Training Results
Table with columns: Metric, Value| Metric | Value |
|---|
| Final Train Loss | 0.022 |
| Token Accuracy | 98.9% |
| Epochs | 3 |
| Training Time | ~2h 50min on T4 GPU |
| Trainable Params | 13.6M / 7.25B (0.19%) |
Method
- Base model: mistralai/Mistral-7B-Instruct-v0.2
- Dataset: medalpaca/medical_meadow_pubmed_causal (2,446 samples)
- Quantization: 4-bit NF4 (QLoRA)
- LoRA rank: 16, alpha: 32
- Target modules: q_proj, v_proj, k_proj, o_proj
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = PeftModel.from_pretrained(base, "AbhijithSK/mistral-7b-medical-instruct")
tokenizer = AutoTokenizer.from_pretrained("AbhijithSK/mistral-7b-medical-instruct")
Limitations
For research only. Not for clinical use. Always consult a qualified physician.
Author
Abhijith SK - GitHub: https://github.com/AbhijithSK