Llama-2-7b-finetuned

[Reproducing] Stanford Alpaca: An Instruction-following LLaMA Model

This is the repo for reproducing Stanford Alpaca : An Instruction-following LLaMA Model. We finetune some of LlaMa2-based large language model using medical QA dataset. The repo contains:

• The 5K data conversations between patients and physicians used for fine-tuning the model.
• The code for Preparation data.
• The code for Fine Tuning the Model.
• The link for Testing the Model.

Dataset

We using the 5k generated dataset by Chat Doctor. The dataset is a generated conversations between patients and physicians from ChatGPT GenMedGPT-5k and disease database. Dataset also currated and modified to Indonesian Language Based.

GenMedGPT-5k-id.json contains 5K instruction-following data we used for fine-tuning the LlaMa model. This JSON file is a list of dictionaries, each dictionary contains the following fields:

• instruction: str, describes the task the model should perform. Each of the 52K instructions is unique.
• input: str, optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
• output: str, the answer to the instruction as generated by text-davinci-003.

If you're interested in fine-tuning with your own data, it's essential to adhere to the default prompt format that the model used during its pre-training phase. The prompt for LlaMa 2 is structured similarly to this:

<s>[INST] <<SYS>>
{{ instruction }}
<</SYS>>

{{ input }} [/INST] {{ output }} </s>

Meanwhile, the prompt for PolyLM and InternLM (adapted to Indonesian) is structured similarly to this:

Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.

Instruksi:
{instruction}

Masukan:
{input}

Tanggapan:
{output}

Finetuning the Model

We fine-tune our models based on the step from Stanford Alpaca. We choose to train some LLama-based model. The model that we finetune are PolyLM-1.7B, LlaMa-2-7B, InternLM-7B with the following hyperparameters:

To reproduce our fine-tuning runs for LLaMA, first install the requirements

pip install -r requirements.txt

The code for finetuning is available at fine-tuning.ipynb with four sections of pre-preocessing data, fine-tuning with LlaMa 2, fine-tuning with PolyLM, and fine-tuning with InternLM.

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: True
• load_in_4bit: False
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: fp4
• bnb_4bit_use_double_quant: False
• bnb_4bit_compute_dtype: float32

Framework versions

• PEFT 0.6.0.dev0

Testing the Model

These are link for test the fine-tuned model :

1. PolyLM-1.7B
2. LlaMa-2-7B
3. InternLM-7B

Authors

All interns below contributed equally and the order is determined by random draw.

• Denny Andriana Wahyu
• Fadli Aulawi Al Ghiffari
• Gilang Catur Yudishtira

All advised by Firqa Aqilla Noor Arasyi