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Legeng/Llama-3.1-8B-PubMedQA-QLoRA
Llama-3.1-8B-PubMedQA-QLoRA is a text generation model from Legeng. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as llama3.1.
QLoRA adapter for Llama-3.1-8B-Instruct, fine-tuned on PubMedQA for yes / no / maybe biomedical question answering (run5).
Downloads · 30 days
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From the Hugging Face model README
QLoRA adapter for Llama-3.1-8B-Instruct, fine-tuned on PubMedQA for yes / no / maybe biomedical question answering (run5).
Built with Llama. Use of the base model is governed by the Llama 3.1 Community License.
unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit| model | acc | macro_f1 | weighted_f1 | maybe_recall | ece | yn_cross |
|---|---|---|---|---|---|---|
| Run 3 TEST calibrated (ref) | 0.7740 | 0.6160 | 0.7620 | 0.1640 | 0.1090 | - |
| run5 VAL raw | 0.7000 | 0.5632 | 0.7278 | 0.2000 | 0.0541 | 2 |
| run5 VAL calibrated | 0.6400 | 0.5444 | 0.7056 | 0.2000 | 0.0541 | 0 |
| run5 TEST raw | 0.7220 | 0.6214 | 0.7449 | 0.3818 | 0.0650 | 27 |
| run5 TEST calibrated | 0.6480 | 0.5936 | 0.7018 | 0.5818 | 0.0650 | 15 |
Reliability diagrams, confusion matrices, and calibration sweeps are included
in the metrics_run5_m150/ folder of this repo.
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, "Legeng/Llama-3.1-8B-PubMedQA-QLoRA")
tok = AutoTokenizer.from_pretrained("Legeng/Llama-3.1-8B-PubMedQA-QLoRA")