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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+tags:
+- scientific-discovery
+- hypothesis-generation
+- inspiration-retrieval
+- multi-task
+datasets:
+- ZonglinY/TOMATO-Star-SFT-Data-R1D-32B
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# MOOSE-Star-R1D-7B Model Card
+
+## Overview
+
+**MOOSE-Star-R1D-7B** is a 7B parameter multi-task language model fine-tuned for both **inspiration retrieval** and **hypothesis composition** in scientific discovery workflows. It matches the IR performance of the single-task model ([MOOSE-Star-IR-R1D-7B](https://huggingface.co/ZonglinY/MOOSE-Star-IR-R1D-7B)) while significantly outperforming the single-task HC model ([MOOSE-Star-HC-R1D-7B](https://huggingface.co/ZonglinY/MOOSE-Star-HC-R1D-7B)), all in a single unified model.
+
+- **Paper**: [MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier](https://arxiv.org/abs/2603.03756) (arXiv:2603.03756)
+- **Base Model**: [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
+- **License**: Apache 2.0
+- **Code**: [ZonglinY/MOOSE-Star](https://github.com/ZonglinY/MOOSE-Star)
+
+## Model Description
+
+| Parameter | Value |
+|-----------|-------|
+| **Base Model** | DeepSeek-R1-Distill-Qwen-7B |
+| **Training Method** | Full-parameter SFT (ZeRO-3) |
+| **Training Data** | TOMATO-Star-SFT-Data-R1D-32B: IR split (150,218 samples) + HC split with 1x bounded (114,548 samples) |
+| **Chat Template** | deepseekr1 |
+| **Cutoff Length** | 16384 |
+| **Learning Rate** | 1e-5 |
+| **Epochs** | 1 |
+| **Batch Size** | 128 |
+
+## Task 1: Inspiration Retrieval (IR)
+
+The model selects the most relevant **cross-paper inspiration** from 15 candidates (A-O) that includes 1 correct inspiration and 14 hard negatives.
+
+### IR Prompt Format (Simplified Overview)
+
+The full prompt template is constructed via `instruction_prompts()` in the code examples below. The general structure is:
+
+```
+[Task instruction preamble]
+
+## Context
+
+**Research Question:**
+{research_question}
+
+**Background Survey (existing methods for THIS task):**
+{background_survey}
+
+**Previous Hypothesis (if any):**
+{previous_hypothesis_or_none}
+
+## Candidate Inspiration Papers
+
+### Candidate [A]
+**Title:** {title_A}
+**Abstract:** {abstract_A}
+
+... (15 candidates total, A through O)
+
+## Output Format
+
+<think>
+[reasoning process]
+</think>
+
+**Selected ID starts:** [X] **Selected ID ends**
+
+**Selection Reason starts:** [reason] **Selection Reason ends**
+```
+
+### IR Usage
+
+#### Option A: SGLang Deployment (Recommended)
+
+```python
+import sys
+sys.path.insert(0, "./Inference")
+from ir_probability_extractor import IRProbabilityExtractor
+
+extractor = IRProbabilityExtractor(base_urls=["http://localhost:1235/v1"])
+result = extractor.get_selection_probabilities(
+    research_question="Your research question",
+    background_survey="Your background survey",
+    candidates=[
+        {"title": "Candidate A title", "abstract": "Candidate A abstract"},
+        {"title": "Candidate B title", "abstract": "Candidate B abstract"},
+        # ... up to 15 candidates (labeled A-O)
+    ],
+)
+print(f"Selected: [{result.selected_label}]")
+print(f"Probabilities: {result.probabilities}")
+```
+
+#### Option B: Direct HuggingFace Inference
+
+```python
+import sys
+sys.path.insert(0, "./utils")
+from prompt_store import instruction_prompts
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import re
+
+model_name = "ZonglinY/MOOSE-Star-R1D-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")
+
+p = instruction_prompts("inspiration_retrieval_with_reasoning_with_alphabetical_candidates")
+
+candidates = [{"title": "...", "abstract": "..."}, ...]
+candidates_text = "".join(
+    f"### Candidate [{chr(ord('A') + i)}]\n**Title:** {c['title']}\n**Abstract:** {c['abstract']}\n\n"
+    for i, c in enumerate(candidates)
+)
+
+research_question = "Your research question"
+background_survey = "Your background survey"
+prompt = (p[0] + research_question
+        + p[1] + background_survey
+        + p[2] + "No previous hypothesis."
+        + p[3] + candidates_text
+        + p[4])
+
+messages = [{"role": "user", "content": prompt}]
+formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+formatted += "<\uff5cAssistant\uff5c>"
+
+inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.9, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+
