commit d1020dd6c04fe73a46af5b2a3663276392e4a26d Author: ModelHub XC Date: Mon Jun 1 12:09:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: domyn/Domyn-Small-v1.0 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..0c9d0be --- /dev/null +++ b/README.md @@ -0,0 +1,395 @@ +--- +library_name: transformers +license: other +license_name: mit +license_link: https://www.domyn.com/legal/software-licenses/domyn-small +pipeline_tag: text-generation +language: +- en +- it +- es +- fr +- de +tags: +- reasoning +- dual-mode +- thinking +- tool-calling +- agentic +- multilingual +--- + +

+ + + Domyn + +

+ +# Domyn Small + +Domyn Small is a 10B-parameter open-weight reasoning model designed for resource-constrained, agentic, and fine-tunable deployments. It pairs a dual-mode (thinking on/off) inference design with grouped-query attention, a native 32k context window (extensible to 131k via YaRN), and tool calling. On reasoning benchmarks it reaches accuracy comparable to leading 7–10B reasoning peers while spending roughly **2–4× fewer reasoning tokens** — placing it on a favourable accuracy/cost Pareto frontier for production inference and downstream fine-tuning. + +Fine-tune Domyn Small to your domain to unlock its real power and to retain full ownership and control over the resulting model. + +## Highlights + +- **Token-efficient reasoning** — ~32% of Qwen3.5-9B's reasoning-token budget and ~35% of OLMo-3-7B-Think's at comparable accuracy on several reasoning tasks ([Token Efficiency](#token-efficiency)). +- **Dual-mode inference** — `thinking on` for deep multi-step reasoning, `thinking off` for fast, compact output. Toggleable from the system prompt or the API. +- **Tool calling** — first-class function calling via `` XML tags, with a chat template that handles tool injection automatically. Strong BFCL V3 single-turn results (75.9 Non-Live / 68.3 Live) at ~280 mean tokens per problem. +- **Expandable context** — 32,768 tokens natively, extensible to 131,072 (128k) via YaRN at inference time. +- **Multilingual** — 50+ languages with explicit coverage; optimised for English and the Tier-A European set (Italian, Spanish, French, German). + +## Model Overview + +- **Developed by**: Domyn S.p.A. +- **Version**: 1.0 +- **Released and last updated on**: May 2026 +- **Input / Output**: Text-only / Text-only +- **Model size**: ~10B parameters +- **Attention**: Grouped-Query Attention (48 query heads, 8 KV heads) +- **Tokenizer**: 256,000-token SentencePiece BPE vocabulary +- **Native context**: 32,768 tokens +- **Extended context**: 131,072 tokens (YaRN, 4× at inference time) +- **Language(s)**: 50+ languages; optimised for English and the Tier-A European set (Italian, Spanish, French, German) +- **Base model**: Initialised from Italia 10B and continually pre-trained on 503B tokens +- **Knowledge cut-off date**: September 2024 (based on pre-training dataset cut-off) +- **License**: MIT + +A full architecture and training-recipe specification is available in the Domyn Small technical report. + +## Quickstart + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http:///v1", + api_key="none", +) + +response = client.chat.completions.create( + model="domyn/Domyn-Small-v1.0", + messages=[ + {"role": "system", "content": "You are Domyn Small, a helpful assistant."}, + {"role": "user", "content": "What is the capital of Italy?"}, + ], +) +print(response.choices[0].message.content) +``` + +## Deployment + +> We recommend **vLLM ≥ 0.9.2** for all the snippets below. + +### vLLM — Basic + +```bash +vllm serve domyn/Domyn-Small-v1.0 \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + --max-model-len 32768 \ + --max-num-seqs 256 \ + --gpu-memory-utilization 0.9 +``` + +### vLLM — With Reasoning Parsing + +To have vLLM automatically extract the model's `` blocks and expose them as a structured `reasoning_content` field, add a reasoning-parser flag. Which flag to use depends on your vLLM version. + +**vLLM < 0.21.0** — Domyn Small emits the same `…` format as OLMo 3, and earlier vLLM releases work with the OLMo 3 parser directly: + +```bash +vllm serve domyn/Domyn-Small-v1.0 \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + --max-model-len 32768 \ + --max-num-seqs 256 \ + --gpu-memory-utilization 0.9 \ + --reasoning-parser olmo3 +``` + +**vLLM ≥ 0.21.0 (recommended)** — use the Domyn-specific parser plugin shipped with this checkpoint (`reasoning_parser_plugin.py`). It reads the per-request `enable_thinking` flag (or the `thinking on` / `thinking off` system-prompt directive) and routes streamed output to the correct lane (`reasoning` vs `content`) for both modes. + +```bash +vllm serve domyn/Domyn-Small-v1.0 \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + --max-model-len 32768 \ + --max-num-seqs 256 \ + --gpu-memory-utilization 0.9 \ + --reasoning-parser think_block \ + --reasoning-parser-plugin /path/to/reasoning_parser_plugin.py +``` + +Replace `/path/to/` with the actual path to the plugin file bundled with the checkpoint. The parser name `think_block` is the registration string declared inside the plugin and must match exactly. + +### vLLM — Extended Context with YaRN + +> YaRN scaling may impact model quality on inputs shorter than 