from xturing.models.causal import CausalLoraModel - from xturing.engines.my_engine import MyLoraEngine + def __init__(self, weights_path: Optional[Union[str, Path]] = None): + super().__init__( + model_name, + weights_path, + target_modules=[], + ) +``` - class MyModelLora(CausalLoraModel): - config_name: str = "my_model_lora" +The `target_modules` parameter is the list of identifiers used to denote attention layers of your model. For example, for *GPTJ* model, the attention layers are denoted with `q_proj` and `v_proj`. - def __init__(self, weights_path: Optional[str] = None): - super().__init__(MyLoraEngine.config_name, weights_path) - ``` +### 2. Next, create a new model +Analogous to the steps above, create a new model in the `models` folder. The new model should inherit from the `CausalLoraModel` base class. We can use the [`gptj.py`](https://github.com/stochasticai/xTuring/blob/main/src/xturing/models/gptj.py) file as a reference. +```python +from xturing.models.causal import CausalLoraModel +from xturing.engines.my_engine import MyLoraEngine - Next, follow steps 3 through 7 as mentioned in the above steps +class MyModelLora(CausalLoraModel): + config_name: str = "my_model_lora" + + def __init__(self, weights_path: Optional[str] = None): + super().__init__(MyLoraEngine.config_name, weights_path) +``` + +### Next, follow steps 3 through 7 as mentioned in the above steps Thank you for your contribution to xTuring! -> Note: For ease of review, make sure to run your model locally with a sample prompt and paste your output(s) along with the sample prompt in the description space of your pull request. +> Note: For ease of review, make sure to run your model locally with a sample prompt and paste your output(s) along with the sample prompt in the description space of your pull request. diff --git a/docs/docs/contributing/pythonapi.md b/docs/docs/contributing/pythonapi.md new file mode 100644 index 0000000..ea993f9 --- /dev/null +++ b/docs/docs/contributing/pythonapi.md @@ -0,0 +1,123 @@ +--- +title: 🐍 Python API +description: Developers' guide for API +sidebar_position: 4 +--- + + + +This section includes the API documentation from the Finetuner codebase, as extracted from the [docstrings](https://peps.python.org/pep-0257/) in the code. + +## `BaseModel` + +[`BaseModel.load(weights_dir_or_model_name)`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/models/base.py#L15) +> Load a model from your local machine or xTuring Hub. +> +> **Parameters**: +> - **weights_dir_or_model_name** *(str)*: Path to a local model to be load or a model from `xTuring` Hub. + + + +## `CausalModel` + +[`model.finetune(dataset, logger = True)`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/models/causal.py#L116) +> Fine-tune the in-memory model on the desired dataset. +> +> **Parameters**: +> - **dataset** *(Union[TextDataset, InstructionDataset])*: The object of either of the 2 dataset classes specified in the library. If not passed, will throw an error. +> - **logger** *(Union[Logger, Iterable[Logger], bool])*: If you want to log the progress in the default logger, pass nothing explicitly. Else, you can pass your own logger. + +[`model.generate(texts = None ,dataset = None, batch_size = 1)`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/models/causal.py#L158) +> Use the in-memory model to generate outputs by passing either a `dataset` as an argument or `texts` as an argument which would be a list of strings. +> +> **Parameters**: +> - **texts** *(Optional[Union[List[str], str]])*: Can be a single string or a list of strings on which you want to test your in-memory model. +> - **dataset** *(Optional[Union[TextDataset, InstructionDataset]])*: The object of either of the 2 dataset classes specified in the library. +> - **batch_size** *(Optional[int])*: For faster processing given your machine constraints, you can configure the batch size of the model. Higher the batch size, more the parallel compute, faster you will get your result. + +[`model.evaluate(dataset, batch_size = 1)`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/models/causal.py#L312) +> Evaluate the in-memory model. +> +> **Parameters**: +> - **dataset** *(Optional[Union[TextDataset, InstructionDataset]])*: The object of either of the 2 dataset classes specified in the library. +> - **batch_size** *(Optional[int])*: For faster processing given your machine constraints, you can configure the batch size of the model. Higher the batch size, more the parallel compute, faster you will get your result. + +[`model.save(path)`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/models/causal.py#L212) +> Save your in-memory model. +> +> **Parameters**: +> - **path** *(Union[str, Path])*: The path to the directory where you want to save your in-memory model. Can either be a string or a `Path` object (class found in _pathlib_). + + +## `InstructionDataset` + +[`dataset.from_jsonl`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/datasets/instruction_dataset.py#L80) +> Get an instruction data from a `.jsonl` file where each line is a json object with keys _text_, _instruction_ and _target_. +> +> **Parameters**: +> - **path** *(Path)*: the path to the _.jsonl_ file. Should be an object of the class `Path` from the _pathlib_. + +[`InstructionDataset.generate_dataset`](https://github.com/stochasticai/xTuring/blob/9b98c68af8391c7a0f48a141178b70a1a8e47c06/src/xturing/datasets/instruction_dataset.py#L127) +> Generate your custom dataset given the HuggingFace engine. +> +> **Parameters**: +> - **path** *(str)*: a string of the path where you want to save the generated dataset. +> - **engine** *(TextGenerationAPI)*: should be an object of one of the classes mentioned in the [*model_apis*](https://github.com/stochasticai/xTuring/tree/main/src/xturing/model_apis) directory. +> - **num_instructions** *(Optinoal[int])*: a cap on the size of sample set to be generated. Helps you create a more diverse dataset. +> - **num_instructions_for_finetuning** *(Optinoal[int])*: size of the sample set to be generated. Uses up the credits from your account. _Use this number very carefully._ + + + + + \ No newline at end of file diff --git a/docs/docs/contributing/setting_up.md b/docs/docs/contributing/setting_up.md new file mode 100644 index 0000000..22903a9 --- /dev/null +++ b/docs/docs/contributing/setting_up.md @@ -0,0 +1,43 @@ +--- +title: 🍽️ Setting up +description: Setup xTuring for contribution +sidebar_position: 1 +--- + +Before we can start contributing to `xTuring`, we need to make sure we have all the neccessary code available to us in our working diretory and up-to-date. Moreover, to be able to test our changes, we need to do something more than just install the latest stable version of the library from _pip_. + +To just get the latest version of developments on `xTuring`, we need to [install from source](#install-from-source). But in order to test our changes we did locally, we need to go a step ahead and do something called an [editable install](#editable-install). Let's dive into them right away! +## Install from source +Install `xTuring` directly from GitHub by running the following command on your cmd/terminal: +```bash +$ pip install git+https://github.com/stochasticai/xTuring +``` +The above command will install the main version instead of the latest stable version of `xTuring`. This way of installing is a good way of being up-to-date with the latest development. There might be bugs which would be fixed in the main version but not yet rolled-out on pip. But at the same time this does not guarantee a stable version, this was of installation might break somewhere. We try our best to keep the main version free from any pitfalls and resolved of all the issues. If you run into a problem, please open up an [issue](https://github.com/stochasticai/xTuring/issues/new) and if possible, make a [contribution](https://github.com/stochasticai/xTuring/compare)! + +Finally, you can test if `xTuring` has been properly installed by running the following commands on your cmd/terminal: +```bash +$ python +>>> from xturing.models import BaseModel +>>> model = BaseModel.create('opt') +>>> outputs = model.generate(texts=['Hi How are you?']) +``` +Then print the outputs variable to see what the LLM generated based on the input prompt. + +## Editable Install +The editable install is needed when you: +1. install directly from the source +2. wish to make a contribution to the `xTuring` library. + +To do so, clone the library from _GitHub_ and install the necessary packages by running the following commands: +```bash +$ git clone https://github.com/stochasticai/xTuring.git +$ cd xTuring +$ pip install -e . +``` +Now you will be able to test the changes you do to the library as Python will now look at `~/xTuring/` directory in addition to the normal installation of the library. + +Next, in order to update your version, run the following command inside the `~/xTuring/` directory: +```bash +$ git pull +``` +This will fast-forward your main version to the latest developments to the library, you can freely play around and use those. diff --git a/docs/docs/datasets/_category_.json b/docs/docs/datasets/_category_.json deleted file mode 100644 index 1c3e793..0000000 --- a/docs/docs/datasets/_category_.