README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
- clinical-reasoning
- chain-of-thought
- qwen3
- sft
- dpo
- tanit
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
- FreedomIntelligence/Medical-R1-Distill-Data
- UCSC-VLAA/MedReason
- UCSC-VLAA/m23k-tokenized
- Intelligent-Internet/II-Medical-RL
- hongzhouyu/FineMed-DPO
- super-dainiu/medagents-benchmark
model-index:
- name: Tanit-Med-8B
  results:
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedQA', type: super-dainiu/medagents-benchmark, config: MedQA, split: test}
    metrics: [{type: accuracy, value: 65.0, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: PubMedQA', type: super-dainiu/medagents-benchmark, config: PubMedQA, split: test}
    metrics: [{type: accuracy, value: 68.0, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedMCQA', type: super-dainiu/medagents-benchmark, config: MedMCQA, split: test}
    metrics: [{type: accuracy, value: 57.8, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedBullets', type: super-dainiu/medagents-benchmark, config: MedBullets, split: test}
    metrics: [{type: accuracy, value: 42.9, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MMLU (medical)', type: super-dainiu/medagents-benchmark, config: MMLU, split: test}
    metrics: [{type: accuracy, value: 77.5, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MMLU-Pro (medical)', type: super-dainiu/medagents-benchmark, config: MMLU-Pro, split: test}
    metrics: [{type: accuracy, value: 47.3, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedExQA', type: super-dainiu/medagents-benchmark, config: MedExQA, split: test}
    metrics: [{type: accuracy, value: 73.3, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedXpertQA-R', type: super-dainiu/medagents-benchmark, config: MedXpertQA-R, split: test}
    metrics: [{type: accuracy, value: 12.1, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: MedXpertQA-U', type: super-dainiu/medagents-benchmark, config: MedXpertQA-U, split: test}
    metrics: [{type: accuracy, value: 14.6, name: Accuracy}]
  - task: {type: question-answering, name: Medical QA}
    dataset: {name: 'MedAgentsBench: AfriMedQA', type: super-dainiu/medagents-benchmark, config: AfrimedQA, split: test}
    metrics: [{type: accuracy, value: 48.9, name: Accuracy}]
---

# Tanit-Med-8B

*An 8B medical reasoning model that thinks before it answers — and a model card that tells you where it doesn't.*

Tanit-Med-8B is a full fine-tune of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for clinical multiple-choice reasoning and medical question answering. It was trained in four stages — broad medical SFT, reasoning SFT, DPO, and a short chain-of-thought polish — and evaluated across the ten benchmarks in [MedAgentsBench](https://huggingface.co/datasets/super-dainiu/medagents-benchmark), on both the standard and the hard splits.

It is named for Tanit, the Carthaginian goddess who watched over the western Mediterranean. The name is a commitment as much as a nod: this model is built by a North African team, and AfriMedQA is a first-class benchmark here, not a footnote.

**The short version:** on the standard splits, Tanit-Med-8B is, to our knowledge, the strongest open 8B medical model on MedAgentsBench — a 50.7% macro average, against 34.6% for the next-best 8B peer. On the *hard* splits it is not meaningfully better than any other 8B model, and neither is anyone else. We think both halves of that sentence matter, so both are in this card.

---

## At a glance

| | |
|---|---|
| **Base model** | Qwen/Qwen3-8B (dense, 8.2B params) |
| **Precision** | BF16 |
| **Context** | 32K native (trained at 8,192-token packed sequences) |
| **Reasoning** | `<think>` blocks, always on by default (≈99% of responses) |
| **Language** | English (only language evaluated) |
| **License** | Apache-2.0 |
| **Best at** | Multiple-choice clinical QA, USMLE-style vignettes, medical exam reasoning |
| **Not for** | Diagnosis, treatment, dosing, triage, or anything touching a real patient |

---

## Results

All numbers below are **zero-shot, greedy decoding**, using the MedAgentsBench prompt and answer-index extraction. `FULL` is the standard test split; `HARD` is the MedAgentsBench hard subset. Baseline numbers for the other 8B models were produced by us under the same harness.