32k. Enable it only when you actually need contexts beyond the native 32,768-token window. + +```bash +vllm serve domyn/Domyn-Small-v1.0 \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + # vLLM < 0.12.0 + --rope-scaling '{"rope_type": "yarn", "factor": 4, "original_max_position_embeddings": 32768}' \ + # vLLM >= 0.12.0 + --hf-overrides '{"rope_parameters": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768}}' \ + --max-model-len 131072 +``` + +### vLLM — With Tool Calling + +Tool calling requires three extra flags and the bundled plugin files (shipped with this model checkpoint): + +```bash +vllm serve domyn/Domyn-Small-v1.0 \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + --max-model-len 32768 \ + --max-num-seqs 256 \ + --gpu-memory-utilization 0.9 \ + --enable-auto-tool-choice \ + --tool-call-parser xml_tool_call \ + --tool-parser-plugin /path/to/tool_parser_plugin.py \ + --chat-template /path/to/chat_template.jinja +``` + +Replace `/path/to/` with the actual paths to the files bundled with the checkpoint. + +### Transformers + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_id = "domyn/Domyn-Small-v1.0" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, dtype=torch.bfloat16, device_map="auto" +) + +messages = [ + { + "role": "system", + "content": "You are Domyn Small, a helpful assistant. thinking on", + }, + {"role": "user", "content": "Solve step by step: what is 17 × 24?"}, +] + +inputs = tokenizer.apply_chat_template( + messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" +) + +outputs = model.generate(**inputs, max_new_tokens=128) +print( + tokenizer.decode( + outputs[0][inputs["input_ids"].shape[-1] :], skip_special_tokens=True + ) +) +``` + +## Thinking Mode + +Domyn Small supports chain-of-thought reasoning controlled by a directive in the system prompt: + +- **Thinking off** (default): omit the directive, or include `thinking off`. +- **Thinking on**: append `thinking on` to your system prompt. + +```python +messages = [ + {"role": "system", "content": "You are Domyn Small, a helpful assistant. thinking on"}, + {"role": "user", "content": "Solve step by step: what is 17 × 24?"}, +] +``` + +When thinking is on, the model emits its reasoning inside `…` tags before the final answer. + +Alternatively, you can control reasoning by passing `enable_thinking` as an extra request parameter. This has the same effect as adding `thinking on` to the system prompt. Because `enable_thinking` is not part of the standard OpenAI schema, it must be forwarded to vLLM via the OpenAI client's `extra_body` field: + +```python +response = client.chat.completions.create( + model="domyn/Domyn-Small-v1.0", + messages=[ + {"role": "user", "content": "Solve step by step: what is 17 × 24?"}, + ], + extra_body={"chat_template_kwargs": {"enable_thinking": True}}, +) +``` + +### Recommended Sampling Parameters + +| Mode | temperature | top_p | top_k | min_p | +|------|-------------|-------|-------|-------| +| Thinking **off** | 0.1 | 0.95 | 50 | 0.1 | +| Thinking **on** | 0.6 | 0.90 | 25 | 0.1 | + +> Do **not** use greedy decoding in thinking mode — it degrades reasoning quality and may cause repetition. + +## Tool Calling + +### How It Works + +Domyn Small has been trained to call functions using `` XML tags. The chat template handles tool formatting automatically: **you do not need to write tool instructions in your system prompt.** + +When you pass a `tools` list to the API, the chat template prepends a structured tool-instruction block to the system prompt automatically. Your own system message (for persona or context) is appended after that block. The final rendered system block looks like: + +``` + + +thinking on/off +``` + +This means your system prompt stays clean — just describe the assistant's persona or context. + +### Python Example + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http:///v1", + api_key="none", +) + +tools = [ + { + "type": "function", + "function": { + "name": "get_weather_forecast", + "description": "Get the weather forecast for a location on a given date.", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City name"}, + "date": {"type": "string", "description": "Date in YYYY-MM-DD format"}, + }, + "required": ["location", "date"], + }, + }, + } +] + +response = client.chat.completions.create( + model="domyn/Domyn-Small-v1.0", + messages=[ + {"role": "system", "content": "You are Domyn Small, a helpful assistant."}, + {"role": "user", "content": "What's the weather like in Rome today?"