commit 5dc6e8b53ffd6bfa54ccc8b5c1ee290e41f6aafd Author: ModelHub XC Date: Mon Jul 20 21:51:09 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: TanitAI/Tanit-Med-8B-DPO Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..2bd25e6 --- /dev/null +++ b/README.md @@ -0,0 +1,305 @@ +--- +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** | `` 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 `` 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. \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -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 XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|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('') and message.content.endswith('')) %} + {%- 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 '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-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\n' + reasoning_content.strip('\n') + '\n\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 %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- 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 %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..a8e7a96 --- /dev/null +++ b/config.json @@ -0,0 +1,71 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "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" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.2.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 151936 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..1701c94 --- /dev/null +++ b/generation_config.json @@ -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": "5.2.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..d187b69 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8269042a54067af8cc9302b0b167d7ddea5947926cf17ecc6e13e6ff95c5387 +size 16381517208 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..c7afbed --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506 +size 11422650 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..0bd5574 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,15 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": true, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "left", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} \ No newline at end of file