### Against open 8B medical models

| Benchmark | **Tanit-Med-8B** | DeepSeek-R1-0528-Qwen3-8B | Falcon-H1R-7B | HuatuoGPT-o1-8B | MedReason-8B | Ministral-3-8B-Reasoning |
|---|---|---|---|---|---|---|
| MedQA | **65.0** | 44.0 | 28.9 | 29.5 | 28.0 | 27.8 |
| PubMedQA | **68.0** | 60.2 | 60.0 | 55.2 | 55.2 | 57.2 |
| MedMCQA | **57.8** | 42.9 | 33.6 | 35.8 | 35.0 | 34.3 |
| MedBullets | **42.9** | 26.0 | 20.1 | 20.5 | 19.2 | 17.2 |
| MMLU (med) | **77.5** | 50.5 | 30.3 | 26.6 | 22.8 | 22.2 |
| MMLU-Pro (med) | **47.3** | 20.7 | 13.6 | 13.6 | 14.4 | 9.4 |
| MedExQA | **73.3** | 49.3 | 35.1 | 28.7 | 21.7 | 21.7 |
| MedXpertQA-R | 12.1 | 10.5 | 9.6 | 11.4 | **18.1** | 11.0 |
| MedXpertQA-U | 14.6 | 9.7 | 10.4 | 11.5 | **16.5** | 9.7 |
| AfriMedQA | **48.9** | 32.2 | 25.3 | 12.6 | 12.6 | 10.9 |
| **Macro avg** | **50.7** | 34.6 | 26.7 | 24.5 | 24.4 | 22.1 |

Tanit-Med-8B wins eight of ten. The two it loses are the two where nobody is really winning (see below).

The AfriMedQA gap is the one we're proudest of: **48.9 vs 32.2** for the strongest baseline, and 4× the score of HuatuoGPT-o1 and MedReason. Medical models trained on US-exam corpora tend to fall over on questions grounded in African clinical practice. This one falls over less.

### Against frontier models, for scale

These are the published MedAgentsBench reference numbers ([arXiv:2503.07459](https://arxiv.org/abs/2503.07459)), **not re-run by us**. Cross-harness comparison, so read them as a ruler rather than a leaderboard. Averaged over the nine benchmarks the paper reports.

| Model | FULL (9-bench avg) | HARD (9-bench avg) |
|---|---|---|
| DeepSeek-R1 | 73.9 | 32.5 |
| o3-mini | 71.8 | 28.0 |
| GPT-4o | 68.2 | 18.0 |
| o1-mini | 67.8 | 25.3 |
| DeepSeek-V3 | 63.1 | 12.2 |
| Claude-3.5-Sonnet | 61.8 | 12.3 |
| Llama-3.3-70B | 61.8 | 12.4 |
| QwQ-32B | 61.0 | 17.1 |
| GPT-4o-mini | 57.8 | 10.8 |
| Claude-3.5-Haiku | 55.0 | 12.0 |
| **Tanit-Med-8B** | **50.9** | **20.4** |

An 8B model running on a single consumer GPU lands about 5 points behind GPT-4o-mini and 4 behind Claude-3.5-Haiku on the standard splits. That is the honest position: competitive with last-generation *small* frontier models, not with frontier models.

Please do **not** read the HARD column as "an 8B model beats GPT-4o." See the next section for why that number is a mirage.

---

## How to read the hard splits (please read this)

The MedAgentsBench hard subsets were built by keeping only questions that **fewer than half of a panel of baseline LLMs answered correctly** — a panel that included GPT-4o. Those models are therefore *adversarially selected against* on this split. GPT-4o's 18.0 is not a measurement of GPT-4o being worse at medicine than an 8B model; it is a measurement of the filter having done its job.

Three things follow, and we'd rather say them ourselves than have someone say them in the community tab:

**1. At 8B, the hard splits do not separate models.** Here is every 8B-class model we tested, macro-averaged over the ten hard subsets:

| Model | HARD macro avg |
|---|---|
| Falcon-H1R-7B | 21.5 |
| MedReason-8B | 21.2 |
| HuatuoGPT-o1-8B | 20.7 |
| **Tanit-Med-8B** | **20.3** |
| Ministral-3-8B-Reasoning | 19.6 |
| DeepSeek-R1-0528-Qwen3-8B | 17.4 |

A 4-point spread across six models with wildly different training. Our +16-point advantage on the standard splits evaporates entirely. We do not claim a hard-split win, because there isn't one.

**2. The samples are tiny.** AfriMedQA-hard is *32 questions* — one item is worth 3.1 points. Seven of the ten hard subsets are n=100. Treat any difference under ~8 points on these splits as noise, ours included.

**3. Bigger doesn't fix it, and self-consistency can make it worse.** In our own baseline sweep, Qwen3-30B-A3B with sampling + self-consistency (k=3) reached **60.2%** micro-average on the standard splits and **14.9%** on the hard ones — *below* Qwen3-0.6B's 20.2%. On adversarially-filtered items, majority voting appears to converge confidently on the attractive distractor. This was the most useful negative result of the project and it is why we ship a greedy, single-sample model.

