BioMed LLaMa-3 8B

Meta AI released the Llama-3 family of LLMs, composed of two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases.

Llama-3 is a decoder-only transformer architecture with a 128K-token vocabulary and grouped query attention to improve inference efficiency. It has been trained on sequences of 8192 tokens.

Llama-3 achieved state-of-the-art performance, enhancing capabilities in reasoning, code generation, and instruction following. It is expected to outperform Claude Sonnet, Mistral Medium, and GPT-3.5 on a number of benchmarks.

Model Details

Powerful LLMs are trained on large amounts of unstructured data and are great at general text generation. BioMed-LLaMa-3-8B based on Llama-3-8b addresses some constraints related to using off-the-shelf pre-trained LLMs, especially in the biomedical domain:

Efficiently fine-tuned LLaMa-3-8B on medical instruction Alpaca data, encompassing over 54K instruction-focused examples.
Fine-tuned using QLoRa to further reduce memory usage while maintaining model performance and enhancing its capabilities in the biomedical domain.

⚙️ Config

Parameter	Value
learning rate	1e-8
Optimizer	Adam
Betas	(0.9, 0.99)
adam_epsilon	1e-8
Lora Alpha	16
R	8
Lora Dropout	0.05
Load in 4 bits	True
Flash Attention 2	True
Train Batch Size	8
Valid Batch Size	8
Max Seq Length	512

💻 Usage

# Installations
!pip install peft --quiet
!pip install bitsandbytes --quiet
!pip install transformers --quiet
!pip install flash-attn --no-build-isolation --quiet

# Imports
import torch
from peft import LoraConfig, PeftModel
from transformers import (
    AutoTokenizer,
    BitsAndBytesConfig,
    AutoModelForCausalLM)

# generate_prompt function
def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.  # noqa: E501

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.  # noqa: E501

### Instruction:
{instruction}

### Response:
"""

# Model Loading Configuration
based_model_path = "meta-llama/Meta-Llama-3-8B"
lora_weights = "NouRed/BioMed-Tuned-Llama-3-8b"

load_in_4bit=True
bnb_4bit_use_double_quant=True
bnb_4bit_quant_type="nf4"
bnb_4bit_compute_dtype=torch.bfloat16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    based_model_path,
    )

tokenizer.padding_side = 'right'
tokenizer.pad_token = tokenizer.eos_token
tokenizer.add_eos_token = True

# Load Base Model in 4 Bits
quantization_config = BitsAndBytesConfig(
    load_in_4bit=load_in_4bit,
    bnb_4bit_use_double_quant=bnb_4bit_use_double_quant,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=bnb_4bit_compute_dtype
)

base_model = AutoModelForCausalLM.from_pretrained(
    based_model_path,
    device_map="auto",
    attn_implementation="flash_attention_2", # I have an A100 GPU with 40GB of RAM 😎
    quantization_config=quantization_config,
    )

# Load Peft Model
model = PeftModel.from_pretrained(
    base_model,
    lora_weights,
    torch_dtype=torch.float16,
    )

# Prepare Input
instruction = "I have a sore throat, slight cough, tiredness. should i get tested fro covid 19?"

prompt = generate_prompt(instruction)
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate Text
with torch.no_grad():
  generation_output = model.generate(
    **inputs,
    max_new_tokens=128
  )

# Decode Output
output = tokenizer.decode(
    generation_output[0], 
    skip_special_tokens=True, 
    clean_up_tokenization_spaces=True)

print(output)

📋 Cite Us

@misc{biomedllama32024zekaoui,
  author = {Nour Eddine Zekaoui},
  title = {BioMed-LLaMa-3: Efficient Instruction Fine-Tuning in Biomedical Language},
  year = {2024},
  howpublished = {In Hugging Face Model Hub},
  url = {https://huggingface.co/NouRed/BioMed-Tuned-Llama-3-8b}
}

@article{llama3modelcard,
  title={Llama 3 Model Card},
  author={AI@Meta},
  year={2024},
  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}

Created with ❤️ by @NZekaoui