初始化项目，由ModelHub XC社区提供模型
Model: InternScience/Agents-K1 Source: Original Platform
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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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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags:
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- information-extraction
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- named-entity-recognition
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- relation-extraction
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- grpo
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- reinforcement-learning
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- qwen3
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- scientific-text
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- biomedical
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---
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# Agents-K1
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**Knowledge extraction model in Agents-K1** is a 4B-parameter language model fine-tuned from
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[`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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with **GRPO** (Group Relative Policy Optimization) on the information-extraction
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corpus, targeting **Named Entity Recognition (NER)** and **Relation Extraction (RE)**
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in English scientific and general-domain text.
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The model produces structured JSON extractions with explicit step-by-step
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reasoning, enabling its use as a building block in downstream knowledge-graph
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construction, citation linking, and multi-hop QA pipelines.
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## Highlights
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- **+3.3 absolute F1** averaged over 10 NER/RE benchmarks vs. the
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Qwen3-4B-Instruct base model, with **gains on every dataset evaluated**
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(including held-out CrossNER domains).
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- Trained with rule-based rewards (format + JSON validity + entity/relation F1),
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no human preference data required.
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- Outputs follow a strict `<think>…</think><answer>…</answer>` schema, making
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reasoning auditable and JSON parsing reliable.
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## Intended use
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Designed as an extraction backbone for:
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- Scientific-literature mining (entities/relations in biomedicine, chemistry,
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CS, etc.)
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- Knowledge-graph construction
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- Pre-processing for retrieval / multi-hop QA systems
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Not intended for general-purpose chat — it has been specialized for structured
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extraction.
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## Usage
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The model uses the same chat template as Qwen3-4B-Instruct and expects a
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schema-driven user prompt. The reply will contain a `<think>` block followed by
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an `<answer>` block with a JSON object.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "InternScience/Agents-K1"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
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system = (
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"You are an expert in information extraction. Given a task instruction "
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"with schema definitions and input text, extract the required information.\n\n"
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"You should think step by step about the extraction task, then provide "
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"your answer in JSON format.\n\n"
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"Format your response as:\n"
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"<think>\nYour step-by-step reasoning...\n</think>\n"
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"<answer>\nYour JSON extraction result here\n</answer>"
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)
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user = (
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"You are an expert in named entity recognition. Please extract entities "
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"that match the schema definition from the input. Return an empty list if "
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"the entity type does not exist. Please respond in the format of a JSON "
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"dictionary.\n\n"
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'Entity types to extract: ["person", "organization", "location"]\n\n'
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"Input text: Marie Curie worked at the University of Paris.\n\n"
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"Please think step by step and respond in the following format:\n"
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"<think>\nYour reasoning process...\n</think>\n"
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"<answer>\nYour JSON extraction result\n</answer>"
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)
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messages = [{"role": "system", "content": system},
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{"role": "user", "content": user}]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
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return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=512, do_sample=False)
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print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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For RE, replace the user template with `Relation types to extract: [...]`
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and a relation-extraction instruction; the output schema is a JSON dict mapping
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relation types to lists of `{head, tail}` pairs.
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## Training data
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Training data comes from **IEPile**, restricted to:
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- English NER and RE tasks
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- 22 source datasets, mixing scientific (SciERC, GENIA_NER, BC5CDR, BC2GM,
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BC4CHEMD, AnatEM, NCBI) and general-domain (CoNLL2003, conll04, FabNER,
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MultiNERD, NYT11, kbp37, …) corpora
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| Split | Size | Notes |
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|-----------:|-------:|-------|
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| Train | 14,400 | 90/10 split, seed=42; each source capped to balance the mix |
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| Validation | 1,600 | |
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70% of samples have non-empty gold labels; 30% are empty-label cases (to prevent
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the model from defaulting to non-empty outputs).
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## Training procedure
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- **Algorithm:** GRPO (PPO without a critic), implemented in
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[veRL](https://github.com/volcengine/verl).
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- **Reward** ∈ \[0, 1\]:
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- format reward: `0.1 · 𝟙[has <think>] + 0.1 · 𝟙[has <answer>]`
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- JSON validity: `0.1 · 𝟙[valid JSON dict]` (or `0.05` for non-dict valid JSON)
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- task F1: `0.7 · F1(pred, gold)` — entity-set F1 for NER, triple-set F1 for RE
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## Evaluation
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Reported numbers are micro-F1 on each benchmark's official test split, using
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the same prompt template as training. Gains are **base → Agents-K1 (GRPO)**.
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| Dataset | Task | n | Base F1 | Agent-K1 F1 | Δ |
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|---------------------------------|:----:|------:|--------:|--------------:|------:|
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| CoNLL2003 | NER | 3,184 | 0.6547 | **0.7007** | +0.046 |
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| NCBI-Disease | NER | 937 | 0.6737 | **0.7340** | +0.060 |
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| BC5CDR | NER | 4,788 | 0.7126 | **0.7494** | +0.037 |
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| CrossNER — AI *(held-out)* | NER | 430 | 0.4862 | **0.5400** | +0.054 |
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| CrossNER — Literature *(held)* | NER | 416 | 0.5462 | **0.5736** | +0.027 |
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| CrossNER — Music *(held)* | NER | 457 | 0.5791 | **0.6050** | +0.026 |
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| CrossNER — Politics *(held)* | NER | 650 | 0.6611 | **0.6855** | +0.024 |
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| CrossNER — Science *(held)* | NER | 532 | 0.5928 | **0.6132** | +0.020 |
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| SciERC | NER | 397 | 0.1166 | **0.1270** | +0.010 |
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| conll04 | RE | 287 | 0.2933 | **0.3181** | +0.025 |
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| **Average** | | | 0.5317 | **0.5647** | **+0.033** |
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All 10/10 benchmarks improve, including the 5 CrossNER domains that are
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**not** in the training mix — evidence of generalization rather than mere
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fitting to in-distribution sources.
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## Limitations
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- **Schema-driven prompting required.** Free-form questions will likely
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return malformed JSON; always supply explicit entity / relation type lists.
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## License
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Released under the **Apache-2.0** license, following the upstream
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[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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license. Users must also comply with the licenses of the IEPile component
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datasets when using this model in derivative works.
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added_tokens.json
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{
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"</think>": 151668,
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"<|vision_end|>": 151653,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"max_position_embeddings": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 36,
|
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"num_key_value_heads": 8,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
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"rope_theta": 5000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.52.4",
|
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"use_cache": true,
|
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"use_sliding_window": false,
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"vocab_size": 151936
|
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}
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generation_config.json
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generation_config.json
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{
|
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
|
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"pad_token_id": 151643,
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"temperature": 0.7,
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"top_k": 20,
|
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"top_p": 0.8,
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"transformers_version": "4.52.4"
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}
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merges.txt
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model.safetensors.index.json
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
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|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"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
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1010000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"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