}, + ], + tools=tools, + temperature=0.0, +) + +choice = response.choices[0] +if choice.finish_reason == "tool_calls": + for tc in choice.message.tool_calls: + print(f"Function: {tc.function.name}") + print(f"Arguments: {tc.function.arguments}") +``` + +## Evaluations + +Domyn Small is evaluated against four peer models in the 7–10B parameter class: **Qwen3.5-9B**, **OLMo-3-7B-Think**, **Llama-3.1-Nemotron-Nano-8B-v1**, and **Ministral-3-8B-Reasoning**. All scores are in thinking-on mode at 32,768-token sequence length (RULER extends to 131,072 via YaRN). + +| Category | Benchmark | Domyn Small | Qwen3.5-9B | OLMo-3-7B-Think | Nemotron-Nano | Ministral-3-8B | +|---|---|---|---|---|---|---| +| Reasoning | MATH-500 | **93.2** | 97.4 | 96.8 | 95.4 | 89.2 | +| | AIME 2025 (avg@48) | **35.7** | 90.0 | 70.4 | 51.2 | 32.3 | +| | GPQA-Diamond | **50.0** | 82.7 | 50.8 | 42.4 | 43.9 | +| Code | HumanEval (pass@1) | **96.3** | 93.3 | 95.7 | 91.5 | 86.6 | +| | LiveCodeBench (pass@1)| **55.0** | 86.2 | 74.8 | 67.2 | 46.0 | +| | MBPP (pass@1) | **76.8** | 76.8 | 86.6 | 77.6 | 66.6 | +| General Knowledge | MMLU | **80.3** | 84.6 | 75.2 | 56.0 | 75.3 | +| | MMLU-PRO | **67.7** | 84.4 | 64.0 | 28.8 | 62.0 | +| Instruction | IFEval (strict) | **79.9** | 91.0 | 83.7 | 70.4 | 62.5 | +| Multilingual | MGSM | **73.1** | 88.9 | 64.0 | 19.9 | 75.5 | +| Long context | RULER 32k | **59.5** | 89.8 | 69.8 | 34.0 | 88.7 | +| | RULER 64k | **29.6** | 87.9 | 17.2 | 18.7 | 85.9 | +| Tool calling | BFCL V3 Non-Live | **75.9** | 78.1 | 61.1 | 63.3 | — | +| | BFCL V3 Live | **68.3** | 78.4 | 66.9 | 40.2 | — | +| | BFCL V3 Multi-Turn | **7.0** | 50.6 | 2.1 | 0.1 | — | + +Domyn Small attains its single-turn BFCL results at ~280 mean tokens per problem against ~590 for Qwen3.5-9B and ~2,429 for OLMo-3-7B-Think — the best accuracy-per-token tool-calling profile in the peer set among models that fully engage the reasoning path. Ministral-3-8B is excluded from the BFCL comparison: during evaluation it consistently failed to close the `[/THINK]` reasoning delimiter, making its structured outputs unparseable by the benchmark. + +## Token Efficiency + +The table below compares mean generated tokens per problem (thinking on, lower is better) against the strongest accuracy peer in the set, Qwen3.5-9B. Grand means weight each benchmark by its problem count. + +| Category | Benchmark | Domyn Small | Qwen3.5-9B | +|---|---|---|---| +| Reasoning | MATH-500 | **2,261** | 7,614 | +| | AIME 2025 | **5,190** | 18,668 | +| | GPQA-Diamond | **3,396** | 8,976 | +| | **Grand mean** | **2,690** | 8,440 | +| Code | HumanEval | **1,884** | 1,144 | +| | LCB-Gen | **5,010** | 12,739 | +| | MBPP | **2,420** | 1,927 | +| | **Grand mean** | **3,312** | 5,870 | +| General Knowledge | MMLU | **1,236** | 3,262 | +| | MMLU-PRO | **2,947** | 4,666 | +| | **Grand mean** | **2,026** | 3,910 | +| Instruction | IFEval | **775** | 3,874 | +| Multilingual | MGSM | **796** | 3,140 | + +On the reasoning suite Domyn Small produces approximately **32% of Qwen3.5-9B's** token budget — a 3.1× saving at comparable accuracy on several benchmarks. + +## Dual-Mode Comparison (Thinking ON vs. OFF) + +Effect of the reasoning toggle on Domyn Small. Same evaluation harness; thinking-on AIME 2025 is reported as avg@48, other thinking-on entries are single-pass. + +| Benchmark | Thinking off | Thinking on | Δ | +|---|---|---|---| +| MATH-500 | 91.4 | 93.2 | +1.8 | +| AIME 2025 | 31.0 | 35.7 | +4.7 | +| LiveCodeBench | 33.8 | 55.0 | +21.2 | +| MBPP | 54.6 | 76.8 | +22.2 | +| HumanEval | 69.5 | 96.3 | +26.8 | +| GPQA-Diamond | 40.0 | 50.0 | +10.0 | +| MMLU-PRO | 60.0 | 67.7 | +7.7 | +| MGSM | 59.7 | 73.1 | +13.4 | +| IFEval (prompt strict) | 78.6 | 79.9 | +1.3 | + +The toggle helps most when the bottleneck is multi-step search or program synthesis (code, science reasoning, multilingual math); it helps least when the bottleneck is recall or format compliance. + +## Intended Uses + +### Primary Use Cases + +Domyn Small is intended for commercial and research use in multiple languages: + +- Regulated-industry use cases in **resource-constrained environments** that need reduced computational cost and faster response times in production. +- **Fine-tuning to any desired domain knowledge** across industries, to equip the model with the context and expertise needed to excel on real-world applications. +- **Agentic applications**, especially agents that need to solve coding and mathematical problems and perform sequential, tool-calling tasks. + +### Out-of-Scope Use Cases + +Domyn Small is not specifically designed or evaluated for all downstream purposes. As with any language model, developers should carefully evaluate accuracy, safety, and fairness before applying it to specific downstream scenarios, particularly high-risk ones. Developers should also ensure compliance with all applicable laws and regulations (including, but not limited to, privacy and trade compliance) relevant to their use case. + + +## EU AI Act Compliance + +Domyn Small is released as a general-purpose AI (GPAI) model under the EU AI Act. Article 53 transparency obligations are discharged via this model card, the Domyn Small technical report (architecture, training data composition, training stages, evaluations, and known limitations end-to-end), and the MIT-licensed open-weights release. The training-data summary required by Article 53(1)(d) is provided as a companion artefact to the model release. + +To uphold data-subject rights and comply with the AI Act and EU copyright framework, we operate an opt-out procedure for rights holders. Anyone who believes their copyrighted material was inadvertently included in our training corpora can contact `copyright@domyn.com`, and we will exclude the affected data from subsequent model iterations. + +## Citation + +If you find this work valuable, please consider citing it: + +```bibtex +@misc{domynsmall2026, + title = {Domyn Small}, + author = {Domyn S.p.A.