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Model: groc/recursive-sat-qwen2.5-1.5b Source: Original Platform
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README.md
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README.md
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---
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- paper-model
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- recursive-reasoning
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- sat
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- qwen2.5
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- transformers
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datasets:
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- LLM4Code/SATBench
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license: mit
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---
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# recursive-sat-qwen2.5-1.5b
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This is a paper model: the `REC-3` release artifact from a paper-aligned replication of recursive SAT reasoning at 1.5B scale.
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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.
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## What This Model Is
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- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
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- Release artifact: `results/runs/REC-3/published_model`
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- Training run: `REC-3`
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- Seed: `303`
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- Config: `configs/rec_seed303.yaml`
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- Dataset source: `LLM4Code/SATBench`
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- Task: SAT / UNSAT classification via recursive trace supervision
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## Why REC-3
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`REC-1` and `REC-3` tie on mean accuracy, but `REC-3` is the cleaner release candidate on end-to-end behavior:
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- Mean accuracy: `45.33%`
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- Easy: `39.0%`
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- Medium: `54.0%`
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- Hard: `43.0%`
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- Parse failure rate: `7.0%`
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- Valid trace rate: `99.0%`
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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%`).
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## Important Caveat
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This is a paper model, not a claim of robust general recursive reasoning.
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The underlying paper draft treats the result as a qualified replication:
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- recursive SFT improves end-to-end SATBench accuracy over raw direct prompting
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- the strongest gain is on medium-difficulty SAT instances
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- absolute performance remains far below the 3B source-paper result
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- recursion behavior is still shallow overall
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Use this release as a research artifact tied to the experiment, metrics, and discussion in the paper repo.
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## Training Summary
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- Objective: `recursive_sft`
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- Train examples: `74,827`
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- Validation examples: `619`
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- Global step: `46,770`
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- Best checkpoint: `checkpoint-9354`
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- Accelerator used for the main run: `cuda`
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## Evaluation Summary
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Main held-out evaluation uses `100` examples each from SATBench easy, medium, and hard buckets.
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Baseline vs released model:
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- Base direct prompt mean accuracy: `37.33%`
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- `REC-3` mean accuracy: `45.33%`
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- Absolute gain: `+8.0 points`
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- Base parse failure rate: `28.67%`
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- `REC-3` parse failure rate: `7.0%`
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## Prompt Format
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The model was trained on recursive traces using:
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- `<call> ... </call>` for subproblem decomposition
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- `<return> ... </return>` for compact returned answers
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It is best treated as a specialized research model for this protocolized SAT setting.
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## Files In This Release
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- `model.safetensors`
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- `config.json`
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- `generation_config.json`
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- `tokenizer.json`
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- `tokenizer_config.json`
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- `chat_template.jinja`
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- `export_metadata.json`
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## Intended Use
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- paper artifact release
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- replication reference
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- SAT recursive-trace evaluation
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- qualitative inspection of recursive protocol behavior
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## Out Of Scope
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- production reasoning system
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- general mathematical reasoning benchmark model
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- safety-critical use
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- claims beyond the SATBench replication setting
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54
chat_template.jinja
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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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) 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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{{- message.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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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0",
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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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13
export_metadata.json
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export_metadata.json
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{
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"source_checkpoint": "results/runs/REC-3/checkpoints/checkpoint-9354",
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"output_dir": "results/runs/REC-3/published_model",
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"copied_files": [
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"chat_template.jinja",
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"config.json",
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"generation_config.json",
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"model.safetensors",
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"tokenizer.json",
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"tokenizer_config.json"
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],
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"run_dir": "results/runs/REC-3"
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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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"do_sample": true,
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"eos_token_id": [
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151645,
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.3.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b6e056dfe59bd3cd54b7d29e88baf9a7ac784902f0339a7005bdcb68ab97fbdb
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size 3087467144
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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