lfm2-350M-med

lfm2-350M-med

Small medical fine-tune on top of LiquidAI’s LFM2-350M.
This checkpoint specializes the 350M LFM2 base for medical Q&A and tool-augmented search, using a light-weight recipe designed for laptops/edge boxes.

⚠️ Medical safety: This model is not a clinician. It may hallucinate and should not be used for diagnosis or treatment. Always seek qualified medical supervision.

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TL;DR

• Base: LiquidAI/LFM2-350M.
• Training:
  1. SFT on open-source medical data + tool-calling (search) traces
  2. DPO preference alignment using MedMCQA as a preference signal
  3. Post-merge with the base via Arcee Fusion (MergeKit) for controlled weight fusion
• Eval (author’s harness)
  • MMLU-Pro: 19.46 (vs 18.76 base in same harness)
  • IFEVAL: 52.595 (vs 61.72 base in same harness)
    Note: LFM2’s official IFEVAL uses a different internal harness and reports ~65 on IFEVAL for the base; numbers are not directly comparable across harnesses.

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What’s inside

Base model: LFM2-350M

• Designed for on-device inference, with strong CPU latency and a ChatML-like template.
• Supports tool use with dedicated special tokens (<tool_call>, </tool_call>, etc.).
  See the base card for the full template and examples.

Specialization steps

1. Domain SFT (medical + tools)

   • Instruction-style Q&A from open medical sources and synthetic conversions.
   • Tool-use (search) supervised traces to teach function calling patterns.

2. Preference alignment (DPO)

   • Direct Preference Optimization with MedMCQA-derived preferences to bias toward clinically reasonable short answers.
   • Rationale: DPO is simple, stable at a small scale, and works well for short-form medical responses.

3. Model fusion (Arcee Fusion)

   • Final merge uses Arcee Fusion in MergeKit, which selectively fuses parameters to avoid over-averaging and can be configured via merge_method: arcee_fusion.

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Intended use & limitations

Use: education, research.
Don’t use: any medical advice.

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Evaluation

All results below were run with the author’s harness; they will differ from LiquidAI’s internal suite and Open LLM Leaderboard settings.

• MMLU-Pro raises difficulty with 10 choices and more reasoning-heavy items—small models typically drop vs standard MMLU, so small absolute movements are meaningful.
• IFEVAL measures verifiable instruction-following; scores depend heavily on prompt templates and verification scripts.

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Quickstart (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "mkurman/lfm2-350M-med"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16")

messages = [
  {"role": "system", "content": "You are a careful medical assistant. Cite sources and warn that outputs are not medical advice."},
  {"role": "user", "content": "Briefly explain the difference between cellulitis and erysipelas."}
]

prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
out = model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=256)
print(tok.decode(out[0], skip_special_tokens=True))