}, + year = {2026}, + eprint = {TBD}, + note = {Technical report, forthcoming}, +} +``` + +## Contacts + +- For general inquiries about Domyn Small, please contact: `models@domyn.com` +- For copyright-related complaints, please contact: `copyright@domyn.com` + +*Affected rightsholders and their authorised representatives, including collective management organisations, may submit sufficiently precise and adequately substantiated complaints electronically concerning any non-compliance with our commitments under the Copyright Chapter of the GPAI Code of Practice. We commit to handling such complaints diligently, impartially, and within a reasonable timeframe, except in cases where the complaint is manifestly unfounded or has already been addressed. This mechanism complements, but does not limit, the available legal measures, remedies, and sanctions under Union and national copyright law.* diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..8f042bd --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,123 @@ +{#- This template extends the base model template to support tool calling. + Tools are injected into the system prompt using XML tags. + Tool calls use XML tags, tool responses use tags. + + Handles all combinations: + 1. tools + user system message → single system block: tool instructions + user content + 2. tools + NO system message → single synthetic system block with tool instructions + 3. no tools + user system message → single system block with user content + 4. no tools + no system message → minimal default system block + + Thinking mode: if any system message contains "thinking on", all system + blocks end with "thinking on" and the generation prompt emits an open . + Otherwise "thinking off" is used and is immediately closed. + + "thinking on" / "thinking off" is stripped from user system content and + re-appended as the final line of the system block to keep it consistent. +-#} +{%- set loop_messages = messages %} +{%- set ns = namespace(thinking=false, has_tools=false, has_system=false, system_emitted=false) %} + +{#- ===== Method A: Check kwarg ===== -#} +{%- if enable_thinking is defined and enable_thinking %} +{%- set ns.thinking = true %} +{%- endif %} + +{#- ===== PASS 1: Scan messages to detect thinking mode and system presence ===== -#} +{%- for message in loop_messages %} +{%- if message['role'] == 'system' %} +{%- set ns.has_system = true %} +{%- if 'thinking on' in message['content'] %} +{%- set ns.thinking = true %} +{%- endif %} +{%- endif %} +{%- endfor %} + +{#- ===== Build tool instruction block if tools provided ===== -#} +{%- if tools is defined and tools is not none and tools | length > 0 %} +{%- set ns.has_tools = true %} +{%- set tool_instruction %} +You are a function calling AI model. You are provided with function signatures within XML tags. +You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. +Here are the available tools: + +{{ tools | tojson }} + +For each function call, return a JSON object with function name and arguments within XML tags as follows: + +{"name": , "arguments": } + +{%- endset %} +{%- endif %} + +{#- ===== Thinking mode suffix — appended to every system block ===== -#} +{%- set thinking_suffix = "thinking on" if ns.thinking else "thinking off" %} + +{#- ===== SYNTHETIC SYSTEM BLOCK (only if NO system messages in conversation) ===== -#} +{%- if not ns.has_system %} +{%- if ns.has_tools %} +{{- 'System\n' }} +{{- tool_instruction + '\n\n' }} +{{- thinking_suffix + '\n' }} +{%- else %} +{{- 'System\n' }} +{{- thinking_suffix + '\n' }} +{%- endif %} +{%- set ns.system_emitted = true %} +{%- endif %} + +{#- ===== PASS 2: Render messages ===== -#} +{%- for message in loop_messages %} + +{#- ---- SYSTEM MESSAGE ---- -#} +{%- if message['role'] == 'system' %} +{#- Strip thinking directives from the content — we handle them via thinking_suffix -#} +{%- set clean_content = message['content'] | replace('thinking on', '') | replace('thinking off', '') | trim %} +{{- 'System\n' }} +{%- if ns.has_tools %} +{{- tool_instruction + '\n' }} +{%- endif %} +{%- if clean_content %} +{{- clean_content + '\n' }} +{%- endif %} +{{- thinking_suffix + '\n' }} +{%- set ns.system_emitted = true %} + +{#- ---- USER MESSAGE ---- -#} +{%- elif message['role'] == 'user' %} +{{- 'User\n' }} +{{- message['content'] + '\n' }} + +{#- ---- ASSISTANT MESSAGE ---- -#} +{%- elif message['role'] == 'assistant' %} +{%- if message.tool_calls is defined and message.tool_calls | length > 0 %} +{{- 'Assistant\n' }} +{%- for tool_call in message.tool_calls %} +{{- '\n' }} +{{- {"name": tool_call.function.name, "arguments": tool_call.function.arguments} | tojson + '\n' }} +{{- '\n' }} +{%- endfor %} +{%- else %} +{{- 'Assistant\n' }} +{{- message['content'] + '\n' }} +{%- endif %} + +{#- ---- TOOL RESPONSE MESSAGE ---- -#} +{%- elif message['role'] == 'tool' %} +{{- 'User\n' }} +{{- '\n' }} +{{- message['content'] + '\n' }} +{{- '\n' }} + +{%- endif %} +{%- endfor %} + +{#- ===== Generation prompt ===== -#} +{%- if add_generation_prompt %} +{{- 'Assistant\n' }} +{%- if ns.thinking %} +{{- '\n' }} +{%- else %} +{{- '\n\n\n' }} +{%- endif %} +{%- endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..a635c56 --- /dev/null +++ b/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "NemotronForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 2, + "dtype": "bfloat16", + "eos_token_id": 5, + "head_dim": 128, + "hidden_act": "relu2", + "hidden_size": 4096, + "initializer_range": 0.0134, + "intermediate_size": 16384, + "max_position_embeddings": 32768, + "mlp_bias": false, + "model_type": "nemotron", + "nemo_version": "0.2.0", + "norm_eps": 1e-05, + "num_attention_heads": 48, + "num_hidden_layers": 40, + "num_key_value_heads": 8, + 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The parser splits each model output on +the literal ```` marker: everything before it is reasoning, +everything after is final content. + +See :class:`ThinkBlockReasoningParser` for the streaming state machine and +how per-request thinking-on/off is discovered. +""" + +from __future__ import annotations + +from collections.abc import Iterable, Sequence +from typing import TYPE_CHECKING + +from vllm.reasoning import ReasoningParser, ReasoningParserManager + +if TYPE_CHECKING: + from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest + from vllm.entrypoints.openai.engine.protocol import DeltaMessage + from vllm.entrypoints.openai.responses.protocol import ResponsesRequest + +# Literal markers emitted by the Domyn-Small chat template. `` is +# pre-emitted by the prompt, so model output never starts with it; only `` +# actually has to be detected at runtime. +START = "" +END = "" + + +def _max_suffix_prefix(s: str, marker: str) -> str: + """Longest non-empty suffix of ``s`` that is also a prefix of ``marker``. + + Used to decide how many trailing bytes of the streaming buffer must be + held back — if those bytes could still grow into ``marker`` on the next + delta, releasing them now would fragment the marker across deltas (e.g. + emitting ````). + """ + for i in range(min(len(marker) - 1, len(s)), 0, -1): + if s.endswith(marker[:i]): + return s[-i:] + return "" + + +@ReasoningParserManager.register_module("think_block") +class ThinkBlockReasoningParser(ReasoningParser): + """Splits model output on the literal ```` marker. + + **Streaming.** Olmo3-style buffered state machine: incoming text is + accumulated in :attr:`_buffer` and only released when the marker is + either confirmed (split point reached) or ruled out (the buffer tail + can no longer be a prefix of ````). This guarantees the marker + is never fragmented across deltas. + + **Per-request lane.** The initial lane (``"reasoning"`` vs + ``"content"``) is set from the request itself: ``True`` if + ``chat_template_kwargs.enable_thinking`` (or ``.thinking``) is truthy, + or if any system message contains the literal ``"thinking on"`` + directive — mirroring the chat template's own detection. + + **Request discovery.** vLLM instantiates the parser per request from + inside ``create_chat_completion(self, request, ...)``, but does not + pass the request to the constructor. We recover it by walking the call + stack at ``__init__`` time, inspecting only each frame's *function + arguments* (so we don't accidentally match request-shaped objects in + module globals or unrelated locals). If no request is found we fall + back to ``thinking=off``, which keeps tool-call streaming working out + of the box. + """ + + def __init__(self, tokenizer, *args, **kwargs) -> None: + # Base ReasoningParser only accepts `tokenizer`; swallow any extras so + # the registration signature stays compatible across vLLM versions. + super().__init__(tokenizer) + self._buffer: str = "" + # Current lane for streaming output: "reasoning" while inside + # ..., "content" otherwise. Locked to "content" once + # `` is observed. + self._state: str = "content" + # Tracks whether we have applied per-request configuration yet — + # stack-walking covers the streaming path; `extract_reasoning` also + # configures on the first non-streaming call as a safety net. + self._configured: bool = False + + request = self._find_request_in_stack() + if request is not None: + self._configure_for_request(request) + + @staticmethod + def _looks_like_request(obj) -> bool: + """Duck-typed check for ChatCompletionRequest / ResponsesRequest. + + Avoids importing vLLM's protocol module, which differs across forks + and isn't guaranteed to be importable at plugin load time. + """ + return hasattr(obj, "messages") and ( + hasattr(obj, "chat_template_kwargs") or hasattr(obj, "stream") + ) + + @classmethod + def _find_request_in_stack(cls, max_depth: int = 12): + """Locate the in-flight request by scanning caller-frame arguments. + + Walks a bounded number of caller frames via ``sys._getframe`` / + ``frame.f_back`` and inspects only each frame's *function + arguments* — never its full locals. This matches vLLM's + ``create_chat_completion(self, request, ...)