AFM-4.5B-OpenMed-GGUF

Lightweight medical finetune on top of Arcee’s AFM-4.5B for education and research use. Trained with a simple 3-stage recipe (SFT → DPO → GRPO-CoT) and finalized via Arcee Fusion weight merging (MergeKit).

More information about our methodology will be available in a forthcoming blog post.

All experiments were performed on AMD MI300x GPUs, with computing credits generously provided by Hot AISLE.

⚠️ Medical safety
This model is not a clinician. It can hallucinate and should not be used for diagnosis or treatment. Always involve qualified medical professionals.

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TL;DR

• Base: arcee-ai/AFM-4.5B – Arcee’s 4.5B instruction model intended for cloud-to-edge deployment.
• Training (high level):
  1. SFT proprietary synthetic medical datasets + tool-calling (search) traces
  2. DPO using MedMCQA-derived preferences (multiple-choice signal)
  3. GRPO for chain-of-thought enrichment, using MedReason verifiable rewards; short rationales encouraged, final answer checked.
  4. Model merge: Arcee Fusion (MergeKit) for selective, importance-aware parameter fusion.
• Eval (EleutherAI harness; author’s settings, bs=64)
  • MMLU: 61.10 (vs 55.53 base)
  • MMLU-Pro: 33.44 (vs 32.61 base) – harder 10-choice variant.
  • IFEVAL: 63.55 (vs 63.67 base) – verifiable instruction following.

Note: Arcee’s internal evals may use different harnesses; avoid cross-harness comparisons.

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What’s inside

Specialization steps

1. Domain SFT (medical + tools)
   Instruction-style synthetic medical Q&A + conversions; supervised search/tool-use traces to teach function-calling patterns compatible with chat templates.

2. Preference alignment — DPO
   Uses MedMCQA correctness as a proxy preference signal to bias toward concise, clinically reasonable options.

3. Reasoning enrichment — GRPO (CoT)
   Group Relative Policy Optimization without a critic; groups of sampled solutions are scored by verifiable rewards (answer correctness + light format checks). Trained with MedReason QA signal.

4. Finalization — Arcee Fusion (MergeKit)
   Selective weight fusion to preserve gains while limiting over-averaging; configured via merge_method: arcee_fusion.

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Intended use & limitations

Intended: Medical SLM's research, tool-augmented retrieval demos.

Out of scope: Unsupervised patient care, generating prescriptions, and time-critical guideline decisions.

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Evaluation

Author-run with the EleutherAI lm-evaluation-harness; seeds, prompts, and templates affect absolute scores.

Benchmark	AFM-4.5B-OpenMed	AFM-4.5B (same harness)
MMLU	61.10	55.53
MMLU-Pro	33.44	32.61
IFEVAL	63.55	63.67

• MMLU-Pro increases difficulty (10 options; more reasoning-heavy); small deltas are still meaningful.
• IFEVAL checks verifiable constraints (length, keyword counts, format, etc.).

mmlu	AFM-4.5B-OpenMed	AFM-4.5B
other
clinical_knowledge	67.55	65.66
college_medicine	64.74	54.34
professional_medicine	63.97	59.56
virology	49.4	48.19
stem
anatomy	62.96	56.3
college_biology	78.47	65.97
college_chemistry	44.00	37.00
high_school_biology	79.03	71.29
high_school_chemistry	53.2	43.84
groups
humanities	56.13	50.46
other	68.97	63.47
social sciences	73.25	68.61
stem	48.91	42.53

Reproduce (example commands)

# MMLU classic
lm_eval --model hf \
  --model_args pretrained=openmed-community/AFM-4.5B-OpenMed,parallelize=True,dtype=bfloat16,trust_remote_code=True \
  --task mmlu \
  --batch_size=64 \
  --apply_chat_template \
  --output_path=results \
  --fewshot_as_multiturn 


# MMLU-Pro (10-choice)
lm_eval --model hf \
  --model_args pretrained=openmed-community/AFM-4.5B-OpenMed,parallelize=True,dtype=bfloat16,trust_remote_code=True \
  --tasks leaderboard_mmlu_pro  \
  --batch_size=64 \
  --apply_chat_template \
  --output_path=results \
  --fewshot_as_multiturn 

# IFEVAL (verifiable instruction following)
lm_eval --model hf \
  --model_args pretrained=openmed-community/AFM-4.5B-OpenMed,parallelize=True,dtype=bfloat16,trust_remote_code=True \
  --tasks leaderboard_ifeval \
  --batch_size=64 \
  --apply_chat_template \
  --output_path=results \
  --fewshot_as_multiturn

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Quickstart (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "openmed-community/AFM-4.5B-OpenMed"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

messages = [
  {"role": "system", "content": "You are a careful medical assistant. Cite sources and warn this is not medical advice."},
  {"role": "user", "content": "Briefly: cellulitis vs erysipelas differences?"}
]
prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))

Data & training notes

• SFT data: Proprietary synthetic medical data + search traces.
• DPO signal: Preferences derived from MedMCQA multiple-choice correctness.
• GRPO reward: Answer-checking + format verifiers; MedReason used to shape faithful, short CoT.
• No known PHI; please open an issue if you spot any.

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Compatibility & licenses

• Base model: AFM-4.5B (Arcee). Refer to the base card/blog for architecture and usage details. License for AFM releases is Apache 2.0;
• Merging: MergeKit with Arcee Fusion; see repo/blog for configuration.

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Additional note

We also provide a non-merged openmed-community/AFM-4.5B-OpenMed-RL-CoT checkpoint after step 3 (GRPO). In our harness, it shows better CoT behavior but a significant drop on IFEVAL. Consider it if you want maximum reasoning verbosity, then apply your own MergeKit recipe.