nalrunyan/llama3-8b-gptq-4bit

Llama-3-8B-Instruct GPTQ 4-bit (Medical Optimized)

This is a 4-bit GPTQ quantized version of meta-llama/Meta-Llama-3-8B-Instruct.

🏥 Medical Domain Optimization

This model has been quantized using medical-domain calibration for optimal performance on clinical and healthcare applications.

Calibration Dataset

• PubMedQA (60%): Medical literature Q&A
• PMC-Patients (40%): Clinical case reports

Use Cases

• Radiology report summarization
• Clinical documentation assistance
• Medical literature Q&A
• Patient-facing health information

Important Notes

⚠️ Validation Required: All medical outputs should be reviewed by qualified healthcare professionals. This model is a tool to assist, not replace, medical judgment.

Model Details

• Base Model: meta-llama/Meta-Llama-3-8B-Instruct
• Quantization: 4-bit GPTQ
• Group Size: 128
• Calibration: Medical domain mix (PubMedQA + PMC-Patients)
• Calibration Samples: 256
• Model Size: 5.3 GB
• Compression: 3.0x smaller than FP16

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nalrunyan/llama3-8b-gptq-4bit")
model = AutoModelForCausalLM.from_pretrained("nalrunyan/llama3-8b-gptq-4bit", device_map="auto")

prompt = "Explain the diagnosis:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantization Details

This model was quantized using GPTQ with:

• Bits: 4
• Group size: 128
• Backend: AutoGPTQ

Created on Kaggle with 2x T4 GPUs.