`` signature and avoids + matching request-shaped objects that happen to live in module + globals or unrelated locals (e.g. test fixtures). + + We deliberately avoid :func:`inspect.stack`, which reads source + files via ``linecache`` and builds ``FrameInfo`` objects for the + whole stack on every call — measurable overhead per request under + high concurrency, since parser construction is per-request and + runs under the GIL on the serving event loop. + """ + import sys + try: + frame = sys._getframe(1) + except Exception: + return None + depth = 0 + while frame is not None and depth < max_depth: + code = frame.f_code + n_args = code.co_argcount + code.co_kwonlyargcount + for name in code.co_varnames[:n_args]: + value = frame.f_locals.get(name) + if cls._looks_like_request(value): + return value + frame = frame.f_back + depth += 1 + return None + + def _configure_for_request(self, request) -> None: + """Set initial streaming lane from the request's thinking flag.""" + self._state = "reasoning" if self._thinking_was_enabled(request) else "content" + self._configured = True + + def _decode(self, ids: Sequence[int]) -> str: + # `skip_special_tokens=False` is required: `` may be tokenized + # as (or contain) special tokens that the default decode would strip, + # which would silently break marker detection. + try: + return self.model_tokenizer.decode(list(ids), skip_special_tokens=False) + except Exception: + return "" + + @property + def reasoning_start_str(self) -> str | None: + return START + + @property + def reasoning_end_str(self) -> str | None: + return END + + def is_reasoning_end(self, input_ids: Sequence[int]) -> bool: + return END in self._decode(input_ids) + + def is_reasoning_end_streaming( + self, input_ids: Sequence[int], delta_ids: Iterable[int] + ) -> bool: + # Decode a 64-token tail window so the marker is detected even when + # it straddles the previous-vs-delta token boundary (BPE may split + # `` across multiple tokens, especially around punctuation). + tail = list(input_ids)[-64:] + return END in self._decode(tail) + + def extract_content_ids(self, input_ids: list[int]) -> list[int]: + text = self._decode(input_ids) + idx = text.rfind(END) + if idx < 0: + return [] + try: + return self.model_tokenizer.encode( + text[idx + len(END):], add_special_tokens=False + ) + except Exception: + return [] + + def count_reasoning_tokens(self, token_ids: Sequence[int]) -> int: + text = self._decode(token_ids) + idx = text.find(END) + prefix = text if idx < 0 else text[:idx] + try: + return len(self.model_tokenizer.encode(prefix, add_special_tokens=False)) + except Exception: + return 0 + + def extract_reasoning( + self, + model_output: str, + request: "ChatCompletionRequest | ResponsesRequest", + ) -> tuple[str | None, str | None]: + """Split a full (non-streaming) output into ``(reasoning, content)``. + + Returns ``(None, content)`` when the request has thinking disabled + and the output contains no marker — the chat template pre-emits + ```` in the prompt in that case, so a marker-less + output is purely the answer. + """ + # Configure streaming state as a side effect: a fork's serving layer + # may call this before streaming starts, and we don't want the + # streaming path to fall back to the `thinking=off` default if the + # request actually had thinking enabled. + if not self._configured: + self._configure_for_request(request) + + s = model_output + if s.startswith(START): + s = s[len(START):] + if END in s: + reasoning, _, content = s.partition(END) + return (reasoning.strip("\n") or None, content.lstrip("\n") or None) + # No `` in output: only treat the text as truncated reasoning + # if we have positive evidence that thinking was enabled — otherwise + # it is the final answer. + if self._thinking_was_enabled(request): + return (s.strip("\n") or None, None) + return (None, s.lstrip("\n") or None) + + @staticmethod + def _thinking_was_enabled(request) -> bool: + """Whether ``request`` asked for reasoning to be emitted. + + Mirrors the chat template's own detection so the parser stays in + lockstep with prompt construction: enabled iff + ``chat_template_kwargs.enable_thinking`` (or ``.thinking``) is + truthy, or any system message contains the literal ``"thinking on"`` + directive (case-insensitive). + """ + kwargs = getattr(request, "chat_template_kwargs", None) or {} + if kwargs.get("enable_thinking") or kwargs.get("thinking"): + return True + messages = getattr(request, "messages", None) or [] + for m in messages: + role = m.get("role") if isinstance(m, dict) else getattr(m, "role", None) + if role != "system": + continue + content = m.get("content") if isinstance(m, dict) else getattr(m, "content", None) + if isinstance(content, str) and "thinking on" in content.lower(): + return True + return False + + def extract_reasoning_streaming( + self, + previous_text: str, + current_text: str, + delta_text: str, + previous_token_ids: Sequence[int], + current_token_ids: Sequence[int], + delta_token_ids: Sequence[int], + ) -> "DeltaMessage | None": + """Emit one ``DeltaMessage`` per delta, routed to reasoning or content. + + The marker ```` is never emitted to the client. Trailing + bytes of the buffer that *could* still grow into the marker on the + next delta are held back, so the marker is never fragmented across + deltas (e.g. ````). When the marker is observed, + pre-marker bytes go to the current lane and post-marker bytes go + to ``content``; the lane is then locked to ``content``. + """ + from vllm.entrypoints.openai.engine.protocol import DeltaMessage + + self._buffer += delta_text + + # Case 1 — marker fully present in the