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freeze: Think 2ep merged endpoint + LoRA adapter for modrill/think-src-o7b-20260908

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+ "schema": "q4b-think-b-2ep-merge-receipt/1",
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+ "arm": "O7B-THINK-A3B",
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+ "checkpoint": "/workspace/math_think_a3b2507_20260906/train_v3_eot_20260908/runs/O7B-THINK-A3B/train/checkpoint-update-00000987-tokens-000064632872",
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: allenai/Olmo-3-1025-7B
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+ library_name: transformers
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+ tags:
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+ - math
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+ - sft
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+ - lora
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+ - think
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+ - task-vector
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+ - iclr2027
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+ ---
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+
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+ # think-src-o7b-20260908
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+
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+ Public freeze of Math Think **2ep endpoint** for ICLR 2027 task-vector work.
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+ Not a chatbot. **Endpoint is the score.** Do not promote 1ep / 1.5ep milestones.
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+
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+ `run_id=math_six_arms_train_v3_eot_20260908`. Sibling of `modrill/nothink-src-*-20260908`; does not overwrite those repos.
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+
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+ ## Score (Exact-240)
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+
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+ AIME24+25 × seeds 42–45, EvalScope reviews, n=240. Authority field `evalscope_reviews.correct`.
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+
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+ | Model | Official /240 | cap |
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+ |---|---:|---:|
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+ | This 2ep endpoint | **51** | 124 |
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+ | Same-run Think Base | 34 | |
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+
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+ Think mode; stop ids [100257, 100265]; O7B eval uses OLMO3-VLLM-PATCH-v1 / vLLM 0.27.1. Same-run Think Base is think_vllm027 34/240 (first think Base attempt failed).
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+
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+ θ_0 is `allenai/Olmo-3-1025-7B` rev `996971efdc504b81f0a6caf73a6c92f976254b9c`.
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+
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+ ## Recipe
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+
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+ OpenR1 11750 rows × 2ep (unique problems 5875), LoRA r64/α128, lr 1e-4, TPU 65536, seed 42, EOT (Qwen tail `151643` / O7B tail `100257`), B-rows both sides [100257].
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+
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+ ## Identity
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+
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+ | Field | Value |
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+ |---|---|
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+ | Arm | `O7B-THINK-A3B` |
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+ | Updates / tokens | 987 / 64,632,872 |
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+ | Merged `model.safetensors` sha256 | `9f75574aed1be033f277a0384b97f4edef3f8ac85401c6781e59c1d46737d526` |
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+ | Endpoint adapter sha256 | `9da5899df6a1d6d7cfb9e412085eed9a85693bd0fd848645b176e34deed0d8e2` |
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+
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+ Root of merged weights is this repo. Endpoint LoRA is in `adapter/`. `MERGE_RECEIPT.json` is the merge audit. `trainer_state` is not uploaded.
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+
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+ ## Load
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ m = AutoModelForCausalLM.from_pretrained("modrill/think-src-o7b-20260908", torch_dtype="bfloat16", device_map="auto")
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+ tok = AutoTokenizer.from_pretrained("modrill/think-src-o7b-20260908")
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+ ```
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