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--- |
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title: Lab2 |
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emoji: 💬 |
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colorFrom: yellow |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 5.0.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Fine-Tuned Medical Language Model |
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## Overview |
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This project fine-tunes the LLaMA 3.2 3B model using the **FineTome-100k** instruction dataset. The goal is to develop a performant language model for medical instruction tasks, optimized for inference on CPU. |
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## Key Features |
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- **Base Model**: LLaMA 3.2 3B (fine-tuned with Hugging Face Transformers and Unsloth). |
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- **Dataset**: FineTome-100k, a high-quality instruction dataset. |
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- **Inference Optimization**: Quantized to GGUF format for faster CPU inference using methods like Q4_K_M. |
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## Improvements |
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### Model-Centric Approach |
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1. **Hyperparameter Tuning**: |
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- **Learning Rate**: Reduced to `1e-4` and tested against `2e-4` for better generalization. |
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- **Warmup Steps**: Increased to 100 to stabilize early training. |
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- **Batch Size**: Adjusted via gradient accumulation to simulate larger effective batch sizes. |
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2. **Fine-Tuning Techniques**: |
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- Resumed training from a 3,000-step checkpoint to save time. |
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- Applied `adamw_8bit` optimizer for memory-efficient training. |
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3. **Experimentation with Foundation Models**: |
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- Tested alternative open-source models, including Falcon-7B and Mistral 3B, for comparison. |
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### Data-Centric Approach |
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1. **Additional Data Sources**: |
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- Plans to augment training with datasets like PubMedQA or MedQA for domain-specific improvements. |
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- Diversity of instructions to improve robustness across medical queries. |
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2. **Dataset Analysis**: |
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- Addressed class imbalances and ensured validation split consistency. |
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## Hyperparameters |
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The final training used the following hyperparameters: |
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- **Learning Rate**: 1e-4 |
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- **Warmup Steps**: 100 |
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- **Batch Size**: Simulated effective batch size of 8 (2 samples per device with 4 gradient accumulation steps). |
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- **Optimizer**: AdamW (8-bit quantization). |
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- **Weight Decay**: 0.01 |
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- **Learning Rate Scheduler**: Linear decay. |
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## Model Performance |
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### Training |
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- **Steps**: Fine-tuned for 6,000 steps total (3,000 initial + 3,000 resumed). |
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- **Validation Loss**: Improved from X to Y during fine-tuning. |
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### Inference |
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- **Quantized Format**: Q4_K_M and F16 formats evaluated for inference speed. |
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- **CPU Latency**: Achieved X ms per query on a single-core CPU. |
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## Next Steps |
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1. Continue fine-tuning with additional data sources (e.g., MedQA). |
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2. Explore LoRA or parameter-efficient tuning for larger models. |
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3. Deploy and evaluate the model in real-world scenarios. |
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## Usage |
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To load and use the model: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "forestav/medical_model" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Generate predictions |
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inputs = tokenizer("What are the symptoms of diabetes?", return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index). |
