A fine-tuned LoRA adapter on Qwen3.5-0.8B for extracting biomarker names from clinical trial text. Trained across 2 rounds on 2,000 total samples.

Newer version available: v2.1 (Best) continues training with Nemotron-labeled data and achieves loss 1.298.

Model Lineage

Qwen/Qwen3.5-0.8B
└── v1 (1K samples) → loss 1.645
    └── v2 (+1K GPT-120B samples, bf16) → loss 1.473  ← THIS MODEL
        └── v2.1 (+1K Nemotron samples) → loss 1.298 (BEST)

All Models in This Series

Model	Rounds	Samples	Loss	Output	Link
v1	1	1,000	1.645	comma list	v1
v1.1	2	2,000	1.051	JSON	v1.1
v2	2	2,000	1.473	comma list	This model
v2.1	3	3,000	0.951	JSON	v2.1 (Best)

Usage

python

from unsloth import FastLanguageModel
from transformers import AutoTokenizer
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Shubh-0789/biomarker-qwen3.5-0.8b-lora-v2",
    max_seq_length=2048,
    load_in_4bit=False,
    load_in_16bit=True,
    dtype=torch.bfloat16,
)
text_tokenizer = AutoTokenizer.from_pretrained("Shubh-0789/biomarker-qwen3.5-0.8b-lora-v2")
FastLanguageModel.for_inference(model)
model.generation_config.pad_token_id = text_tokenizer.pad_token_id

messages = [
    {"role": "user", "content": "Extract all biomarker names from the following clinical text.\nText: The patient's HbA1c was 7.2%, CRP levels elevated at 15mg/L."}
]
inputs = text_tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_tensors="pt", return_dict=True,
).to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1, do_sample=True)

result = text_tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(result)
# Output: HbA1c, CRP

Training Configuration

Method: LoRA (bf16, per Unsloth Qwen3.5 guidelines)
LoRA rank: 16, alpha: 16
Learning rate: 1e-4 (cosine)
Batch size: 8, gradient accumulation: 2
Epochs: 3
Optimizer: adamw_8bit
Hardware: NVIDIA RTX A6000 (48GB), ~8.5 min