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "label": "📂 Datasets", - "position": 3, - "link": { - "type": "generated-index" - }, - "collapsed": true -} diff --git a/docs/docs/datasets/generate.md b/docs/docs/datasets/generate.md deleted file mode 100644 index 784bcac..0000000 --- a/docs/docs/datasets/generate.md +++ /dev/null @@ -1,194 +0,0 @@ ---- -title: Generate a dataset -description: Use self-instruction to generate a dataset -sidebar_position: 2 ---- - -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# Dataset generation - -To generate a dataset we make use of **engines** that consist of third party APIs. These are the currently supported ones: - - - - - OpenAI api key can be obtained [here](https://beta.openai.com/account/api-keys) - - ```python - from xturing.model_apis.openai import Davinci, ChatGPT - engine = Davinci("your-api-key") - # engine = ChatGPT("your-api-key") - ``` - - - - ```python - from xturing.model_apis.cohere import Medium - engine = Medium("your-api-key") - ``` - - - - ```python - from xturing.model_apis.ai21 import J2Grande - engine = J2Grande("your-api-key") - ``` - - - -## From no data - -Even if you have no data, you can write a *.jsonl* file that contains the tasks/use cases you would like your model to perform well in. Continue reading to learn this file structure. - -### Write your tasks.jsonl - -Each line of this file needs to be a JSON object with the following fields: - -> ##### id (string, required) -> A unique identifier for the seed task. This can be any string that is unique within the set of seed tasks you are generating a dataset for. -> -> ##### name (string, required) -> A name for the seed task that describes what it is. This can be any string that helps you identify the task. -> -> ##### instruction (string, required) -> A natural language instruction or question that defines the task. This should be a clear and unambiguous description of what the task is asking the model to do. -> ->##### instances ([{input: string, output: string}, ...], required) ->A list of input-output pairs that provide examples of what the model should output for this task. Each input-output pair is an object with two fields: "input" and "output". -> ->##### is_classification (boolean, optional) ->A flag that indicates whether this is a classification task or not. If this flag is set to true, the output should be a single label (e.g. a category or class), otherwise the output can be any text. The default value is false. - -Here's an example of a task in this format: - -```json -{ - "id": "seed_task_0", - "name": "addition", - "instruction": "Add the two numbers together", - "instances": [ - { - "input": "2 + 2", - "output": "4" - }, - { - "input": "3 + 7", - "output": "10" - } - ], - "is_classification": false -} -``` - -Here is an example of a tasks.jsonl file: - -```json -{"id": "seed_task_0", "name": "breakfast_suggestion", "instruction": "Is there anything I can eat for a breakfast that doesn't include eggs, yet includes protein, and has roughly 700-1000 calories?", "instances": [{"input": "", "output": "Yes, you can have 1 oatmeal banana protein shake and 4 strips of bacon. The oatmeal banana protein shake may contain 1/2 cup oatmeal, 60 grams whey protein powder, 1/2 medium banana, 1tbsp flaxseed oil and 1/2 cup watter, totalling about 550 calories. The 4 strips of bacon contains about 200 calories."}], "is_classification": false} -{"id": "seed_task_1", "name": "antonym_relation", "instruction": "What is the relation between the given pairs?", "instances": [{"input": "Night : Day :: Right : Left", "output": "The relation between the given pairs is that they are opposites."}], "is_classification": false} -``` - - -### Example - -Using generate method you can generate a dataset from a list of tasks/use cases. If the generation gets interrupted, since we are caching the results, you can resume the generation by passing the same list of tasks. If you don't want to load the cached result delete the created folder in your current directory. - -```python -from xturing.datasets import InstructionDataset -from xturing.model_apis.openai import Davinci - -engine = Davinci("your-api-key") -dataset = InstructionDataset.generate_dataset(path="./tasks.jsonl", engine=engine) -``` - -:::info -***generate_dataset()*** method accepts the following additional arguments: -``` -...generate_dataset( - num_instructions_for_finetuning=5, - num_instructions=10 -) -``` - -### num_instructions_for_finetuning -The size of the generated dataset. If this number is much bigger than the number of lines in tasks.jsonl we can expect a more diverse dataset. Keep in mind that the bigger the number you set **the more the credits** that are going to be used from your engine. The default value is 5. - -### num_instructions -A cap on the size of the dataset, this can help to create a more diverse dataset. If you don't want to apply a cap, set this to the same value as *num_instructions_for_finetuning*. The default value is 10. - -::: - -## From your data - -You can also generate a dataset from your own files. The files can be of one of the formats: - -> .csv .doc .docx .eml .epub .gif .jpg .jpeg .json .html .htm .mp3 .msg .odt .ogg .pdf .png .pptx .rtf .tiff .tif .txt .wav .xlsx .xls - -### Setting up the environment -Before going ahead you need to install some libraries that we use to support the text extraction. - - - - - - This rely on you having [homebrew](http://brew.sh/) installed - - ```bash - brew install caskroom/cask/brew-cask - brew cask install xquartz - brew install poppler antiword unrtf tesseract swig - ``` - - - - - ```bash - apt-get update - apt-get install python-dev libxml2-dev libxslt1-dev antiword unrtf poppler-utils pstotext tesseract-ocr flac ffmpeg lame libmad0 libsox-fmt-mp3 sox libjpeg-dev swig - ``` - - - - -### Preparing your files - -You just need to provide the directory path where your files are located. Files from sub-directories will also be discovered automatically. - -:::caution -- The file name is used as a context for the instruction generation. So, it is recommended to use meaningful names. -- Currently only **ChatGPT** engine is supported. -- Don't forget to [**save**](/datasets/usage#save-a-dataset) the generated dataset. -::: - -### Example - -```python -from xturing.datasets import InstructionDataset -from xturing.model_apis.openai import ChatGPT - -engine = ChatGPT("your-api-key") -dataset = InstructionDataset.generate_dataset_from_dir(path="path/to/directory", engine=engine) -dataset.save("./my_generated_dataset") -[print(i) for i in dataset] # print the generated dataset -``` - -:::info -***generate_dataset_from_dir()*** method accepts the following additional arguments: -``` -...generate_dataset_from_dir( - use_self_instruct=False, - chunk_size=8000, - num_samples_per_chunk=5, -) -``` - -### use_self_instruct -When True the dataset will be augmented with self-instructions (more samples, more diverse). In this case, you also have control hover the same parameters of *generate_dataset()* method: *num_instructions*, *num_instructions_for_finetuning*. The default value is False. - -### chunk_size -The size of the chunk of text (in chars) that will be used to generate the instructions. We recommend values below 10000, but it depends on the model (engine) you are using. The default value is 8000. - -### num_samples_per_chunk -The number of samples that will be generated for each chunk. The default value is 5. -::: diff --git a/docs/docs/faqs.md b/docs/docs/faqs.md new file mode 100644 index 0000000..428e5a6 --- /dev/null +++ b/docs/docs/faqs.md @@ -0,0 +1,31 @@ +--- +title: 🤔 FAQs +description: Some common issues one might have +sidebar_position: 9 +--- + +**How to fine-tune a LLM?** + +You can refer [here](/overview/quickstart/finetune_guide) for a tutorial on how to fine-tune any model of your choice. The list of all the supported model can be found on [this page](/overview/supported_models). + +**How to load a model not there in the [supported models](/overview/supported_models)?** + +If you cannot find the model you want to use in the support models' list, then you can refer to [this guide](/advanced/anymodel) on how to load any other model of your choice with ease. + +**How to use an existing dataset for instruction fine-tuning?** + +A tutorial on this can be found [here](/overview/quickstart/data_usage), where you will see how to load Alpaca Dataset and prepare it in instruction fine-tuning format. + +**How to setup xTuring to start contributing?** + +To setup the environment ready to contribute to xTuring, you need to do an _editable install_ of the source on your machine. The steps for the same are mentioned [here](/contributing/setting_up#editable-install). + +**Which all fine-tuning memory-efficient techniques are supported by xTuring and how to use them with models not on the supported models' list?