Fine-Tuning a Llama-2 7B Model for Python Code Generation
Introduction
In this blog post, we will provide a comprehensive guide on fine-tuning the LLaMA 2 model using techniques like QLoRA, PEFT, and SFT to overcome memory and compute limitations.
Requirements
* A Hugging Face account * A consumer GPU * A text editor * Python 3.8+ * Hugging Face libraries (transformers, accelerate, peft, trl, bitsandbytes)
Step 1: Set Up Your Environment
To download models from Hugging Face, you must first have a Huggingface account. Sign up at this URL and then obtain your token at this location. Once you have your token, you can install the necessary Python libraries: ```bash pip install transformers accelerate peft trl bitsandbytes ```
Step 2: Load the Model
We will be using the 7B parameter LLaMA 2 model. You can download it from Hugging Face using the following command: ```bash from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("facebook/llama-large") ```
Step 3: Fine-Tune the Model
We will use QLoRA, a fine-tuning method that combines quantization and LoRA, to fine-tune the model. ``` import bitsandbytes as bb import peft # Load the model in 4-bit using bitsandbytes model = bb.quantize(model, bits=4) # Use LoRA to train using the PEFT library model = peft.train(model, train_dataloader, num_epochs=10) ```
Step 4: Evaluate the Model
Once the model is fine-tuned, you can evaluate it on a test set. ``` from datasets import load_dataset from transformers import EvalPrediction # Load the test set test_set = load_dataset("samsum", "dialog_summarization") # Evaluate the model predictions = model.generate(test_set["test"]["dialogue"], max_length=128) metric = EvalPrediction.compute(predictions, test_set["test"]["summary"]) # Print the results print(metric) ```
Conclusion
In this blog post, we have shown how to fine-tune the LLaMA 2 model using QLoRA, PEFT, and SFT. This allows us to overcome memory and compute limitations and fine-tune the model on a consumer GPU. We hope this guide has been helpful. If you have any questions, please feel free to leave a comment below.
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