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Model: Pclanglais/POntAvignon-4b
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- fr
+- en
+base_model: Qwen/Qwen3-4B
+tags:
+- linked-art
+- performing-arts
+- ontology
+- json-ld
+- theater
+- festival-avignon
+- digital-humanities
+datasets:
+- PleIAs/linked-art-avignon
+pipeline_tag: text-generation
+---
+
+# POntAvignon-4b
+
+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.
+
+POntAvignon-4b is based on Qwen but relies on the SYNTH-syntax from Pleias' Baguettotron.
+
+## Model Details
+
+| | |
+|---|---|
+| **Base model** | [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
+| **Parameters** | 4B |
+| **Training** | Full SFT, 3 epochs, bf16 |
+| **Context** | 16k tokens |
+| **Output** | `<think>` reasoning + Linked Art JSON-LD |
+| **Train loss** | 0.360 |
+| **Token accuracy** | 96.6% |
+| **Valid JSON rate** | 97% (on held-out test set) |
+
+## Entity Types
+
+The model extracts 7 entity types from a single programme, each as a separate query:
+
+| Entity | Linked Art type | Description |
+|---|---|---|
+| **A** | `PropositionalObject` | The abstract creative Work — title, creator, BnF role, adaptation links |
+| **B_meta** | `Activity` | Production metadata — title, venue, dates, genre, festival link |
+| **B_cast** | `Activity.produced_by` | All performers — actors, dancers, musicians with BnF roles and characters |
+| **B_crew** | `Activity.produced_by` | Creative/technical staff — director, lighting, costumes, set design with BnF roles |
+| **C** | `Activity` | Single Performance — one specific date/time, venue, parent production link |
+| **Festival** | `Activity` | Overall Event — festival edition (e.g. Festival d'Avignon 1996) |
+| **Text** | `LinguisticObject` | Source literary work — the play or text being adapted/performed |
+
+## Reasoning
+
+The model uses `<think>` tags to produce SYNTH-style dense reasoning traces before outputting JSON-LD. The reasoning:
+
+- Names the task explicitly and stays focused on it
+- Engages with document structure, era, and typographic conventions
+- Works through French theatrical vocabulary and BnF role mapping
+- Resolves ontological boundaries (Work vs Production vs Performance)
+- Uses confidence markers: ● sure, ◐ probable, ○ guess, ⚠ risk
+
+## Usage
+
+### With vLLM (recommended)
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="PleIAs/linked-art-qwen3-4b", dtype="bfloat16", max_model_len=4096)
+tokenizer = llm.get_tokenizer()
+
+programme = """____ PAGE 1 ____
+
+FESTIVAL D'AVIGNON
+COUR D'HONNEUR DU PALAIS DES PAPES
+7, 8, 12, 15, 18, 19 juillet à 22 h
+
+# Médée
+
+de Sénèque
+Mise en scène Jacques Lassalle
+"""
+
+messages = [
+    {"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."},
+    {"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}"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+prompt += "<think>\n"
+
+output = llm.generate([prompt], SamplingParams(max_tokens=2048, temperature=0.7, top_p=0.9))
+print(output[0].outputs[0].text)
+```
+
+### Demo notebook
+
+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.
+
+## Training Data
+
+The model was trained on 12,507 samples derived from ~1,400 Festival d'Avignon programmes (1971–2022), spanning three source collections:
+
+- **BnF fonds** — digitized paper programmes (1971–2002)
+- **CommAvignon** — born-digital programmes from festival communications (2007–2022)
+- **SiteAvignon** — programmes scraped from the festival website (2018)
+
+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.
+
+## Ontology
+
+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:
+
+- **BnF role vocabulary** — French-language controlled terms from the Bibliothèque nationale de France, mapped to person attributions
+- **Deterministic IDs** — content-derived identifiers (e.g. `W-MEDE-JACQ-1996-a3f1`) for deduplication
+- **Source attribution** — every extracted fact links back to the programme document as primary source
+
+## Limitations
+
+- **Specialized corpus**: trained exclusively on Festival d'Avignon programmes. Other festivals or theatrical traditions may need additional training data.
+- **French-centric**: programme text, role vocabulary, and typographic conventions are French. Non-French programmes are out of scope.
+- **Large cast/crew**: productions with 30+ team members may produce truncated B_cast/B_crew outputs near the context limit.
+- **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.
+- **Reasoning traces** improve accuracy and provide explainability but may contain intermediate errors that are corrected in the final JSON-LD output.

added_tokens.json

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+  "<think>": 151667,
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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.4"
+}

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