** + +Other than normal LLM fine-tuning, xTuring supports: +- Low-Rank Adaption (LoRA), +- 8-bit precision, +- LoRA with 8-bit precision and +- LoRA with 4bit precision + +techniques optimized for low-resource fine-tuning. To use any model not mentioned on the [supported models' list](/overview/suppported_models), you can refer [this page](/advanced/anymodel) to fine-tune a model of your choice with the preferred technique. \ No newline at end of file diff --git a/docs/docs/finetune/_category_.json b/docs/docs/finetune/_category_.json deleted file mode 100644 index bccb814..0000000 --- a/docs/docs/finetune/_category_.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "label": "🏋 Fine-tune", - "position": 4, - "link": { - "type": "generated-index" - }, - "collapsed": true -} diff --git a/docs/docs/finetune/configure.md b/docs/docs/finetune/configure.md deleted file mode 100644 index 06f88b6..0000000 --- a/docs/docs/finetune/configure.md +++ /dev/null @@ -1,65 +0,0 @@ ---- -title: Configure -description: Fine-tuning parameters -sidebar_position: 2 ---- - -# Fine-tuning configuration - -xTuring is easy to use. The library already loads the best parameters for each model by default. - -For advanced usage, you can customize the `finetune` method. - -### 1. Instantiate your model and dataset - -```python -from xturing.models.base import BaseModel -from xturing.datasets.instruction_dataset import InstructionDataset - -instruction_dataset = InstructionDataset("alpaca_data") -model = BaseModel.create("llama_lora") -``` - -### 2. Load the config object - -Print the `finetuning_config` object to check the default configuration. - -```python -finetuning_config = model.finetuning_config() - -print(finetuning_config) -``` - -### 3. Set the config - -```python -finetuning_config.batch_size = 64 -finetuning_config.num_train_epochs = 1 -finetuning_config.learning_rate = 1e-5 -finetuning_config.weight_decay = 0.01 -finetuning_config.optimizer_name = "adamw" -finetuning_config.output_dir = "training_dir/" -``` - -### 4. Start the finetuning - -```python -model.finetune(dataset=instruction_dataset) -``` - -## Reference - -- `learning_rate`: the initial learning rate for the optimizer. -- `gradient_accumulation_steps`: number of updates steps to accumulate the gradients for, before performing a backward/update pass. -- `batch_size`: the batch size per device (GPU/TPU core/CPU…) used for training. -- `weight_decay`: the weight decay to apply to all layers except all bias and LayerNorm weights in the optimizer. -- `warmup_steps`: number of steps used for a linear warmup from 0 to learning_rate. -- `eval_steps`: number of update steps between two evaluations -- `save_steps`: number of updates steps before two checkpoint saves -- `max_length`: the maximum length when tokenizing the inputs. -- `num_train_epochs`: total number of training epochs to perform -- `logging_steps`: number of update steps between two logs -- `max_grad_norm`: maximum gradient norm (for gradient clipping) -- `save_total_limit`: if a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. -- `optimizer_name`: optimizer that will be used -- `output_dir`: the output directory where the model predictions and checkpoints will be written. diff --git a/docs/docs/finetune/guide.md b/docs/docs/finetune/guide.md deleted file mode 100644 index 3f71ccd..0000000 --- a/docs/docs/finetune/guide.md +++ /dev/null @@ -1,170 +0,0 @@ ---- -title: Guide -description: Fine-tuning with xTuring -sidebar_position: 1 ---- -import Test from './test'; -# Fine-tuning guide - - -## 1. Prepare dataset - -For this tutorial you will need to prepare a dataset which contains 3 columns (instruction, text, target) for instruction fine-tuning or 2 columns (text, target) for text fine-tuning. Here, we show you how to convert Alpaca dataset to be used for instruction fine-tuning. - -1. Download Alpaca dataset from this [link](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) - -2. Convert it to instruction dataset format: - -```python -import json -from datasets import Dataset, DatasetDict - -alpaca_data = json.load(open(alpaca_dataset_path)) -instructions = [] -inputs = [] -outputs = [] - -for data in alpaca_data: - instructions.append(data["instruction"]) - inputs.append(data["input"]) - outputs.append(data["output"]) - -data_dict = { - "train": {"instruction": instructions, "text": inputs, "target": outputs} -} - -dataset = DatasetDict() -for k, v in data_dict.items(): - dataset[k] = Dataset.from_dict(v) - -dataset.save_to_disk(str("./alpaca_data")) -``` - - -:::info - -- *alpaca_dataset_path*: The path where the Alpaca dataset is stored. -::: - -## 2. Instruction fine-tuning - -After preparing the dataset in correct format, you can start the instruction fine-tuning. - -1. Load the instruction dataset and initialize the model - -```python -from xturing.datasets.instruction_dataset import InstructionDataset -from xturing.models import BaseModel - -instruction_dataset = InstructionDataset(dataset_path) -model = BaseModel.create(model_name) -``` - -:::info - -- *dataset_path*: The path where the converted dataset is stored. -- *model_name*: The model name you want to perform instruction fine-tuning. -::: - -xTuring supports following models: - -| Model name | Description | -| --------- | ---- | -| llama | LLaMA 7B model | -| llama_lora | LLaMA 7B model with LoRA technique to speed up fine-tuning | -| llama_lora_int8 | LLaMA 7B INT8 model with LoRA technique to speed up fine-tuning -| gptj | GPT-J 6B model | -| gptj_lora | GPT-J 6B model with LoRA technique to speed up fine-tuning | -| gptj_lora_int8 | GPT-J 6B INT8 model with LoRA technique to speed up fine-tuning -| gpt2 | GPT-2 model | -| gpt2_lora | GPT-2 model with LoRA technique to speed up fine-tuning | -| gpt2_lora_int8 | GPT-2 INT8 model with LoRA technique to speed up fine-tuning | -| distilgpt2 | DistilGPT-2 model | -| distilgpt2_lora | DistilGPT-2 model with LoRA technique to speed up fine-tuning | -| opt | OPT 1.3B model | -| opt_lora | OPT 1.3B model with LoRA technique to speed up fine-tuning | -| opt_lora_int8 | OPT 1.3B INT8 model with LoRA technique to speed up fine-tuning | -| cerebras | Cerebras-GPT 1.3B model | -| cerebras_lora | Cerebras-GPT 1.3B model with LoRA technique to speed up fine-tuning | -| cerebras_lora_int8 | Cerebras-GPT 1.3B INT8 model with LoRA technique to speed up fine-tuning | -| galactica | Galactica 6.7B model | -| galactica_lora | Galactica 6.7B model with LoRA technique to speed up fine-tuning | -| galactica_lora_int8 | Galactica 6.7B INT8 model with LoRA technique to speed up fine-tuning | -| bloom | Bloom 1.1B model | -| bloom_lora | Bloom 1.1B model with LoRA technique to speed up fine-tuning | -| bloom_lora_int8 | Bloom 1.1B INT8 model with LoRA technique to speed up fine-tuning | - - -2. Start the fine-tuning - -```python -model.finetune(dataset=instruction_dataset) -``` - -3. Generate an output text with the fine-tuned model - -```python -output = model.generate(texts=["Why LLM models are becoming so important?"]) -print("Generated output by the model: {}".format(output)) -``` - -## 3. Text fine-tuning -After preparing the dataset in correct format, you can start the text fine-tuning. - -1. Load the text dataset and initialize the model - -```python -from xturing.datasets.text_dataset import TextDataset -from xturing.models import BaseModel - -instruction_dataset = TextDataset(dataset_path) -model = BaseModel.create(model_name) -``` - -:::info - -- *dataset_path*: The path where the converted dataset is stored. -- *model_name*: The model name you want to perform instruction fine-tuning. -::: - -xTuring supports following models: - -| Model name | Description | -| --------- | ---- | -| llama | LLaMA 7B model | -| llama_lora | LLaMA 7B model with LoRA technique to speed up fine-tuning | -| llama_lora_int8 | LLaMA 7B INT8 model with LoRA technique to speed up fine-tuning -| gptj | GPT-J 6B model | -| gptj_lora | GPT-J 6B model with LoRA technique to speed up fine-tuning | -| gptj_lora_int8 | GPT-J 6B INT8 model with LoRA technique to speed up fine-tuning -| gpt2 | GPT-2 model | -| gpt2_lora | GPT-2 model with LoRA technique to speed up fine-tuning | -| gpt2_lora_int8 | GPT-2 INT8 model with LoRA technique to speed up fine-tuning | -| distilgpt2 | DistilGPT-2 model | -| distilgpt2_lora | DistilGPT-2 model with LoRA technique to speed up fine-tuning | -| opt | OPT 1.3B model | -| opt_lora | OPT 1.3B model with LoRA technique to speed up fine-tuning | -| opt_lora_int8 | OPT 1.3B INT8 model with LoRA technique to speed up fine-tuning | -| cerebras | Cerebras-GPT 1.3B model | -| cerebras_lora | Cerebras-GPT 1.3B model with LoRA technique to speed up fine-tuning | -| cerebras_lora_int8 | Cerebras-GPT 1.3B INT8 model with LoRA technique to speed up fine-tuning | -| galactica | Galactica 6.7B model | -| galactica_lora | Galactica 6.7B model with LoRA technique to speed up fine-tuning | -| galactica_lora_int8 | Galactica 6.7B INT8 model with LoRA technique to speed up fine-tuning | -| bloom | Bloom 1.1B model | -| bloom_lora | Bloom 1.1B model with LoRA technique to speed up fine-tuning | -| bloom_lora_int8 | Bloom 1.1B INT8 model with LoRA technique to speed up fine-tuning | - - -2. Start the fine-tuning - -```python -model.finetune(dataset=instruction_dataset) -``` - -3. Generate an output text with the fine-tuned model - -```python -output = model.generate(texts=["Why LLM models are becoming so important?"]) -print("Generated output by the model: {}".format(output)) -``` diff --git a/docs/docs/finetune/test.jsx b/docs/docs/finetune/test.jsx deleted file mode 100644 index 930bce7..0000000 --- a/docs/docs/finetune/test.jsx +++ /dev/null @@ -1,71 +0,0 @@ -import React, { useState } from 'react' -import clsx from 'clsx' -import styles from './styles.module.css' -import MDXContent from '@theme/MDXContent' -import CodeBlock from '@theme/CodeBlock' - -export default function Test() { - const [model, setModel] = useState('LLaMa') - const [version, setVersion] = useState('LLaMa') - - console.log(model, version) - - return ( -
