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@@ -33,3 +33,4 @@ saved_model/**/* 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

README.md

@@ -0,0 +1,184 @@
+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+- information-extraction
+- named-entity-recognition
+- relation-extraction
+- grpo
+- reinforcement-learning
+- qwen3
+- scientific-text
+- biomedical
+---
+
+# Scholar-R1
+
+**Scholar-R1** is a 4B-parameter language model fine-tuned from
+[`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+with **GRPO** (Group Relative Policy Optimization) on the information-extraction
+corpus, targeting **Named Entity Recognition (NER)** and **Relation Extraction (RE)**
+in English scientific and general-domain text.
+
+The model produces structured JSON extractions with explicit step-by-step
+reasoning, enabling its use as a building block in downstream knowledge-graph
+construction, citation linking, and multi-hop QA pipelines.
+
+## Highlights
+
+- **+3.3 absolute F1** averaged over 10 NER/RE benchmarks vs. the
+  Qwen3-4B-Instruct base model, with **gains on every dataset evaluated**
+  (including held-out CrossNER domains).
+- Trained with rule-based rewards (format + JSON validity + entity/relation F1),
+  no human preference data required.
+- Outputs follow a strict `<think>…</think><answer>…</answer>` schema, making
+  reasoning auditable and JSON parsing reliable.
+
+## Intended use
+
+Designed as an extraction backbone for:
+
+- Scientific-literature mining (entities/relations in biomedicine, chemistry,
+  CS, etc.)
+- Knowledge-graph construction
+- Pre-processing for retrieval / multi-hop QA systems
+
+Not intended for general-purpose chat — it has been specialized for structured
+extraction.
+
+## Usage
+
+The model uses the same chat template as Qwen3-4B-Instruct and expects a
+schema-driven user prompt. The reply will contain a `<think>` block followed by
+an `<answer>` block with a JSON object.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Leon-AI/Scholar-R1"
+tok   = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
+
+system = (
+    "You are an expert in information extraction. Given a task instruction "
+    "with schema definitions and input text, extract the required information.\n\n"
+    "You should think step by step about the extraction task, then provide "
+    "your answer in JSON format.\n\n"
+    "Format your response as:\n"
+    "<think>\nYour step-by-step reasoning...\n</think>\n"
+    "<answer>\nYour JSON extraction result here\n</answer>"
+)
+
+user = (
+    "You are an expert in named entity recognition. Please extract entities "
+    "that match the schema definition from the input. Return an empty list if "
+    "the entity type does not exist. Please respond in the format of a JSON "
+    "dictionary.\n\n"
+    'Entity types to extract: ["person", "organization", "location"]\n\n'
+    "Input text: Marie Curie worked at the University of Paris.\n\n"
+    "Please think step by step and respond in the following format:\n"
+    "<think>\nYour reasoning process...\n</think>\n"
+    "<answer>\nYour JSON extraction result\n</answer>"
+)
+
+messages = [{"role": "system", "content": system},
+            {"role": "user",   "content": user}]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
+                                 return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=512, do_sample=False)
+print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+For RE, replace the user template with `Relation types to extract: [...]`
+and a relation-extraction instruction; the output schema is a JSON dict mapping
+relation types to lists of `{head, tail}` pairs.
+
+## Training data
+
+Training data comes from **IEPile**, restricted to:
+
+- English NER and RE tasks
+- 22 source datasets, mixing scientific (SciERC, GENIA_NER, BC5CDR, BC2GM,
+  BC4CHEMD, AnatEM, NCBI) and general-domain (CoNLL2003, conll04, FabNER,
+  MultiNERD, NYT11, kbp37, …) corpora
+