buffer: split and switch lane. + # The pre-marker chunk stays on the *current* lane (reasoning if we + # were inside , content otherwise); the post-marker chunk + # always goes to content; the lane is locked to content afterwards. + idx = self._buffer.find(END) + if idx >= 0: + pre = self._buffer[:idx] + post = self._buffer[idx + len(END):] + self._buffer = "" + pre_lane = self._state + self._state = "content" + if not pre and not post: + return None + fields: dict = {} + if pre: + fields[pre_lane] = pre + if post: + # `.get` covers the edge case where pre_lane is already + # "content" and both pre and post are non-empty — they get + # concatenated into a single content delta. + fields["content"] = fields.get("content", "") + post + return DeltaMessage(**fields) + + # Case 2 — no marker yet: release everything except a possible + # partial-marker tail, which we retain for the next delta. + held = _max_suffix_prefix(self._buffer, END) + safe_end = len(self._buffer) - len(held) + if safe_end == 0: + return None + chunk = self._buffer[:safe_end] + self._buffer = self._buffer[safe_end:] + return DeltaMessage(**{self._state: chunk}) diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..23f162f --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,37 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + 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appear in a single response (parallel tool calling). + +Usage: + vllm serve \ + --enable-auto-tool-choice \ + --tool-parser-plugin /absolute/path/to/tool_parser_plugin.py \ + --tool-call-parser xml_tool_call \ + --chat-template /absolute/path/to/tool_chat_template.jinja +""" + +import ast +import json +import re +import uuid +from typing import Sequence, Union + +# --------------------------------------------------------------------------- +# Import compatibility: vLLM >=0.8 moved tool_parsers to vllm.tool_parsers; +# older versions keep them under vllm.entrypoints.openai.tool_parsers. +# --------------------------------------------------------------------------- +try: + # Newer vLLM, roughly 0.15+ + from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest + from vllm.entrypoints.openai.engine.protocol import ( + DeltaFunctionCall, + DeltaMessage, + DeltaToolCall, + ExtractedToolCallInformation, + FunctionCall, + ToolCall, + ) +except ImportError: + # Older vLLM + from vllm.entrypoints.openai.protocol import ( + ChatCompletionRequest, + DeltaFunctionCall, + DeltaMessage, + DeltaToolCall, + ExtractedToolCallInformation, + FunctionCall, + ToolCall, + ) + +try: + from vllm.tool_parsers.abstract_tool_parser import ToolParser, ToolParserManager +except ImportError: + from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import ( + ToolParser, + ToolParserManager, + ) + +from vllm.logger import init_logger + +logger = init_logger(__name__) + + +def _generate_tool_call_id() -> str: + """Generate a unique tool-call ID in the format expected by OpenAI.""" + return f"call_{uuid.uuid4().hex[:24]}" + + +# --------------------------------------------------------------------------- +# Register the parser so it can be referenced via --tool-call-parser +# --------------------------------------------------------------------------- +@ToolParserManager.register_module(["xml_tool_call"]) +class XMLToolCallParser(ToolParser): + """ + Parses tool calls wrapped in ... XML tags. + + Handles both single and parallel (multiple) tool calls in one response. + Supports streaming and non-streaming extraction. + """ + + # Regex to match complete ... blocks + TOOL_CALL_RE = re.compile( + r"\s*(.*?)\s*", + re.DOTALL, + ) + + # Regex that also matches an incomplete (still-streaming) block + TOOL_CALL_OPEN_RE = re.compile( + r"\s*(.*?)(?:|$)", + re.DOTALL, + ) + + TOOL_CALL_START = "" + TOOL_CALL_END = "" + + def __init__(self, tokenizer, tools=None): + # vLLM newer versions: ToolParser.__init__(tokenizer, tools) + # vLLM older versions: ToolParser.__init__(tokenizer) + try: + super().__init__(tokenizer, tools) + except TypeError: + super().__init__(tokenizer) + self.tools = tools or [] + + # ---- streaming state ---- + self.current_tool_id: int = -1 + self.current_tool_name_sent: bool = False + self.prev_tool_call_arr: list[dict] = [] + self.streamed_args_for_tool: list[str] = [] + + # ------------------------------------------------------------------ + # Optional: adjust the request before inference + # ------------------------------------------------------------------ + @staticmethod + def _parse_tool_json(raw: str) -> dict | None: + """Parse a tool call JSON block, handling Python-style single quotes.""" + # Try standard JSON first + try: + return json.loads(raw) + except (json.JSONDecodeError, ValueError): + pass + # Fall back to ast.literal_eval for Python-style dicts with single quotes + try: + result = ast.literal_eval(raw) + if isinstance(result, dict): + return result + except (ValueError, SyntaxError): + pass + return None + + def adjust_request( + self, request: ChatCompletionRequest + ) -> ChatCompletionRequest: + return request + + # ------------------------------------------------------------------ + # NON-STREAMING extraction + # ------------------------------------------------------------------ + def