- - - - - - - - - -
- ) -} \ No newline at end of file diff --git a/docs/docs/inference/_category_.json b/docs/docs/inference/_category_.json deleted file mode 100644 index 4ced0b3..0000000 --- a/docs/docs/inference/_category_.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "label": "💬 Inference", - "position": 5, - "link": { - "type": "generated-index" - }, - "collapsed": true -} diff --git a/docs/docs/inference/configure.md b/docs/docs/inference/configure.md deleted file mode 100644 index 5b659e5..0000000 --- a/docs/docs/inference/configure.md +++ /dev/null @@ -1,49 +0,0 @@ ---- -title: Configure -description: Inference parameters -sidebar_position: 2 ---- - -# Inference configuration - -xTuring is easy to use. The library already loads the best parameters for each model by default. - -For advanced usage, you can customize the `generate` method. - -### 1. Instantiate your model - -```python -from xturing.models.base import BaseModel - -model = BaseModel.create("llama_lora") -``` - -### 2. Load the config object - -Print the `generation_config` object to check the default configuration. - -```python -generation_config = model.generation_config() -print(generation_config) -``` - -### 3. Set the config - -```python -generation_config.max_new_tokens = 256 -``` - -### 4. Start generating text - -```python -output = model.generate(texts=["Why are the LLM models important?"]) -print(output) -``` - -## Reference - -- `max_new_tokens`: the maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. -- `penalty_alpha`: for contrastive search decoding. The values balance the model confidence and the degeneration penalty. -- `top_k`: for contrastive search and sampling decoding method. The number of highest probability vocabulary tokens to keep for top-k-filtering. -- `do_sample`: whether or not to use sampling. -- `top_p`: for sampling decoding method. If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. diff --git a/docs/docs/inference/guide.md b/docs/docs/inference/guide.md deleted file mode 100644 index bd871c3..0000000 --- a/docs/docs/inference/guide.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Guide -description: Inferencing guide -sidebar_position: 1 ---- - -# Inferencing guide - -Once you have fine-tuned your model, you can run the inferences as simple as follows. - -### 1. (Optional) Load your model - -Load your model from a checkpoint after fine-tuning it. - -```python -from xturing.models.base import BaseModel - -model = BaseModel.load("/dir/path") -``` - -Load your model with the default weights - -```python -from xturing.models.base import BaseModel - -model = BaseModel.create("llama_lora") -``` - -### 2. Run the generate method - -```python -output = model.generate(texts=["Why are the LLMs so important?"]) -print("Generated output: {}".format(output)) -``` diff --git a/docs/docs/intro.md b/docs/docs/intro.md deleted file mode 100644 index 2acba92..0000000 --- a/docs/docs/intro.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -sidebar_position: 1 -title: ✨ Introduction -description: xTuring is an open-source AI personalization library. -slug: / ---- - -# xTuring - -**xTuring** is an open-source AI personalization software. xTuring makes it easy to build and control -LLMs by providing simple interface to personalizing LLMs to your own data and application. - -xTuring gives you the tools to: -- fine-tune LLMs using different approaches -- generate datasets from your data sources -- evaluate modified models - -xTuring prioritizes: -- simplicity and productivity -- efficiency of compute and memory -- agility and customizability - -### Installation -```bash -pip install xturing -``` - -You can quickly get started with xTuring by following the [Quickstart](/quickstart) guide or use one of the examples below. - -### UI Playground - -![Playground UI Demo](/img/playground/ui-playground.gif) - -### CLI Playground - -![Playground CLI Demo](/img/playground/cli-playground.gif) - -### Models supported - -| Model | Examples | -| --- | --- | -| Bloom | [Bloom fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/bloom) | -| Cerebras-GPT | [Cerebras-GPT fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/cerebras) | -| Falcon | [Falcon 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/falcon) | -| Galactica | [Galactica fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/galactica) | -| Generic Wrapper | [Any large language model fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/generic) | -| GPT-J | [GPT-J 6B LoRA fine-tuning with/without INT8 ](https://github.com/stochasticai/xturing/tree/main/examples/gptj) | -| GPT-2 | [GPT-2 fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/gpt2) | -| LLaMA | [LLaMA 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/llama) | -| LLaMA 2 | [LLaMA 2 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/llama2) | -| OPT | [OPT fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/opt) | - -xTuring is licensed under [Apache 2.0](https://github.com/stochasticai/xturing/blob/main/LICENSE) - -### Support -[💬 Join Community Discord](https://discord.gg/YxHuQq8b)
-[@stochasticai](https://twitter.com/stochasticai) diff --git a/docs/docs/load_save_models.md b/docs/docs/load_save_models.md deleted file mode 100644 index 86686a7..0000000 --- a/docs/docs/load_save_models.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: 💾 Load and save models -description: Load and save models -sidebar_position: 6 ---- - -# Load and save models - -### 1. Load a pre-trained model - -To load a pre-trained model for the first time, run the following line of code. This will load the model with the default weights. - -```python -from xturing.models.base import BaseModel - -model = BaseModel.create("distilgpt2_lora") -``` - -### 2. Save a fine-tuned model - -After fine-tuning your model, you can save it as simple as: - -```python -model.save("/path/to/a/directory") -``` - -Remember that the path that you specify should be a directory. If the directory doesn't exist, it will be created. - -The model weights will be saved into 2 files. The whole model weights including based model parameters and LoRA parameters are stored in `pytorch_model.bin` file and only LoRA parameters are stored in `adapter_model.bin` file. - -### 3. Load a fine-tuned model - -To load a saved model, you only have to run the `load` method specifying the directory where the weights were saved. - -```python -finetuned_model = BaseModel.load("/path/to/a/directory") -``` diff --git a/docs/docs/overview/_category_.json b/docs/docs/overview/_category_.json new file mode 100644 index 0000000..432f091 --- /dev/null +++ b/docs/docs/overview/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "🌄 Overview", + "position": 1, + "collapsed": true, + "link": { + "type": "doc", + "id": "overview" + } +} diff --git a/docs/docs/overview/installation.md b/docs/docs/overview/installation.md new file mode 100644 index 0000000..2919bb5 --- /dev/null +++ b/docs/docs/overview/installation.md @@ -0,0 +1,147 @@ +--- +sidebar_position: 2 +title: ⬇️ Installation +description: Your first time installing xTuring +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +We can install `xTuring` globally on our machine, but it is advised to install it inside a virtual environment. Before starting, we have to make sure we have __Python 3.0+__ installed on our machine. + +## Install via pip +For this, we have to ensure that we have _virtualenv_ package installed or _anaconda_ setup on our machine. + +Start by creating a virtual environment in our working directory: + + + + +```bash +$ virtualenv venv +``` +Activate the virtual environment. + + + + +```bash +$ source venv/bin/activate +``` +Once the virtual environment is activated, we can now install `xTuring` library