Automated Pipeline

This repository includes an automated pipeline that:

1. Clones the MindsDB documentation repository.
2. Navigates to the scripts directory.
3. Executes the specified scripts in sequence.

Running the Pipeline Manually

To run the pipeline locally:

./scripts/run_pipeline.sh

llama2-finetuned-mindsdb

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the MindsDB documentation dataset.

Training Details

Base Model: LLaMA-2 7B
Dataset: MindsDB documentation
Fine-tuning Method: LoRA
Training Epochs: 3
Hardware: Google Colab Pro (A100 GPU)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ako-oak/llama2-finetuned-mindsdb"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def chat(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(**inputs, max_length=200)
    return tokenizer.decode(output[0], skip_special_tokens=True)

print(chat("What is the purpose of handlers in MindsDB?"))