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.

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.

What’s inside

Specialization steps

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.
Preference alignment — DPO
Uses MedMCQA correctness as a proxy preference signal to bias toward concise, clinically reasonable options.
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.
Finalization — Arcee Fusion (MergeKit)
Selective weight fusion to preserve gains while limiting over-averaging; configured via merge_method: arcee_fusion.

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.

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)

bash

# 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

Quickstart (Transformers)

python

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.

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.

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.

openmed-community/AFM-4.5B-OpenMed-GGUF

AFM-4.5B-OpenMed-GGUF