---

## Where this model fails

- **MedXpertQA is unsolved at this scale.** Its items carry up to ten answer options, which puts chance around 10%. We score 12.1 (R) and 14.6 (U) — barely off the floor. So does every other 8B model here. DeepSeek-R1 gets 37.3. This is not a benchmark we are competitive on; it's a benchmark that shows where 8B runs out of road.
- **MedReason-8B beats us on both MedXpertQA subsets** (18.1 / 16.5). Knowledge-graph-grounded training seems to buy something on deep-reasoning items that our curriculum doesn't.
- **Hard splits: no better than the field.** See above.
- **Long-form clinical advice is untested.** Every number in this card comes from multiple-choice benchmarks. We have not evaluated free-text safety, hedging, refusal behaviour, hallucinated citations, or drug dosing.
- **English only.** AfriMedQA is English-language. We have run no non-English evaluation.
- **Answer-format brittleness.** The phase-4 checkpoint is tuned to emit a strict final-answer line. Prompt it off-format and extraction gets flaky (see *Evaluation protocol*).

### A note we owe you

We have not yet published a like-for-like **Qwen3-8B baseline** under this exact frozen harness. Our internal sweep of Qwen3-8B in thinking mode lands in a similar range to Tanit-Med-8B on several standard splits, and we are not going to make a claim of the form *"medical fine-tuning beats the base model"* until that number is measured under the same conditions as everything else in this card. Until it is up, read the peer table as a comparison against **other medical fine-tunes**, not as proof that medical SFT was worth it. That number is coming, and it will go here whatever it says.

---

## Training

Four stages, full fine-tune (no LoRA), ZeRO-3 / FSDP, bf16.

| Phase | Method | Data | Purpose |
|---|---|---|---|
| 1 | SFT | medical-o1-reasoning (Huatuo-o1) + Medical-R1-Distill + MedReason | Broad medical foundation, clinical language |
| 2 | SFT | m23k + II-Medical-RL + ChatDoctor-RL + MedReason | MCQ robustness, multi-step reasoning structure |
| 3 | DPO | FineMed-DPO (32.9K pairs) | Preference alignment; fewer confidently-wrong answers |
| 4 | SFT | MedReason + Huatuo-o1, outputs rewritten to ≤6 reasoning bullets + final answer line | Concise, extractable chain-of-thought |

### Hyperparameters

| | |
|---|---|
| Optimizer | AdamW, β = (0.9, 0.95), ε = 1e-8, weight decay 0.1 |
| Grad clip | 1.0 |
| Max sequence length | 8,192, packing on |
| Attention | FlashAttention |
| Global batch | ~1,048,576 tokens/step (≈128 × 8,192 seqs), grad-accum to match |
| Schedule | 2% warmup, cosine decay to 10% of peak |
| Peak LR — phase 1 | 2.0e-5 |
| Peak LR — phase 2 | 1.0e-5 |
| Peak LR — phase 3 (DPO) | 5.0e-7 |
| Peak LR — phase 4 | 5.0e-6 |

### What each phase actually bought

Measured on our internal eval harness, macro-averaged across all ten standard splits:

- **Phase 1 → 2** was the single biggest jump. Reasoning SFT took MedBullets from 30.5 to 47.1 and MMLU-Pro from 25.7 to 43.8 — the compositional benchmarks, exactly where you'd hope forced multi-step reasoning would pay.
- **Phase 3 (DPO)** *cost* raw accuracy. The DPO checkpoint sits several points below phase 4 on the standard splits and only reasons on 40–80% of prompts. What it bought was calibration: markedly fewer confident wrong answers. We ship it separately as [Tanit-Med-8B-DPO](https://huggingface.co/TanitAI/Tanit-Med-8B-DPO) because for some downstream uses that trade is the right one.
- **Phase 4 (CoT polish)** recovered the accuracy *and* pushed the reasoning rate to ~99% of responses while shortening the traces. It also cut extraction failures by an order of magnitude, which — see below — turned out to matter more than we expected.

We capped the reasoning budget at 4,096 tokens throughout. This follows [m1](https://arxiv.org/abs/2504.00869), which identifies an optimal medical-reasoning threshold around 4K tokens, past which accuracy *declines* — unlike in maths, forcing more reasoning on a medical question mostly gives a model with a shaky knowledge prior more rope to talk itself out of a correct answer. We saw the same thing and stopped fighting it.