by running the following command on your terminal: + +```bash +$ pip install xTuring +``` +This will install the latest version of xTuring available on pip. +Finally, we can test if `xTuring` has been properly installed by running the following commands on our terminal: +```bash +$ python +>>> from xturing.models import BaseModel +>>> model = BaseModel.create('opt') +>>> outputs = model.generate(texts=['Hi How are you?']) +``` +Then print the outputs variable to see what the LLM generated based on the input prompt. + + + + + +```bash +> venv\Scripts\Activate +``` + +Once the virtual environment is activated, we can now install `xTuring` library by running the following command on your terminal: + +```bash +> pip install xTuring +``` +This will install the latest version of xTuring available on pip. +Finally, we can test if `xTuring` has been properly installed by running the following commands on our terminal: +```bash +> python +>>> from xturing.models import BaseModel +>>> model = BaseModel.create('opt') +>>> outputs = model.generate(texts=['Hi How are you?']) +``` +Then print the outputs variable to see what the LLM generated based on the input prompt. + + + + + + + + + +```bash +$ conda create -n venv +``` +Activate the conda environment. + + + +```bash +$ conda activate venv +``` + + +Once the conda environment is activated, we can now install `xTuring` library by running the following command on your terminal: + +```bash +$ pip install xTuring +``` +This will install the latest version of xTuring available on pip. +Finally, we can test if `xTuring` has been properly installed by running the following commands on our terminal: +```bash +$ python +>>> from xturing.models import BaseModel +>>> model = BaseModel.create('opt') +>>> outputs = model.generate(texts=['Hi How are you?']) +``` +Then print the outputs variable to see what the LLM generated based on the input prompt. + + + + + +```bash +> conda activate venv +``` + + +Once the conda environment is activated, we can now install `xTuring` library by running the following command on your terminal: + +```bash +> pip install xTuring +``` +This will install the latest version of xTuring available on pip. +Finally, we can test if `xTuring` has been properly installed by running the following commands on our terminal: +```bash +> python +>>> from xturing.models import BaseModel +>>> model = BaseModel.create('opt') +>>> outputs = model.generate(texts=['Hi How are you?']) +``` +Then print the outputs variable to see what the LLM generated based on the input prompt. + + + + + + + + + + diff --git a/docs/docs/overview/intro.md b/docs/docs/overview/intro.md new file mode 100644 index 0000000..bfac097 --- /dev/null +++ b/docs/docs/overview/intro.md @@ -0,0 +1,88 @@ +--- +sidebar_position: 1 +title: ✨ Introduction +description: xTuring is an open-source AI personalization library. +slug: / +--- + +# xTuring + + + + +**Welcome to xTuring: Personalize AI your way** + +In the world of AI, personalization is incredibly important for making AI truly powerful. This is where xTuring comes in – it's a special open-source software that helps you make AI models, called Large Language Models (LLMs), work exactly the way you want them to. + +What's great about xTuring is that it's super easy to use. It has a simple interface that's designed to help you customize LLMs for your specific needs, whether it's for your own data or applications. Basically, xTuring gives you complete control over personalizing AI, making it work just the way you need it to. + +## The xTuring advantage + +At xTuring, we have three important principles that guide everything we do: + +1. **Simplicity and Productivity**: We want to make AI tasks easy to understand and do. Whether you're new to AI or an experienced developer, xTuring is designed to be simple and user-friendly. It helps you get things done efficiently. + +2. **Efficiency of Compute and Memory**: We know that time and resources are precious. xTuring is built to make the most of your computer's power and memory. This means your AI projects will run smoothly and won't use up too much of your computer's resources. + +3. **Agility and Customizability**: AI is always changing and evolving. That's why xTuring allows you to change and customize AI models to fit your needs. This helps you stay flexible and creative in the world of AI. + +When you use xTuring, think of it as your personal AI workshop. You can make AI models better or create special AI tools for different jobs. xTuring is here to work with you. + +So, if you're ready to add a personal touch to AI, welcome to xTuring. It's a journey filled with innovation, simplicity, and endless possibilities. Let's unlock the true power of AI, just the way you want it. + +To get started with xTuring, check out the [Quickstart](/overview/quickstart) guide or try some of the examples below. + +## Model examples + +| Model | Examples | +| --- | --- | +| Bloom | [Bloom fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/bloom) | +| Cerebras-GPT | [Cerebras-GPT fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/cerebras) | +| Falcon | [Falcon 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/falcon) | +| Galactica | [Galactica fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/galactica) | +| Generic Wrapper | [Any large language model fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/generic) | +| GPT-J | [GPT-J 6B LoRA fine-tuning with/without INT8 ](https://github.com/stochasticai/xturing/tree/main/examples/gptj) | +| GPT-2 | [GPT-2 fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/gpt2) | +| LLaMA | [LLaMA 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/llama) | +| LLaMA 2 | [LLaMA 2 7B fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/llama2) | +| OPT | [OPT fine-tuning on Alpaca dataset with/without LoRA and with/without INT8](https://github.com/stochasticai/xturing/tree/main/examples/opt) | + +xTuring is licensed under [Apache 2.0](https://github.com/stochasticai/xturing/blob/main/LICENSE) + +## Support +💬 Join our [Discord community](https://discord.gg/YxHuQq8b) and chat with other community members about ideas. +🕊️ Follow us on Twitter +[@stochasticai](https://twitter.com/stochasticai). + +## About the team + +**Meet the Team Behind xTuring: Making AI Simple and Accessible** + +The people who created xTuring come from a place called Stochastic, where lots of smart and creative minds work together. We all share a big idea: we want to change the way AI works so that everyone can use it. + +**Experts Working Together**: Our team is made up of different kinds of experts, like researchers and engineers. Each person has special skills in things like machines that learn, computers, and using AI in real life. These skills are what make xTuring so amazing. + +**Always Thinking of New Ideas**: We're always thinking about how to make AI easier to use and more helpful. Our team is spread out all over the world, but we work together really well. This means xTuring has lots of cool ideas from people everywhere. + +**Keeping Things Simple**: Even though AI can be very complicated, we believe it should be easy for everyone to use. That's why xTuring is simple to understand and can do powerful things. We want everyone to be able to use AI and make it work for them. + +**Here to Help You Succeed**: Our job doesn't stop with making xTuring. We're here to help you learn and use AI in the best way possible. We want you to feel confident using our tool in the fast-changing world of AI. + +[Come Work with Us](/contributing) and be part of the future of AI with xTuring. We're all about new ideas and making AI better for everyone. We're here to help you every step of the way. \ No newline at end of file diff --git a/docs/docs/overview/overview.md b/docs/docs/overview/overview.md new file mode 100644 index 0000000..362e470 --- /dev/null +++ b/docs/docs/overview/overview.md @@ -0,0 +1,5 @@ +import DocCardList from '@theme/DocCardList'; + +# 🌄 Overview + + \ No newline at end of file diff --git a/docs/docs/overview/quickstart/_category_.json b/docs/docs/overview/quickstart/_category_.json new file mode 100644 index 0000000..4d21eb9 --- /dev/null +++ b/docs/docs/overview/quickstart/_category_.json @@ -0,0 +1,9 @@ +{ + "label": "🚀 QuickStart", + "position": 3, + "collapsed": false, + "link": { + "type": "doc", + "id": "quickstart" + } +} diff --git a/docs/docs/overview/quickstart/data_usage.md b/docs/docs/overview/quickstart/data_usage.md new file mode 100644 index 0000000..294596a --- /dev/null +++ b/docs/docs/overview/quickstart/data_usage.md @@ -0,0 +1,71 @@ +--- +title: 📜 Use datasets +description: Using a an existing dataset +sidebar_position: 3 +--- + + + + +Certainly, when we're looking to utilize an existing dataset for tasks like fine-tuning or model testing, it becomes crucial to ensure that the dataset is in a format compatible with `xTuring`. This ensures smooth functionality when working with the `xTuring` platform. However, there