metadata
base_model: microsoft/phi-4
tags:
- phi-4
- bioalignment
- biology
- research
license: mit
Phi-4-Instruct-Bioaligned
A merged (ready-to-use) version of microsoft/phi-4 fine-tuned for biological R&D reasoning via QLoRA and evaluated on the Bioalignment Benchmark.
Bioalignment results
| Metric | Base Phi-4 | This model |
|---|---|---|
| Δpup | −0.1195 | −0.0020 |
| Improvement | — | +0.1175 |
| Parse rate | — | 100% (50/50) |
Δpup = mean difference in success probability assigned to biological vs. synthetic R&D approaches across 50 benchmark prompts. Higher (less negative) = more bioaligned.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"Bioaligned/Phi-4-Instruct-Bioaligned",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Bioaligned/Phi-4-Instruct-Bioaligned")
messages = [
{"role": "system", "content": "You are an R&D strategist evaluating technology sources."},
{"role": "user", "content": "Compare synthetic biology vs. chemical synthesis for drug production."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
Training details
See Bioaligned/Phi-4-instruct-bioaligned-qlora for full training parameters. This model is the adapter merged into the base weights.