51 lines
1.7 KiB
Markdown
51 lines
1.7 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- distillation
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- math
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- s1
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- openthoughts
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- oracle-internal-trace
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- qwen2.5-7b-instruct
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- full-finetune
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datasets:
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- Chia-Mu-Lab/ot-ideal-q3_32b-clean
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language:
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- en
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library_name: transformers
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---
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# Oracle internal-trace distillation · Qwen3-32B teacher -> Qwen2.5-7B-Instruct student
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This repo holds the **oracle internal-trace** student for the 32B row of the REP
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paper's main distillation comparison table (`tab:maincomparison`).
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The student is `Qwen/Qwen2.5-7B-Instruct` full-fine-tuned (s1 recipe) on
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**Qwen3-32B's own hidden reasoning traces** (`<think>` internal chain of thought,
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no attack) sampled on 10k OpenThoughts prompts
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(`Chia-Mu-Lab/ot-ideal-q3_32b-clean`, r1+boxed target).
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The weights at the repo root are **checkpoint-2015 (epoch 5)**, the checkpoint
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reported in the paper.
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## Reported metrics (this run, `kod-s1-ot-ideal-q3_32b-clean-v1`)
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| ckpt | MATH500 | AIME24 | AIME25 | JEE (strict, full) | LCB pass@1 |
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|---|---|---|---|---|---|
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| base | 70.9 | 10.0 | 4.4 | 29.2 | 18.3 |
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| step-01613 (ep4) | 67.6 | 12.2 | 13.3 | 32.9 | 17.6 |
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| **step-02015 (ep5)** | **70.0** | **16.7** | **15.6** | 37.5 | 15.8 |
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The paper table reports the **JEE-Math subset** (46.4 / 49.3 strict/partial) rather
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than the full-JEE strict number (37.5) shown here; all other cells match the paper
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row exactly (MATH500 70.0 / AIME24 16.7 / AIME25 15.6 / LCB 15.8).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Chia-Mu-Lab/qwen25-7b-ot-ideal-q3_32b-clean", torch_dtype="bfloat16")
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tok = AutoTokenizer.from_pretrained("Chia-Mu-Lab/qwen25-7b-ot-ideal-q3_32b-clean")
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```
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