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Model: groc/recursive-sat-qwen2.5-1.5b
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- paper-model
+- recursive-reasoning
+- sat
+- qwen2.5
+- transformers
+datasets:
+- LLM4Code/SATBench
+license: mit
+---
+
+# recursive-sat-qwen2.5-1.5b
+
+This is a paper model: the `REC-3` release artifact from a paper-aligned replication of recursive SAT reasoning at 1.5B scale.
+
+It is a supervised fine-tune of `Qwen/Qwen2.5-1.5B-Instruct` trained on recursive SAT traces derived from SATBench with explicit `<call>` / `<return>` structure. The goal is research replication and analysis, not general-purpose production use.
+
+## What This Model Is
+
+- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
+- Release artifact: `results/runs/REC-3/published_model`
+- Training run: `REC-3`
+- Seed: `303`
+- Config: `configs/rec_seed303.yaml`
+- Dataset source: `LLM4Code/SATBench`
+- Task: SAT / UNSAT classification via recursive trace supervision
+
+## Why REC-3
+
+`REC-1` and `REC-3` tie on mean accuracy, but `REC-3` is the cleaner release candidate on end-to-end behavior:
+
+- Mean accuracy: `45.33%`
+- Easy: `39.0%`
+- Medium: `54.0%`
+- Hard: `43.0%`
+- Parse failure rate: `7.0%`
+- Valid trace rate: `99.0%`
+
+Compared with `REC-1`, `REC-3` keeps the same mean accuracy while reducing parse failure (`7.0%` vs `8.33%`), improving hard accuracy (`43.0%` vs `42.0%`), and slightly improving valid trace rate (`99.0%` vs `98.33%`).
+
+## Important Caveat
+
+This is a paper model, not a claim of robust general recursive reasoning.
+
+The underlying paper draft treats the result as a qualified replication:
+
+- recursive SFT improves end-to-end SATBench accuracy over raw direct prompting
+- the strongest gain is on medium-difficulty SAT instances
+- absolute performance remains far below the 3B source-paper result
+- recursion behavior is still shallow overall
+
+Use this release as a research artifact tied to the experiment, metrics, and discussion in the paper repo.
+
+## Training Summary
+
+- Objective: `recursive_sft`
+- Train examples: `74,827`
+- Validation examples: `619`
+- Global step: `46,770`
+- Best checkpoint: `checkpoint-9354`
+- Accelerator used for the main run: `cuda`
+
+## Evaluation Summary
+
+Main held-out evaluation uses `100` examples each from SATBench easy, medium, and hard buckets.
+
+Baseline vs released model:
+
+- Base direct prompt mean accuracy: `37.33%`
+- `REC-3` mean accuracy: `45.33%`
+- Absolute gain: `+8.0 points`
+- Base parse failure rate: `28.67%`
+- `REC-3` parse failure rate: `7.0%`
+
+## Prompt Format
+
+The model was trained on recursive traces using:
+
+- `<call> ... </call>` for subproblem decomposition
+- `<return> ... </return>` for compact returned answers
+
+It is best treated as a specialized research model for this protocolized SAT setting.
+
+## Files In This Release
+
+- `model.safetensors`
+- `config.json`
+- `generation_config.json`
+- `tokenizer.json`
+- `tokenizer_config.json`
+- `chat_template.jinja`
+- `export_metadata.json`
+
+## Intended Use
+
+- paper artifact release
+- replication reference
+- SAT recursive-trace evaluation
+- qualitative inspection of recursive protocol behavior
+
+## Out Of Scope
+
+- production reasoning system
+- general mathematical reasoning benchmark model
+- safety-critical use
+- claims beyond the SATBench replication setting

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "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": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

export_metadata.json

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+{
+  "source_checkpoint": "results/runs/REC-3/checkpoints/checkpoint-9354",
+  "output_dir": "results/runs/REC-3/published_model",
+  "copied_files": [
+    "chat_template.jinja",
+    "config.json",
+    "generation_config.json",
+    "model.safetensors",
+    "tokenizer.json",
+    "tokenizer_config.json"
+  ],
+  "run_dir": "results/runs/REC-3"
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.3.0"
+}

model.safetensors

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+size 3087467144

tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}