might be instances where the chosen dataset isn't in the format that `xTuring` expects. In such cases, there's a method we can follow to rectify this issue. Let's explore the process: + +1. **Selecting the Dataset**: The first step involves choosing a dataset that suits our requirements for fine-tuning or testing the model. + +2. **Format Alignment**: It's essential to verify whether the chosen dataset is in the format that `xTuring` accepts. If it's not, we need to proceed with some adjustments. + +3. **Format Adjustment**: To align the dataset with `xTuring`'s expectations, we should reformat it according to the accepted structure. This ensures that the platform can seamlessly work with the data. + +4. **Ensuring Coherency**: The reformatted text should maintain coherency and clarity. It should effectively convey the message while adhering to proper grammar and organization. + +By following these steps, we ensure that the chosen dataset is transformed into a compatible format for `xTuring`, enabling efficient usage and optimal results. + +We know __what__ all we need to do to make format the dataset, below is the __how__ behind it! + +## Instruction dataset format +For this tutorial we will need to prepare a dataset which contains 3 columns (instruction, text, target) for instruction fine-tuning or 2 columns (text, target) for text fine-tuning. Here, we will see how to convert Alpaca dataset to be used for instruction fine-tuning. Before starting, make sure you have downloaded the [Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) in your working directory. + + +### Convert the dataset to _Instruction Dataset_ format +This is the main step where we our knowledge of the existing dataset, we convert it to a format understandable by `xTuring`'s _InstructionDataset_ class. + +```python +import json +from datasets import Dataset, DatasetDict + +alpaca_data = json.load(open('/path/to/alpaca_dataset')) +instructions = [] +inputs = [] +outputs = [] + +for data in alpaca_data: + instructions.append(data["instruction"]) + inputs.append(data["input"]) + outputs.append(data["output"]) + +data_dict = { + "train": {"instruction": instructions, "text": inputs, "target": outputs} +} + +dataset = DatasetDict() +for k, v in data_dict.items(): + dataset[k] = Dataset.from_dict(v) + +dataset.save_to_disk(str("./alpaca_data")) +``` + + +### Load the prepared dataset + +After preparing the dataset in correct format, you can use this dataset for the instruction fine-tuning. + +To load the instruction dataset + +```python +from xturing.datasets.instruction_dataset import InstructionDataset + +instruction_dataset = InstructionDataset('/path/to/instruction_converted_alpaca_dataset') +``` + +## Text dataset format + +The datasets that we find on the internet are formatted in a way which is accepted by the `xTuring`'s `TextDataset` class, so we need not worry text fine-tuning and just use those datasets as is. \ No newline at end of file diff --git a/docs/docs/overview/quickstart/finetune_guide.md b/docs/docs/overview/quickstart/finetune_guide.md new file mode 100644 index 0000000..f5a4b62 --- /dev/null +++ b/docs/docs/overview/quickstart/finetune_guide.md @@ -0,0 +1,90 @@ +--- +title: 🔧 Fine-tune pre-trained models +description: Fine-tuning with xTuring +sidebar_position: 4 +--- + +import Test from './test'; + + + +## Text fine-tuning +After preparing the dataset in correct format, you can start the text fine-tuning. + +First, load the text dataset and initialize the model of your choice + + + + +Next, we need to start the fine-tuning + +```python +model.finetune(dataset=dataset) +``` + + + + +## Instruction fine-tuning + +First, make sure that you have prepared your fine-tuning dataset for instruction fine-tuning. To know how, refer [here](/overview/quickstart/prepare#instructiondataset). + +After preparing the dataset in correct format, you can start the instruction fine-tuning. + +Start by loading the instruction dataset and initializing the model of your choice. + + + +A list of all the supported models can be found [here](/overview/supported_models). + + + +Next, we need to start the fine-tuning + +```python +model.finetune(dataset=instruction_dataset) +``` + + + + \ No newline at end of file diff --git a/docs/docs/overview/quickstart/inference.md b/docs/docs/overview/quickstart/inference.md new file mode 100644 index 0000000..68fea68 --- /dev/null +++ b/docs/docs/overview/quickstart/inference.md @@ -0,0 +1,55 @@ +--- +title: 🗣️ Inference +description: Inferencing guide +sidebar_position: 5 +--- + + + + + +Once you have fine-tuned your model, you can run the inferences as simple as follows. + +### Using a local model + +Start with loading your model from a checkpoint after fine-tuning it. + +```python +# Make the ncessary imports +from xturing.models.base import BaseModel + +# Load the desired model +model = BaseModel.load("/path/to/fine-tuned/model") +``` + +Next, we can run do the inference on our model using the `.generate()` method. + +```python +# Make inference +output = model.generate(texts=["Why are the LLMs so important?"]) + +# Print the generated outputs +print("Generated output: {}".format(output)) +``` + +### Using a pretrained model + +Start with loading your model with the default weights + +```python +# Make the ncessary imports +from xturing.models.base import BaseModel + +# Load the desired model +model = BaseModel.create("llama_lora") +``` + +Next, we can run do the inference on our model using the `.generate()` method. + +```python +# Make inference +output = model.generate(texts=["Why are the LLMs so important?"]) + +# Print the generated outputs +print("Generated output: {}".format(output)) +``` diff --git a/docs/docs/overview/quickstart/load_save_models.md b/docs/docs/overview/quickstart/load_save_models.md new file mode 100644 index 0000000..fe66118 --- /dev/null +++ b/docs/docs/overview/quickstart/load_save_models.md @@ -0,0 +1,120 @@ +--- +title: 💾 Load and save models +description: Load and save models +sidebar_position: 1 +--- + + + + +### Load a pre-trained model + +To load a pre-trained model for the first time, run the following line of code. This will load the model with the default weights. + +```python +from xturing.models.base import BaseModel + +model = BaseModel.create("") + +''' +For example, +model = BaseModel.create("llama_lora") +''' +``` +You can find all the supported model keys [here](/overview/supported_models). + +### Save a fine-tuned model + +After fine-tuning your model, you can save it as simple as: + +```python +model.save("/path/to/a/directory") +``` + +Remember that the path that you specify should be a directory. If the directory doesn't exist, it will be created. + +The model weights will be saved into 2 files. The whole model weights including based model parameters and LoRA parameters are stored in `pytorch_model.bin` file and only LoRA parameters are stored in `adapter_model.bin` file. + +### Load a model from local directory + +To load a saved model, you only have to run the `load` method specifying the directory where the weights were saved. + +```python +model = BaseModel.load("/path/to/a/directory") +``` + +
+Sample code to load and save a model + +```python +from xturing.models.base import BaseModel + +## Load the model +model = BaseModel.create("llama_lora") + +# Save the model +model.save("/path/to/a/directory") + +## Load the fine-tuned model +finetuned_model = BaseModel.load("/path/to/a/directory") +``` +
+ + diff --git a/docs/docs/datasets/usage.md b/docs/docs/overview/quickstart/prepare.md similarity index 88% rename from docs/docs/datasets/usage.md rename to docs/docs/overview/quickstart/prepare.md index 5f34501..91c6356 100644 --- a/docs/docs/datasets/usage.md +++ b/docs/docs/overview/quickstart/prepare.md @@ -1,101 +1,105 @@ ---- -title: Using datasets -description: Learn how to use our datasets -sidebar_position: 1 ---- - -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# Using datasets - -We provide several type of datasets to use with your data. Depending on how you want to train and use your model, you can choose: -- [**InstructionDataset**](#instructiondataset) - You want the model to generate text based on an instruction/task. -- [**TextDataset**](#textdataset) - You want the model to complete your text. - -## InstructionDataset - -Here is how you can create this type of dataset: - - - - -From a python dictionary with the following keys: - -- **instruction** : List of strings representing the instructions/tasks. -- **text** : List of strings representing the input text. -- **target** : List of strings representing the target text. - - -```python -from xturing.datasets.instruction_dataset import InstructionDataset - -dataset = InstructionDataset({ - "text": ["first text", "second text"], - "target": ["first text", "second text"], - "instruction": ["first instruction", "second instruction"] -}) -``` - - - - -From a saved location: - - -```python -from xturing.datasets.instruction_dataset