初始化项目，由ModelHub XC社区提供模型
Model: Pclanglais/POntAvignon-4b Source: Original Platform
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---
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license: apache-2.0
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language:
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- fr
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- en
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base_model: Qwen/Qwen3-4B
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tags:
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- linked-art
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- performing-arts
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- ontology
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- json-ld
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- theater
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- festival-avignon
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- digital-humanities
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datasets:
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- PleIAs/linked-art-avignon
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pipeline_tag: text-generation
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---
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# POntAvignon-4b
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A 4B reasoning model that annotates French theater programmes against the [Linked Art](https://linked.art/) performing-arts ontology extension. Given raw programme markdown from the Festival d'Avignon (1947–present), it extracts structured JSON-LD entities with chain-of-thought reasoning.
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POntAvignon-4b is based on Qwen but relies on the SYNTH-syntax from Pleias' Baguettotron.
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## Model Details
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| | |
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|---|---|
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| **Base model** | [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
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| **Parameters** | 4B |
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| **Training** | Full SFT, 3 epochs, bf16 |
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| **Context** | 16k tokens |
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| **Output** | `<think>` reasoning + Linked Art JSON-LD |
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| **Train loss** | 0.360 |
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| **Token accuracy** | 96.6% |
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| **Valid JSON rate** | 97% (on held-out test set) |
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## Entity Types
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The model extracts 7 entity types from a single programme, each as a separate query:
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| Entity | Linked Art type | Description |
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|---|---|---|
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| **A** | `PropositionalObject` | The abstract creative Work — title, creator, BnF role, adaptation links |
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| **B_meta** | `Activity` | Production metadata — title, venue, dates, genre, festival link |
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| **B_cast** | `Activity.produced_by` | All performers — actors, dancers, musicians with BnF roles and characters |
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| **B_crew** | `Activity.produced_by` | Creative/technical staff — director, lighting, costumes, set design with BnF roles |
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| **C** | `Activity` | Single Performance — one specific date/time, venue, parent production link |
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| **Festival** | `Activity` | Overall Event — festival edition (e.g. Festival d'Avignon 1996) |
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| **Text** | `LinguisticObject` | Source literary work — the play or text being adapted/performed |
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## Reasoning
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The model uses `<think>` tags to produce SYNTH-style dense reasoning traces before outputting JSON-LD. The reasoning:
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- Names the task explicitly and stays focused on it
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- Engages with document structure, era, and typographic conventions
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- Works through French theatrical vocabulary and BnF role mapping
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- Resolves ontological boundaries (Work vs Production vs Performance)
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- Uses confidence markers: ● sure, ◐ probable, ○ guess, ⚠ risk
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## Usage
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### With vLLM (recommended)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="PleIAs/linked-art-qwen3-4b", dtype="bfloat16", max_model_len=4096)
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tokenizer = llm.get_tokenizer()
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programme = """____ PAGE 1 ____
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FESTIVAL D'AVIGNON
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COUR D'HONNEUR DU PALAIS DES PAPES
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7, 8, 12, 15, 18, 19 juillet à 22 h
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# Médée
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de Sénèque
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Mise en scène Jacques Lassalle
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"""
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messages = [
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{"role": "system", "content": "You annotate French theater programmes from the Festival d'Avignon against the Linked Art performing-arts ontology. Extract structured JSON-LD entities from programme markdown."},
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{"role": "user", "content": f"Extract the Work entity (A) from this theater programme. Output valid Linked Art JSON-LD for the Work as a PropositionalObject, including title, creator with BnF role, source attribution, and any adaptation/influence links.\n\nSource: Medee_FDA1996.md\n\n---\n\n{programme}"},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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prompt += "<think>\n"
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output = llm.generate([prompt], SamplingParams(max_tokens=2048, temperature=0.7, top_p=0.9))
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print(output[0].outputs[0].text)
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```
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### Demo notebook
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A [Colab notebook](demo/linked_art_demo.ipynb) is available that runs all 7 entity types on a demo programme with a tabbed display. Runs on a free T4 GPU.
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## Training Data
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The model was trained on 12,507 samples derived from ~1,400 Festival d'Avignon programmes (1971–2022), spanning three source collections:
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- **BnF fonds** — digitized paper programmes (1971–2002)
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- **CommAvignon** — born-digital programmes from festival communications (2007–2022)
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- **SiteAvignon** — programmes scraped from the festival website (2018)
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Each sample pairs a raw programme markdown with a Linked Art JSON-LD entity and a SYNTH-style reasoning trace. Reasoning traces were generated by Claude Sonnet (3,110 samples) and then scaled to the full dataset using a Gemma 12B backreasoning model trained on the Sonnet traces.
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## Ontology
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The model targets the [Linked Art Performing Arts extension](https://linked.art/) (v0.9), developed in collaboration with the ERC "From Stage to Data" project (Clarisse Bardiot). Key features:
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- **BnF role vocabulary** — French-language controlled terms from the Bibliothèque nationale de France, mapped to person attributions
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- **Deterministic IDs** — content-derived identifiers (e.g. `W-MEDE-JACQ-1996-a3f1`) for deduplication
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- **Source attribution** — every extracted fact links back to the programme document as primary source
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## Limitations
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- **Specialized corpus**: trained exclusively on Festival d'Avignon programmes. Other festivals or theatrical traditions may need additional training data.
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- **French-centric**: programme text, role vocabulary, and typographic conventions are French. Non-French programmes are out of scope.
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- **Large cast/crew**: productions with 30+ team members may produce truncated B_cast/B_crew outputs near the context limit.
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- **Date inference**: when the programme doesn't state the year explicitly, the model infers it from the filename (e.g. `FDA1996`). Without a filename, year extraction may fail.
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- **Reasoning traces** improve accuracy and provide explainability but may contain intermediate errors that are corrected in the final JSON-LD output.
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added_tokens.json
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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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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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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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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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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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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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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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"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": 40960,
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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": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.52.4",
|
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"use_cache": false,
|
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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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|
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|
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],
|
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"pad_token_id": 151643,
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"temperature": 0.6,
|
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"top_k": 20,
|
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"top_p": 0.95,
|
||||
"transformers_version": "4.52.4"
|
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}
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
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|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
||||
size 11422654
|
||||
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": 131072,
|
||||
"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