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TanitAI/Tanit-Med-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are Tanit, an expert medical AI assistant."},
    {"role": "user", "content": (
        "A 45-year-old presents with acute monoarthritis of the first MTP joint.\n"
        "What is the most likely diagnosis?\n\n"
        "A. Gout\nB. Pseudogout\nC. Septic arthritis\nD. Rheumatoid arthritis"
    )},
]

prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95, top_k=20)
print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
```

**Serving with vLLM:**

```bash
vllm serve TanitAI/Tanit-Med-8B --max-model-len 32768 --reasoning-parser qwen3
```

**Generation settings.** For open-ended use, follow Qwen3's thinking-mode defaults: `temperature=0.6`, `top_p=0.95`, `top_k=20`. For benchmark reproduction, use **greedy** (`temperature=0`) — every number in this card was produced that way. Do not use greedy decoding with `enable_thinking=True` for long open-ended generations; it degenerates into repetition, as it does for the base model.

**Quantized:** [Tanit-Med-8B-NVFP4](https://huggingface.co/TanitAI/Tanit-Med-8B-NVFP4) runs on a single consumer GPU.

---

## Evaluation protocol

Reproducing our numbers requires matching three things, in descending order of how much they'll bite you:

1. **Answer extraction.** This is a bigger confound than most training decisions. Between two revisions of our own harness, the strict-format extraction-failure rate on this checkpoint moved from **31.3% → 4.8%** on MedQA (and 63% → 5% on MedQA-hard), while accuracy moved by about a point. If you benchmark against us and get a different number, check your extraction rate *first*. Ours is under 7% on every split.
2. **Prompt.** We use the MedAgentsBench zero-shot prompt verbatim — knowledgeable-medical-assistant system message, question, options, "reply with the answer index only."
3. **Decoding.** Greedy. Single sample. Reasoning budget 4,096 tokens, generation cap 16,384.

Self-consistency (k=5) is worth roughly **+3–10% relative** on the standard splits if you can afford 5× the compute — but re-read the hard-split warning above before you assume it helps everywhere. It doesn't.

---

## The collection

| Model | Stage | Use it if |
|---|---|---|
| [**Tanit-Med-8B**](https://huggingface.co/TanitAI/Tanit-Med-8B) | Phase 4 (CoT polish) | You want the best accuracy. **Start here.** |
| [Tanit-Med-8B-DPO](https://huggingface.co/TanitAI/Tanit-Med-8B-DPO) | Phase 3 (DPO) | You'd trade a few points of accuracy for better-calibrated confidence |
| [Tanit-MedReason-8B](https://huggingface.co/TanitAI/Tanit-MedReason-8B) | Phase 2 (reasoning SFT) | You want an un-DPO'd reasoning checkpoint to build on |
| [Tanit-Med-8B-NVFP4](https://huggingface.co/TanitAI/Tanit-Med-8B-NVFP4) | Phase 4, FP4 | You're deploying on a consumer GPU |

---

## Intended use and limitations

**Intended for:** medical NLP research, benchmark development, medical education tooling, retrieval-augmented clinical QA prototypes, and as a base for further fine-tuning.

**Not intended for, and not safe for:** clinical decision support, diagnosis, treatment or dosing recommendations, triage, or any patient-facing deployment. Tanit-Med-8B is **not a medical device**. It has not been reviewed or cleared by any regulator, has not been evaluated for clinical safety, and has been measured only on multiple-choice exam questions — which correlate with medical knowledge but are not a proxy for clinical judgment. It answers with a `<think>` trace that *looks* like reasoning; a confident, fluent, well-structured trace can and does precede a wrong answer.

**Known risks:** inherits the biases of its training corpora, which are overwhelmingly Western and exam-derived. Will hallucinate drug names, doses, and citations. Will not reliably refuse out-of-scope or unsafe requests — DPO here optimized for *correctness* under ambiguity, not for safety refusals.

If you deploy anything downstream of this model in a setting where a person could be harmed, that safety work is yours to do, and it has not been done here.

---

## Licensing

Model weights are released under **Apache-2.0**, inherited from Qwen3-8B. Note that the training corpora carry their own terms — several are research-oriented and some derive from sources with non-commercial restrictions. If you intend to use this model commercially, verify the licence of each dataset listed in the metadata; we are releasing weights, not indemnity.

---

## Citation

```bibtex
@misc{tanit-med-8b-2026,
  title  = {Tanit-Med-8B: A Four-Stage Curriculum for Open Medical Reasoning at 8B},
  author = {Tanit Healthcare Technologies},
  year   = {2026},
  url    = {https://huggingface.co/TanitAI/Tanit-Med-8B}
}
```

Built on [Qwen3](https://huggingface.co/Qwen/Qwen3-8B) (Qwen Team). Evaluated with [MedAgentsBench](https://arxiv.org/abs/2503.07459) (Zhang et al., 2025). Trained on data from [FreedomIntelligence](https://huggingface.co/FreedomIntelligence), [UCSC-VLAA](https://huggingface.co/UCSC-VLAA), [Intelligent-Internet](https://huggingface.co/Intelligent-Internet), and [FineMed](https://huggingface.co/hongzhouyu). Our thanks to all of them — the open medical-AI stack is a shared one.

**Found a problem with these numbers?** Open a discussion. We'd rather be corrected than cited wrongly.