+| Split      | Size   | Notes |
+|-----------:|-------:|-------|
+| Train      | 14,400 | 90/10 split, seed=42; each source capped to balance the mix |
+| Validation | 1,600  | |
+
+70% of samples have non-empty gold labels; 30% are empty-label cases (to prevent
+the model from defaulting to non-empty outputs).
+
+## Training procedure
+
+- **Algorithm:** GRPO (PPO without a critic), implemented in
+  [veRL](https://github.com/volcengine/verl).
+- **Reward** ∈ \[0, 1\]:
+  - format reward: `0.1 · 𝟙[has <think>] + 0.1 · 𝟙[has <answer>]`
+  - JSON validity: `0.1 · 𝟙[valid JSON dict]` (or `0.05` for non-dict valid JSON)
+  - task F1: `0.7 · F1(pred, gold)` — entity-set F1 for NER, triple-set F1 for RE
+
+### Hyper-parameters
+
+| | |
+|---|---|
+| Base model | Qwen3-4B-Instruct |
+| Learning rate | 1e-6 |
+| KL loss coeff. (low-var KL) | 0.01 |
+| Train batch size (prompts) | 128 |
+| PPO mini / micro batch | 64 / 4 per GPU |
+| Rollouts per prompt (`n`) | 6 |
+| Sampling | T=0.7, top-p=0.9 |
+| Max prompt / response | 2048 / 2048 |
+| Epochs | 1 (112 optimizer steps) |
+| Rollout engine | vLLM (gpu_memory_utilization=0.6) |
+| Distributed | FSDP via Ray, 8 × H200 GPUs |
+| Wall-clock | ~1 h 18 min |
+
+## Evaluation
+
+Reported numbers are micro-F1 on each benchmark's official test split, using
+the same prompt template as training. Gains are **base → Scholar-R1 (GRPO)**.
+
+| Dataset                         | Task | n     | Base F1 | Scholar-R1 F1 | Δ |
+|---------------------------------|:----:|------:|--------:|--------------:|------:|
+| CoNLL2003                       | NER  | 3,184 | 0.6547 | **0.7007** | +0.046 |
+| NCBI-Disease                    | NER  |   937 | 0.6737 | **0.7340** | +0.060 |
+| BC5CDR                          | NER  | 4,788 | 0.7126 | **0.7494** | +0.037 |
+| CrossNER — AI *(held-out)*      | NER  |   430 | 0.4862 | **0.5400** | +0.054 |
+| CrossNER — Literature *(held)*  | NER  |   416 | 0.5462 | **0.5736** | +0.027 |
+| CrossNER — Music *(held)*       | NER  |   457 | 0.5791 | **0.6050** | +0.026 |
+| CrossNER — Politics *(held)*    | NER  |   650 | 0.6611 | **0.6855** | +0.024 |
+| CrossNER — Science *(held)*     | NER  |   532 | 0.5928 | **0.6132** | +0.020 |
+| SciERC                          | NER  |   397 | 0.1166 | **0.1270** | +0.010 |
+| conll04                         | RE   |   287 | 0.2933 | **0.3181** | +0.025 |
+| **Average**                     |      |       | 0.5317 | **0.5647** | **+0.033** |
+
+All 10/10 benchmarks improve, including the 5 CrossNER domains that are
+**not** in the training mix — evidence of generalization rather than mere
+fitting to in-distribution sources.
+
+## Limitations
+
+- **English only.** No Chinese / multilingual training data was used.
+- **Schema-driven prompting required.** Free-form questions will likely
+  return malformed JSON; always supply explicit entity / relation type lists.
+- **F1 is still modest on hard scientific RE / fine-grained NER**
+  (e.g. SciERC). Treat outputs as candidates for downstream verification, not
+  ground truth.
+- Inherits all biases and failure modes of the underlying Qwen3-4B-Instruct
+  base model.
+
+## License
+
+Released under the **Apache-2.0** license, following the upstream
+[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+license. Users must also comply with the licenses of the IEPile component
+datasets when using this model in derivative works.
+
+

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

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 %}
+{%- 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" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- 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' }}
+{%- endif %}

config.json

@@ -0,0 +1,30 @@
+{
+  "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": 262144,
+  "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": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

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