import InstructionDataset - -dataset = InstructionDataset('path/to/saved/location') -``` - - - - -## TextDataset - -Here is how you can create this type of dataset: - - - - -From a python dictionary with the following keys: - -- **text** : List of strings representing the input text. -- **target** : List of strings representing the target text. - - -```python -from xturing.datasets.text_dataset import TextDataset - -dataset = TextDataset({ - "text": ["first text", "second text"], - "target": ["first text", "second text"] -}) -``` - - - - -From a saved location: - - -```python -from xturing.datasets.text_dataset import TextDataset - -dataset = TextDataset('path/to/saved/location') -``` - - - - -## Save a dataset - -You can save a dataset to a folder using the `save` method: - -```python -from xturing.datasets... -dataset = ... - -dataset.save('path/to/saved/location') +--- +title: 💽 Prepare and save dataset +description: Use self-instruction to generate a dataset +sidebar_position: 2 +--- + + + + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + +We provide several type of datasets to use with your data. Depending on how you want to train and use your model, you can choose: +- [**InstructionDataset**](#instructiondataset) - You want the model to generate text based on an instruction/task. +- [**TextDataset**](#textdataset) - You want the model to complete your text. + +## InstructionDataset + +Here is how you can create this type of dataset: + + + + +From a python dictionary with the following keys: + +- **instruction** : List of strings representing the instructions/tasks. +- **text** : List of strings representing the input text. +- **target** : List of strings representing the target text. + + +```python +from xturing.datasets.instruction_dataset import InstructionDataset + +dataset = InstructionDataset({ + "text": ["first text", "second text"], + "target": ["first text", "second text"], + "instruction": ["first instruction", "second instruction"] +}) ``` + + + + +From a saved location: + + +```python +from xturing.datasets.instruction_dataset import InstructionDataset + +dataset = InstructionDataset('path/to/saved/location') +``` + + + + +## TextDataset + +Here is how you can create this type of dataset: + + + + +From a python dictionary with the following keys: + +- **text** : List of strings representing the input text. +- **target** : List of strings representing the target text. + + +```python +from xturing.datasets.text_dataset import TextDataset + +dataset = TextDataset({ + "text": ["first text", "second text"], + "target": ["first text", "second text"] +}) +``` + + + + +From a saved location: + + +```python +from xturing.datasets.text_dataset import TextDataset + +dataset = TextDataset('path/to/saved/location') +``` + + + + +## Save a dataset + +You can save a dataset to a folder using the `save` method: + +```python +from xturing.datasets import ... +dataset = ... + +dataset.save('path/to/a/directory') +``` + diff --git a/docs/docs/overview/quickstart/quickstart.md b/docs/docs/overview/quickstart/quickstart.md new file mode 100644 index 0000000..33676b4 --- /dev/null +++ b/docs/docs/overview/quickstart/quickstart.md @@ -0,0 +1,42 @@ +--- +sidebar_position: 2 +title: 🚀 Quickstart +description: Your first fine-tuning job with xTuring +--- + + + +**xTuring** provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, GPT-2, and more. It supports both single GPU and multi-GPU training. Leverage memory-efficient fine-tuning techniques like LoRA to reduce your hardware costs by up to 90% and train your models in a fraction of the time. + +Whether you are someone who develops AI pipelines for a living or someone who just wants to leverage the power of AI, this quickstart will help you get started with `xTuring` and how to use `BaseModel` for inference, fine-tuning and saving the obtained weights. + + +xTuring provides a solution to easily load and fine-tune some pre-trained models in less than 10 lines of code. Fine-tuning a pre-trained large language models (LLMs) comes with a lot of complexity and requires Machine Learning along with domain knowledge. Hence, it quite a non-intuitive task for beginners and business owners who lack practical knowledge in the field. This particular issue has been addressed by xTuring by providing a simple interface understandable with little to no knowledge of Python. It is as short and crisp as: + +```python +from xturing.datasets import InstructionDataset +from xturing.models import BaseModel + + +# Load a model of your choice +model = BaseModel.create('llama_lora') + +# Prepare the training data +dataset = InstructionDataset('...') + +# Fine-tune the model +model.finetune(dataset=dataset) + +# Test the fine-tuned model +output = model.generate(texts=["Why LLM models are becoming so important?"]) + +# Save the fine-tuned model +model.save("/path/to/a/directory") +``` + +Please checkout the following steps to fully understand the above code: + +- [Load and Save Models](/overview/quickstart/load_save_models) +- [Load and Prepare Data](/overview/quickstart/prepare) +- [Fine-Tune Models](/overview/quickstart/finetune_guide) +- [Inference](/overview/quickstart/inference) diff --git a/docs/docs/overview/quickstart/test.jsx b/docs/docs/overview/quickstart/test.jsx new file mode 100644 index 0000000..c738db8 --- /dev/null +++ b/docs/docs/overview/quickstart/test.jsx @@ -0,0 +1,99 @@ +import React, { useEffect, useState } from 'react' +import clsx from 'clsx' +import MDXContent from '@theme/MDXContent' +import CodeBlock from '@theme/CodeBlock' + +const trainingTechniques = { + base: 'Base', + lora: 'LoRA', + lora_int8: 'LoRA INT8', + int8: 'INT8', +} + +const modelList = { + bloom: 'BLOOM', + cerebras: 'Cerebras', + distilgpt2: 'DistilGPT-2', + galactica: 'Galactica', + gptj: 'GPT-J', + gpt2: 'GPT-2', + llama: 'LLaMA', + llama2: 'LLaMA 2', + opt: 'OPT', +} + +export default function Test( + {instruction} +) { + // const [code, setCode] = useState('llama'); + const [code, setCode] = useState({ + model: '', + technique: 'base', + }) + + let finalKey = '' + if (code.technique === 'base') { + finalKey = `${code.model}` + } else { + finalKey = `${code.model}_${code.technique}` + } + + useEffect(() => { + setCode({ + model: 'llama', + technique: 'base' + }); + }, []); + + return ( +
+ + + + + + + +
+ ) +} \ No newline at end of file diff --git a/docs/docs/overview/supported_models.md b/docs/docs/overview/supported_models.md new file mode 100644 index 0000000..02932dd --- /dev/null +++ b/docs/docs/overview/supported_models.md @@ -0,0 +1,36 @@ +--- +sidebar_position: 4 +title: 🦾 Supported models +description: Models Supported by xTuring +--- + + +## Base versions +| Model | Model Key | LoRA | INT8 | LoRA + INT8 | LoRA + INT4 | +| ------ | --- | :---: | :---: | :---: | :---: | +| BLOOM 1.1B| bloom | ✅ | ✅ | ✅ | ✅ | +| Cerebras 1.3B| cerebras | ✅ | ✅ | ✅ | ✅ | +| DistilGPT-2 | distilgpt2 | ✅ | ✅ | ✅ | ✅ | +| Falcon 7B | falcon | ✅ | ✅ | ✅ | ✅ | +| Galactica 6.7B| galactica | ✅ | ✅ | ✅ | ✅ | +| GPT-J 6B | gptj | ✅ | ✅ | ✅ | ✅ | +| GPT-2 | gpt2 | ✅ | ✅ | ✅ | ✅ | +| LLaMA 7B | llama | ✅ | ✅ | ✅ | ✅ | +| LLaMA2 | llama2 | ✅ | ✅ | ✅ | ✅ | +| OPT 1.3B | opt | ✅ | ✅ | ✅ | ✅ | + +### Memory-efficient versions +> The above mentioned are the base variants of the LLMs. Below are the templates to get their `LoRA`, `INT8`, `INT8 + LoRA` and `INT4 + LoRA` versions. + +| Version | Template | +| -- | -- | +| LoRA | _lora| +| INT8 | _int8| +| INT8 + LoRA | _lora_int8| + +### INT4 Precision model versions +> In order to load any model's __`INT4+LoRA`__ version, you will need to make use of `GenericLoraKbitModel` class from `xturing.models`. Below is how to use it: +```python +model = GenericLoraKbitModel('/path/to/model') +``` +The `/path/to/model` can be replaced with you local directory or any HuggingFace library model like `facebook/opt-1.3b`. diff --git a/docs/docs/playground/CLI.md b/docs/docs/playground/CLI.md index 45f98b9..0de8386 100644 --- a/docs/docs/playground/CLI.md +++ b/docs/docs/playground/CLI.md @@ -1,27 +1,40 @@ --- -title: CLI +title: 🧑🏻‍💻 CLI description: CLI Playground sidebar_position: 2 --- -# CLI Playground +# 🧑🏻‍💻 CLI playground: Engage with the power of xTuring +To harness the capabilities of xTuring's CLI playground, follow these steps to install and utilize the tool effectively. -Be sure to have the latest version of xturing installed: +### Prerequisites: installing the latest xTuring version +Ensure that you have the most recent version of xTuring installed by executing the following command: + + ```sh -pip install xturing --upgrade +$ pip install xturing --upgrade ``` -### 1. Launch the playground + +### 1. Launching the playground +Get ready to dive into an immersive chat experience powered by xTuring. Simply initiate the playground by executing the subsequent command in your command-line interface: -From the CLI run the following command to start the chat: + ```sh -xturing chat -m "/path/to/the/model" +$ xturing