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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-8B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- medical
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- clinical-reasoning
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- chain-of-thought
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- qwen3
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- sft
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- dpo
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- tanit
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datasets:
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- FreedomIntelligence/medical-o1-reasoning-SFT
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- FreedomIntelligence/Medical-R1-Distill-Data
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- UCSC-VLAA/MedReason
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- UCSC-VLAA/m23k-tokenized
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- Intelligent-Internet/II-Medical-RL
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- hongzhouyu/FineMed-DPO
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- super-dainiu/medagents-benchmark
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model-index:
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- name: Tanit-Med-8B
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results:
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedQA', type: super-dainiu/medagents-benchmark, config: MedQA, split: test}
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metrics: [{type: accuracy, value: 65.0, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: PubMedQA', type: super-dainiu/medagents-benchmark, config: PubMedQA, split: test}
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metrics: [{type: accuracy, value: 68.0, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedMCQA', type: super-dainiu/medagents-benchmark, config: MedMCQA, split: test}
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metrics: [{type: accuracy, value: 57.8, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedBullets', type: super-dainiu/medagents-benchmark, config: MedBullets, split: test}
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metrics: [{type: accuracy, value: 42.9, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MMLU (medical)', type: super-dainiu/medagents-benchmark, config: MMLU, split: test}
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metrics: [{type: accuracy, value: 77.5, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MMLU-Pro (medical)', type: super-dainiu/medagents-benchmark, config: MMLU-Pro, split: test}
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metrics: [{type: accuracy, value: 47.3, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedExQA', type: super-dainiu/medagents-benchmark, config: MedExQA, split: test}
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metrics: [{type: accuracy, value: 73.3, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedXpertQA-R', type: super-dainiu/medagents-benchmark, config: MedXpertQA-R, split: test}
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metrics: [{type: accuracy, value: 12.1, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: MedXpertQA-U', type: super-dainiu/medagents-benchmark, config: MedXpertQA-U, split: test}
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metrics: [{type: accuracy, value: 14.6, name: Accuracy}]
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- task: {type: question-answering, name: Medical QA}
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dataset: {name: 'MedAgentsBench: AfriMedQA', type: super-dainiu/medagents-benchmark, config: AfrimedQA, split: test}
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metrics: [{type: accuracy, value: 48.9, name: Accuracy}]
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---
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# Tanit-Med-8B
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*An 8B medical reasoning model that thinks before it answers — and a model card that tells you where it doesn't.*
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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.
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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.
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**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.
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---
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## At a glance
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| | |
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|---|---|
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| **Base model** | Qwen/Qwen3-8B (dense, 8.2B params) |
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| **Precision** | BF16 |
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| **Context** | 32K native (trained at 8,192-token packed sequences) |
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| **Reasoning** | `<think>` blocks, always on by default (≈99% of responses) |
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| **Language** | English (only language evaluated) |
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| **License** | Apache-2.0 |
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| **Best at** | Multiple-choice clinical QA, USMLE-style vignettes, medical exam reasoning |
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| **Not for** | Diagnosis, treatment, dosing, triage, or anything touching a real patient |
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---
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## Results
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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.
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### Against open 8B medical models
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| Benchmark | **Tanit-Med-8B** | DeepSeek-R1-0528-Qwen3-8B | Falcon-H1R-7B | HuatuoGPT-o1-8B | MedReason-8B | Ministral-3-8B-Reasoning |
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|---|---|---|---|---|---|---|
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| MedQA | **65.0** | 44.0 | 28.9 | 29.5 | 28.0 | 27.8 |
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| PubMedQA | **68.0** | 60.2 | 60.0 | 55.2 | 55.2 | 57.2 |
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| MedMCQA | **57.8** | 42.9 | 33.6 | 35.8 | 35.0 | 34.3 |
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| MedBullets | **42.9** | 26.0 | 20.1 | 20.5 | 19.2 | 17.2 |
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| MMLU (med) | **77.5** | 50.5 | 30.3 | 26.6 | 22.8 | 22.2 |
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| MMLU-Pro (med) | **47.3** | 20.7 | 13.6 | 13.6 | 14.4 | 9.4 |
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| MedExQA | **73.3** | 49.3 | 35.1 | 28.7 | 21.7 | 21.7 |
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| MedXpertQA-R | 12.1 | 10.5 | 9.6 | 11.4 | **18.1** | 11.0 |
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| MedXpertQA-U | 14.6 | 9.7 | 10.4 | 11.5 | **16.5** | 9.7 |
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| AfriMedQA | **48.9** | 32.2 | 25.3 | 12.6 | 12.6 | 10.9 |
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| **Macro avg** | **50.7** | 34.6 | 26.7 | 24.5 | 24.4 | 22.1 |
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Tanit-Med-8B wins eight of ten. The two it loses are the two where nobody is really winning (see below).
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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.
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### Against frontier models, for scale
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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.
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| Model | FULL (9-bench avg) | HARD (9-bench avg) |
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|---|---|---|
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| DeepSeek-R1 | 73.9 | 32.5 |
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| o3-mini | 71.8 | 28.0 |
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| GPT-4o | 68.2 | 18.0 |
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| o1-mini | 67.8 | 25.3 |
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| DeepSeek-V3 | 63.1 | 12.2 |
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| Claude-3.5-Sonnet | 61.8 | 12.3 |
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| Llama-3.3-70B | 61.8 | 12.4 |
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| QwQ-32B | 61.0 | 17.1 |
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| GPT-4o-mini | 57.8 | 10.8 |
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| Claude-3.5-Haiku | 55.0 | 12.0 |
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| **Tanit-Med-8B** | **50.9** | **20.4** |
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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.