+match = re.search(r"\*\*Selected ID starts:\*\*\s*\[(\w)\]\s*\*\*Selected ID ends\*\*", response)
+if match:
+    print(f"Selected: [{match.group(1)}]")
+```
+
+## Task 2: Hypothesis Composition (HC)
+
+The model generates **delta hypotheses** from inspiration papers. Given a research question, background survey, and new inspiration paper, it outputs structured hypothesis components.
+
+### HC Prompt Format (Simplified Overview)
+
+The full prompt template is constructed via `instruction_prompts()` in the code examples below. The general structure is:
+
+```
+[Task instruction preamble]
+
+## Information Provided
+
+**Research Question**:
+{research_question}
+
+**Background Survey**:
+{background_survey}
+
+**Previous Hypothesis**:
+{previous_hypothesis_or_none}
+
+**New Inspiration Paper Title**:
+{inspiration_title}
+
+**New Inspiration Paper Abstract**:
+{inspiration_abstract}
+
+## Your Response
+
+<think>
+[reasoning process]
+</think>
+
+Inspiration: [Key concept]
+- Motivation (WHY): [Why this addresses a gap]
+- Mechanism (HOW IT WORKS): [How the concept works]
+- Methodology (HOW IT'S INTEGRATED): [Implementation steps]
+```
+
+### HC Usage
+
+```python
+import sys
+sys.path.insert(0, "./utils")
+from prompt_store import instruction_prompts
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "ZonglinY/MOOSE-Star-R1D-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")
+
+p = instruction_prompts("prepare_HC_sft_data_to_go_comprehensive_v2_delta")
+
+research_question = "Your research question here"
+background_survey = "Your background survey here"
+inspiration_title = "Inspiration paper title"
+inspiration_abstract = "Inspiration paper abstract"
+
+prompt = (p[0] + research_question
+        + p[1] + background_survey
+        + p[2] + "No previous hypothesis."
+        + p[3] + inspiration_title
+        + p[4] + inspiration_abstract
+        + p[5])
+
+messages = [{"role": "user", "content": prompt}]
+formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+formatted += "<\uff5cAssistant\uff5c>"
+
+inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.9, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Evaluation Results
+
+### Inspiration Retrieval (Table 1)
+
+| Model | Accuracy |
+|-------|----------|
+| Random Selection | 6.70% |
+| R1-Distilled-Qwen-7B (base) | 28.42% |
+| MS-IR-7B (single-task) | 54.37% |
+| **MS-7B (this model)** | **54.34%** |
+
+### Hypothesis Composition - Normal (Table 2)
+
+Rubric-based evaluation with ground-truth inspirations (Judge: GPT-4o):
+
+| Model | Total | Mot | Mec | Met | Length |
+|-------|-------|-----|-----|-----|--------|
+| R1-Distilled-Qwen-7B (base) | 4.05 | 1.96 | 1.30 | 0.80 | 231.02 |
+| MS-HC-7B (single-task) | 4.68 | 2.13 | 1.46 | 1.09 | 204.12 |
+| MS-HC-7B w/ 1x bounded | 4.74 | 2.16 | 1.48 | 1.10 | 203.84 |
+| **MS-7B (this model)** | **5.02** | **2.22** | **1.59** | **1.20** | 208.98 |
+
+### Hypothesis Composition - Bounded (Table 3)
+
+Performance under varying levels of inspiration noise (Judge: GPT-4o):
+
+| Model | Easy Total | Medium Total | Hard Total |
+|-------|-----------|-------------|-----------|
+| R1-Distilled-Qwen-7B (base) | 2.72 | 2.27 | 2.00 |
+| MS-HC-7B w/ 2x bounded | 3.18 | 2.74 | 2.56 |
+| **MS-7B (this model)** | **3.37** | **2.86** | **2.78** |
+
+## Key Findings
+
+- **IR performance preserved**: Multi-task training maintains full IR accuracy (54.34% vs 54.37% single-task)
+- **HC significantly improved**: Multi-task HC outperforms all single-task variants, including those with bounded composition augmentation
+- **Robust under noise**: Largest improvements on Hard bounded composition, suggesting IR reasoning skills transfer to HC
+
+## Citation
+
+```bibtex
+@article{yang2025moosestar,
+  title={MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier},
+  author={Yang, Zonglin and Bing, Lidong},
+  journal={arXiv preprint arXiv:2603.03756},
+  year={2025}
+}
+```

all_results.json

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+{
+    "epoch": 1.0,
+    "total_flos": 2898754626256896.0,
+    "train_loss": 0.5475362745847836,
+    "train_runtime": 128435.5344,
+    "train_samples_per_second": 2.061,
+    "train_steps_per_second": 0.016
+}

chat_template.jinja

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+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\n'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151646,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

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