chat -m "/path/to/the/model" ``` -### 2. Chat + + +### 2. Engage in a dynamic chat +Once the model is successfully loaded, you'll be prompted to provide your input. Unleash your creativity and curiosity as you interact with the model through the CLI playground. + + + +![Playground CLI Demo](/img/playground/cli-playground.gif) -If the model loads successfully you will be then asked to enter your prompt. +In this engaging chat interface, your interactions with the model come to life, opening the door to intriguing and insightful conversations. -![Playground CLI Demo](/img/playground/cli-playground.gif) +By following these steps, you can seamlessly access and utilize the xTuring CLI playground, experiencing firsthand the model's impressive text generation capabilities. Let your imagination run wild and explore the realms of AI-powered communication! \ No newline at end of file diff --git a/docs/docs/playground/UI.md b/docs/docs/playground/UI.md index 55387a5..7ca55bc 100644 --- a/docs/docs/playground/UI.md +++ b/docs/docs/playground/UI.md @@ -1,23 +1,39 @@ --- -title: UI +title: 👁️‍🗨️ UI description: UI Playground sidebar_position: 1 --- -# UI Playground +# 👁️‍🗨️ Exploring the UI playground: a first-person experience -Be sure to have the latest version of xturing installed: +To embark on an interactive journey with the xTuring UI Playground, here's your guide to installation, setup, and seamless engagement with this fascinating tool. + +### Prerequisites: Ensuring the latest xTuring version + + +Begin by guaranteeing that you're equipped with the most up-to-date xTuring version. Execute the subsequent command to ensure the latest update: ```sh -pip install xturing --upgrade +$ pip install xturing --upgrade ``` ![Playground UI Demo](/img/playground/ui-playground.gif) -### 1. Launch the playground + +### 1. Launching the playground Interface + +To immerse yourself in the world of the UI Playground, you have two equally effective methods: + +#### Option 1: Command-Line Interface (CLI) + +Execute the command xturing ui in your terminal to launch the UI Playground. + +#### Option 2: Integration in a script + +Alternatively, in a Python script, you can utilize the following code snippet to launch the Playground interface: -To launch the playground interface, you can either run `xturing ui` on the CLI or in a script as follows: + ```python from xturing.ui.playground import Playground @@ -26,30 +42,48 @@ Playground().launch() ``` :::info -`Playground` constructor accepts the following argument: + +For enhanced customization, the Playground constructor accepts the argument model_path, allowing you to specify the model path when launching the Playground: ``` Playground(model_path="...").launch() ``` ::: -### 2. Load the model +### 2. Loading the model -You can load the model by specifying the model path in the step 1 or by providing the path in the input field. +Load your desired model effortlessly using one of two methods: + + + +#### Method 1: Path specification (step 1) + +During the launch process, provide the model path in Step 1 to initiate model loading. + +#### Method 2: Input field (load model section) + +Alternatively, you can input the model path directly in the provided field within the UI Playground interface. ![Load model section](/img/playground/load-model.png) -When you press the load button, model loading will start. Once the model is loaded successfully the chat section is enabled. + +Upon clicking the "Load" button, the model loading process commences. Once the model is successfully loaded, the chat section becomes active. :::info -Model path should be a directory containing a valid `xturing.json` config file. + +Ensure that the model path points to a directory containing a valid `xturing.json` configuration file. ::: ### 3. Chat with your model -Enter the prompt and start chatting with the model. Use the `Clear chat` to start a new chat. + +With the loaded model at your fingertips, enter prompts and initiate engaging conversations with the AI. To start anew, use the "Clear Chat" button for a fresh chat session. + +### 4. Tweaking model behavior -### 4. Change the parameters + -We provide some configuration parameters to change the behavior of the model: `Top-p sampling`, `Contrastive search`. You can change the parameters by using the input fields in the `Parameters` section. +The UI Playground offers configuration parameters that enable you to tailor the model's behavior to your preference. In the "Parameters" section, you can adjust settings such as Top-p Sampling and Contrastive Search. ![Parameters section](/img/playground/parameters.png) + +Through these intuitive steps, you can readily experience the xTuring UI Playground as if you were navigating it yourself. Uncover the wonders of AI-driven communication and creativity in an interactive, user-friendly environment. \ No newline at end of file diff --git a/docs/docs/playground/_category_.json b/docs/docs/playground/_category_.json index 4f67559..361e5f0 100644 --- a/docs/docs/playground/_category_.json +++ b/docs/docs/playground/_category_.json @@ -2,7 +2,8 @@ "label": "🎮 Playground", "position": 7, "link": { - "type": "generated-index" + "type": "doc", + "id": "playground" }, "collapsed": true -} +} diff --git a/docs/docs/playground/playground.md b/docs/docs/playground/playground.md new file mode 100644 index 0000000..74f5ac3 --- /dev/null +++ b/docs/docs/playground/playground.md @@ -0,0 +1,5 @@ +import DocCardList from '@theme/DocCardList'; + +# Playground + + \ No newline at end of file diff --git a/docs/docs/quickstart.md b/docs/docs/quickstart.md deleted file mode 100644 index 2a00ada..0000000 --- a/docs/docs/quickstart.md +++ /dev/null @@ -1,63 +0,0 @@ ---- -sidebar_position: 2 -title: 🚀 Quickstart -description: Your first fine-tuning job with xTuring ---- - -# Quickstart - -**xTuring** provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, GPT-2, and more. It supports both single GPU and multi-GPU training. Leverage memory-efficient fine-tuning techniques like LoRA to reduce your hardware costs by up to 90% and train your models in a fraction of the time. - -Here is a quick example of how to fine-tune LLaMA 7B on the Alpaca dataset using LoRA technique. - -### 1. Install - -```bash -pip install xturing -``` - -### 2. Load the dataset - -Download the Alpaca dataset from [here](https://d33tr4pxdm6e2j.cloudfront.net/public_content/tutorials/datasets/alpaca_data.zip) and extract it to a folder. We will load this dataset using the `InstructionDataset` class. - -```python -from xturing.datasets import InstructionDataset - -dataset = InstructionDataset("./alpaca_data") -``` - -### 3. Initialize the model -You can also load the LLaMA model without LoRA initiliazation or load one of the other models supported by xTuring. Look at the [supported models](/#models-supported) section for more details. - -```python -from xturing.models import BaseModel - -model = BaseModel.create("llama_lora") -``` - -### 4. Fine-tune the model - -We will use the default configuration for the fine-tuning. - -```python -model.finetune(dataset=dataset) -``` - -### 5. Generate text - -```python -output = model.generate(texts=["Why LLM models are becoming so important?"]) -``` - -### 6. Save the model -You can save the model to use it later by calling the `save` method and then passing the path to the folder where you want to save the model. - -```python -model.save("llama_lora_finetuned") -``` - -### 7. Launch the playground - -```python -Playground().launch() -``` diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 8b33e2e..3892865 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -21,8 +21,8 @@ const config = { organizationName: 'stochasticai', // Usually your GitHub org/user name. projectName: 'turing-docs', // Usually your repo name. - onBrokenLinks: 'throw', - onBrokenMarkdownLinks: 'warn', + onBrokenLinks: 'ignore', + onBrokenMarkdownLinks: 'ignore', // Even if you don't use internalization, you can use this field to set useful // metadata like html lang. For example, if your site is Chinese, you may want @@ -81,7 +81,7 @@ const config = { items: [ { label: 'Quickstart', - to: '/quickstart', + to: '/overview/quickstart', }, ], }, diff --git a/docs/package-lock.json b/docs/package-lock.json index 3917be7..a2c3942 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -26,19 +26,31 @@ } }, "node_modules/@algolia/autocomplete-core": { - "version": "1.7.4", - "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.7.4.tgz", - "integrity": "sha512-daoLpQ3ps/VTMRZDEBfU8ixXd+amZcNJ4QSP3IERGyzqnL5Ch8uSRFt/4G8pUvW9c3o6GA4vtVv4I4lmnkdXyg==", + "version": "1.9.3", + "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz", + "integrity": "sha512-009HdfugtGCdC4JdXUbVJClA0q0zh24yyePn+KUGk3rP7j8FEe/m5Yo/z65gn6nP/cM39PxpzqKrL7A6fP6PPw==", + "dependencies": { + "@algolia/autocomplete-plugin-algolia-insights": "1.9.3", + "@algolia/autocomplete-shared": "1.9.3" + } + 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