extract_tool_calls( + self, + model_output: str, + request: ChatCompletionRequest, + ) -> ExtractedToolCallInformation: + """ + Parse all ... blocks from the full model + output and convert them to OpenAI ToolCall objects. + """ + + # Find all complete tool-call blocks + raw_matches = self.TOOL_CALL_RE.findall(model_output) + + if not raw_matches: + # No tool calls found — return the text as-is + return ExtractedToolCallInformation( + tools_called=False, + tool_calls=[], + content=model_output, + ) + + tool_calls: list[ToolCall] = [] + for raw_json in raw_matches: + parsed = self._parse_tool_json(raw_json) + if parsed is None: + logger.warning( + "Failed to parse tool call JSON: %s", raw_json + ) + continue + + fn_name = parsed.get("name", "") + fn_args = parsed.get("arguments", {}) + + # Ensure arguments is a JSON string (OpenAI format) + if isinstance(fn_args, dict): + fn_args_str = json.dumps(fn_args) + elif isinstance(fn_args, str): + # Model may emit arguments as a JSON string — validate and pass through + try: + json.loads(fn_args) + fn_args_str = fn_args + except (json.JSONDecodeError, ValueError): + # Try ast.literal_eval for Python-style dicts (e.g. single quotes, + # unquoted keys). If that also fails, emit an empty dict so + # downstream json.loads never sees an invalid string. + try: + recovered = ast.literal_eval(fn_args) + fn_args_str = json.dumps(recovered) if isinstance(recovered, dict) else json.dumps({}) + except (ValueError, SyntaxError): + fn_args_str = "{}" + else: + fn_args_str = str(fn_args) + + tool_calls.append( + ToolCall( + id=_generate_tool_call_id(), + type="function", + function=FunctionCall( + name=fn_name, + arguments=fn_args_str, + ), + ) + ) + + # Strip tool-call blocks from content to get any surrounding text + remaining_content = self.TOOL_CALL_RE.sub("", model_output).strip() + + return ExtractedToolCallInformation( + tools_called=True, + tool_calls=tool_calls, + content=remaining_content if remaining_content else None, + ) + + # ------------------------------------------------------------------ + # STREAMING extraction + # ------------------------------------------------------------------ + def extract_tool_calls_streaming( + self, + previous_text: str, + current_text: str, + delta_text: str, + previous_token_ids: Sequence[int], + current_token_ids: Sequence[int], + delta_token_ids: Sequence[int], + request: ChatCompletionRequest, + ) -> Union[DeltaMessage, None]: + """ + Incrementally parse tool calls from the streaming token output. + + Strategy: + - Before seeing , stream tokens as regular content. + - Once is detected, buffer until . + - On , emit the complete tool call delta. + - Support multiple sequential tool calls. + """ + + # If we haven't seen a tool_call opening tag yet, pass through as + # regular content (unless the start tag is partially forming). + if self.TOOL_CALL_START not in current_text: + # Check if the current text ends with a partial match of the + # start tag — if so, hold back to avoid emitting partial tags. + for i in range(1, len(self.TOOL_CALL_START)): + if current_text.endswith(self.TOOL_CALL_START[:i]): + # Possibly forming the start tag — hold delta + return None + return DeltaMessage(content=delta_text) + + # ---- We are inside or past a block ---- + + # Find all *complete* tool call blocks so far + complete_matches = self.TOOL_CALL_RE.findall(current_text) + num_complete = len(complete_matches) + + # Determine how many we've already streamed + num_already_sent = len(self.prev_tool_call_arr) + + if num_complete > num_already_sent: + # A new tool call just completed — emit it + new_raw = complete_matches[num_already_sent] + parsed = self._parse_tool_json(new_raw) + if parsed is None: + logger.warning( + "Streaming: failed to parse tool call JSON: %s", + new_raw, + ) + return None + + fn_name = parsed.get("name", "") + fn_args = parsed.get("arguments", {}) + if isinstance(fn_args, dict): + fn_args_str = json.dumps(fn_args) + elif isinstance(fn_args, str): + try: + json.loads(fn_args) + fn_args_str = fn_args + except (json.JSONDecodeError, ValueError): + try: + recovered = ast.literal_eval(fn_args) + fn_args_str = json.dumps(recovered) if isinstance(recovered, dict) else json.dumps({}) + except (ValueError, SyntaxError): + fn_args_str = "{}" + else: + fn_args_str = str(fn_args) + + self.current_tool_id += 1 + self.prev_tool_call_arr.append(parsed) + self.streamed_args_for_tool.append(fn_args_str) + self.current_tool_name_sent = True + + return DeltaMessage( + tool_calls=[ + DeltaToolCall( + index=self.current_tool_id, + id=_generate_tool_call_id(), + type="function", + function=DeltaFunctionCall( + name=fn_name, + arguments=fn_args_str, + ), + ) + ] + ) + + # If we're currently inside an incomplete tool call block, + # don't emit anything — wait for it to complete. + # Check if there's an open without a matching close + open_count = current_text.count(self.TOOL_CALL_START) + close_count = current_text.count(self.TOOL_CALL_END) + if open_count > close_count: + # Still buffering inside a tool call + return None + + # If we're past all tool call blocks, stream remaining content + # (unlikely for most models but handles edge cases) + return None \ No newline at end of file