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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.
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---
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## How to read the hard splits (please read this)
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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.
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Three things follow, and we'd rather say them ourselves than have someone say them in the community tab:
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**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:
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| Model | HARD macro avg |
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|---|---|
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| Falcon-H1R-7B | 21.5 |
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| MedReason-8B | 21.2 |
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| HuatuoGPT-o1-8B | 20.7 |
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| **Tanit-Med-8B** | **20.3** |
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| Ministral-3-8B-Reasoning | 19.6 |
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| DeepSeek-R1-0528-Qwen3-8B | 17.4 |
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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.
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**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.
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**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.
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---
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## Where this model fails
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- **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.
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- **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.
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- **Hard splits: no better than the field.** See above.
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- **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.
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- **English only.** AfriMedQA is English-language. We have run no non-English evaluation.
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- **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*).
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### A note we owe you
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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.
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---
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## Training
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Four stages, full fine-tune (no LoRA), ZeRO-3 / FSDP, bf16.
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| Phase | Method | Data | Purpose |
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|---|---|---|---|
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| 1 | SFT | medical-o1-reasoning (Huatuo-o1) + Medical-R1-Distill + MedReason | Broad medical foundation, clinical language |
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| 2 | SFT | m23k + II-Medical-RL + ChatDoctor-RL + MedReason | MCQ robustness, multi-step reasoning structure |
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| 3 | DPO | FineMed-DPO (32.9K pairs) | Preference alignment; fewer confidently-wrong answers |
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| 4 | SFT | MedReason + Huatuo-o1, outputs rewritten to ≤6 reasoning bullets + final answer line | Concise, extractable chain-of-thought |
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### Hyperparameters
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|---|---|
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| Optimizer | AdamW, β = (0.9, 0.95), ε = 1e-8, weight decay 0.1 |
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| Grad clip | 1.0 |
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| Max sequence length | 8,192, packing on |
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| Attention | FlashAttention |
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| Global batch | ~1,048,576 tokens/step (≈128 × 8,192 seqs), grad-accum to match |
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| Schedule | 2% warmup, cosine decay to 10% of peak |
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| Peak LR — phase 1 | 2.0e-5 |
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| Peak LR — phase 2 | 1.0e-5 |
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| Peak LR — phase 3 (DPO) | 5.0e-7 |
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| Peak LR — phase 4 | 5.0e-6 |
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### What each phase actually bought
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Measured on our internal eval harness, macro-averaged across all ten standard splits:
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- **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.
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- **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.
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- **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.
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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.
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "TanitAI/Tanit-Med-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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messages = [
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{"role": "system", "content": "You are Tanit, an expert medical AI assistant."},
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{"role": "user", "content": (
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"A 45-year-old presents with acute monoarthritis of the first MTP joint.\n"
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"What is the most likely diagnosis?\n\n"
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"A. Gout\nB. Pseudogout\nC. Septic arthritis\nD. Rheumatoid arthritis"
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)},
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]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95, top_k=20)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
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```
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**Serving with vLLM:**
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```bash
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vllm serve TanitAI/Tanit-Med-8B --max-model-len 32768 --reasoning-parser qwen3
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```
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**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.
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**Quantized:** [Tanit-Med-8B-NVFP4](https://huggingface.co/TanitAI/Tanit-Med-8B-NVFP4) runs on a single consumer GPU.
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---
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## Evaluation protocol
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Reproducing our numbers requires matching three things, in descending order of how much they'll bite you:
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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.
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2. **Prompt.** We use the MedAgentsBench zero-shot prompt verbatim — knowledgeable-medical-assistant system message, question, options, "reply with the answer index only."
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3. **Decoding.** Greedy. Single sample. Reasoning budget 4,096 tokens, generation cap 16,384.
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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.
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---
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## The collection
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| Model | Stage | Use it if |
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|---|---|---|
|
||||
| [**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.
|
||||
28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"</think>": 151668,
|
||||
"</tool_call>": 151658,
|
||||
"</tool_response>": 151666,
|
||||
"<think>": 151667,
|
||||
"<tool_call>": 151657,
|
||||
"<tool_response>": 151665,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
89
chat_template.jinja
Normal file
89
chat_template.jinja
Normal file
@@ -0,0 +1,89 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
72
config.json
Normal file
72
config.json
Normal file
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:31c8ef7d87fe00afbf81fabe07e09f3e6cafe902ae7cc327bb97cd358c529eaf
|
||||
size 4902257696
|
||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fc3c660a3f4690b16e15eb5c1a28baf90614e778ae6aee30557f38c573760a6c
|
||||
size 4915960368
|
||||
3
model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2d4b13fd1ef8ac33f27f106f998a9bc324d018b52aa3eb569c7415f806b7403f
|
||||
size 4983068496
|
||||
3
model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3586bf84c6e07efddb1385335c36e978c705119e7ede5e8a5b77dab6dffd0328
|
||||
size 1580230264
|
||||
407
model.safetensors.index.json
Normal file
407
model.safetensors.index.json
Normal file
@@ -0,0 +1,407 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 8190735360,
|
||||
"total_size": 16381470720
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00004-of-00004.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.q_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.k_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.q_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.self_attn.k_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
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||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"is_local": true,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user