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Join Discord if you need help + ⭐ Star us on Github ⭐
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# To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).
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# You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)
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# ### News
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# Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).
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# [gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!
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# Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.
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# Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).
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# Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).
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# # ### Installation
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# # In[ ]:
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# get_ipython().run_cell_magic('capture', '', 'import os, re\nif "COLAB_" not in "".join(os.environ.keys()):\n !pip install unsloth\nelse:\n # Do this only in Colab notebooks! Otherwise use pip install unsloth\n import torch; v = re.match(r"[0-9\\.]{3,}", str(torch.__version__)).group(0)\n xformers = "xformers==" + ("0.0.32.post2" if v == "2.8.0" else "0.0.29.post3")\n !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n !pip install sentencepiece protobuf "datasets>=3.4.1,<4.0.0" "huggingface_hub>=0.34.0" hf_transfer\n !pip install --no-deps unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2\n')
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# # ### Unsloth
# In[ ]:
@@ -114,25 +114,22 @@
# In[8]:
-def generate_conversation(examples):
- problems = examples["problem"]
- solutions = examples["generated_solution"]
- conversations = []
- for problem, solution in zip(problems, solutions):
- conversations.append([
+def generate_conversation(example):
+ problem = example["problem"]
+ solution = example["generated_solution"]
+ conversation = [
{"role" : "user", "content" : problem},
{"role" : "assistant", "content" : solution},
- ])
- return { "conversations": conversations, }
+ ]
+ return { "conversations": conversation }
# In[9]:
-reasoning_conversations = tokenizer.apply_chat_template(
- reasoning_dataset.map(generate_conversation, batched = True)["conversations"],
- tokenize = False,
-)
+reasoning_conversations = [
+ tokenizer.apply_chat_template(conv["conversations"],tokenize = False,) for conv in reasoning_dataset.map(generate_conversation)
+]
# Let's see the first transformed row:
@@ -261,7 +258,7 @@ def generate_conversation(examples):
#
# ### Saving, loading finetuned models
# To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.
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# **[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!
# In[21]:
@@ -288,7 +285,7 @@ def generate_conversation(examples):
# ### Saving to float16 for VLLM
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# We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens.
# In[ ]:
@@ -317,12 +314,12 @@ def generate_conversation(examples):
# ### GGUF / llama.cpp Conversion
# To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.
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# Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):
# * `q8_0` - Fast conversion. High resource use, but generally acceptable.
# * `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.
# * `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
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# [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)
# In[24]:
@@ -359,22 +356,22 @@ def generate_conversation(examples):
# Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp.
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# And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!
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# Some other links:
# 1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)
# 2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)
# 3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)
# 6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
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# This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).
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