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CITATION.cff

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+cff-version: 1.2.0
+message: "If you use Ulam-1-Small, please cite this model release and paper."
+title: "Ulam-1-Small: Post-Training for Mathematical Reasoning"
+type: software
+authors:
+  - name: "Ulam AI"
+version: "1.0.0"
+date-released: "2026-08-19"
+repository-code: "https://huggingface.co/ulamai/Ulam-1-Small"
+url: "https://ulam.ai"
+license-url: "https://huggingface.co/ulamai/Ulam-1-Small/blob/v1.0.0/LICENSE"
+keywords:
+  - mathematical reasoning
+  - language model
+  - post-training
+  - reinforcement learning
+  - direct preference optimization

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Ulam AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NOTICE

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+Ulam-1-Small
+Copyright (c) 2026 Ulam AI
+
+Ulam-1-Small is a modified mathematical-reasoning model with the lineage:
+
+  Qwen/Qwen2.5-3B
+  -> Qwen/Qwen2.5-Coder-3B
+  -> WeiboAI/VibeThinker-3B
+  -> Ulam post-training stages
+
+Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT,
+Copyright (c) Alibaba Cloud. All Rights Reserved.
+
+Built with Qwen. Improved using Qwen.
+
+Pinned public upstream revisions used for release provenance:
+
+- Qwen/Qwen2.5-3B:
+  3aab1f1954e9cc14eb9509a215f9e5ca08227a9b
+- Qwen/Qwen2.5-Coder-3B:
+  09d9bc5d376b0cfa0100a0694ea7de7232525803
+- WeiboAI/VibeThinker-3B:
+  77bd2cced09193c8b9a59a32bd8577bbd1f3e01c
+
+The VibeThinker repository declares MIT in its model-card metadata and license
+section but does not contain a standalone LICENSE file at the pinned revision.
+The historical Qwen Research License text is retained under
+`UPSTREAM_LICENSES/` as a lineage and attribution record for VibeThinker's
+declared Qwen/Qwen2.5-Coder-3B base model.
+
+Ulam training-data relationships:
+
+- ulamai/verified-research-reasoning-trajectories
+- ulamai/verified-math-olympiad-trajectories
+
+See README.md, provenance.json, LICENSE, and UPSTREAM_LICENSES/ for complete
+attribution, modifications, limitations, and provenance.

README.md

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+---
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+license: mit
+base_model: WeiboAI/VibeThinker-3B
+base_model_relation: finetune
+datasets:
+  - ulamai/verified-research-reasoning-trajectories
+  - ulamai/verified-math-olympiad-trajectories
+tags:
+  - math
+  - reasoning
+  - qwen2
+  - conversational
+  - safetensors
+  - text-generation-inference
+  - ulam
+model-index:
+  - name: Ulam-1-Small
+    results:
+      - task:
+          type: text-generation
+          name: Mathematical proof generation
+        dataset:
+          type: simobench
+          name: SIMOBench primary itemized audit (126 problems)
+        metrics:
+          - type: simobench_itemized_score
+            name: Itemized proof score (maximum 882)
+            value: 667
+        source:
+          name: Ulam-1-Small evaluation receipt
+          url: https://huggingface.co/ulamai/Ulam-1-Small/blob/v1.0.0/evaluation_results.json
+      - task:
+          type: text-generation
+          name: Research mathematical reasoning
+        dataset:
+          type: erdosbench
+          name: ErdosBench external-judge audit (226 items)
+        metrics:
+          - type: erdosbench_weighted_mean
+            name: Weighted grade mean (A/B/C/D/F/M = 4/2.7/1.7/1/0/0)
+            value: 2.2871681415929204
+        source:
+          name: Ulam-1-Small evaluation receipt
+          url: https://huggingface.co/ulamai/Ulam-1-Small/blob/v1.0.0/evaluation_results.json
+---
+
+# Ulam-1-Small
+
+Ulam-1-Small is a 3.086-billion-parameter mathematical reasoning model developed by Ulam AI. It is a standalone BF16 Transformers model merged from the exact Ulam SFT-v2 step-409 parent and the prompt-balanced DPO checkpoint-20 adapter.
+
+**Built with Qwen.** The model lineage includes Qwen2.5-3B, Qwen2.5-Coder-3B, and WeiboAI/VibeThinker-3B. Upstream lineage and modifications are recorded in [NOTICE](NOTICE) and `provenance.json`.
+
+The release is aimed at exploratory mathematical problem solving and proof-style reasoning. It is not a theorem-certification system: strong claims, counterexamples, and purported proofs require independent expert review.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Developer | Ulam AI |
+| Repository | `ulamai/Ulam-1-Small` |
+| Release | `v1.0.0` |
+| Architecture | `Qwen2ForCausalLM` |
+| Parameters | 3,085,938,688 |
+| Weight dtype | BF16 |
+| Context configuration | 131,072 tokens |
+| Selected checkpoint | Prompt-balanced DPO checkpoint 20 |
+| Distribution format | Standalone merged safetensors model |
+
+The model was selected for research-oriented use rather than for closed olympiad proof completion. V-SAO checkpoint 177 was stronger on both SIMOBench judging passes, while checkpoint-20 DPO had the highest mean in the 226-item ErdosBench audit and a safer observed tail among self-declared strong claims. This is an objective-dependent product decision, not a claim that DPO checkpoint 20 dominates all mathematical tasks.
+
+## Quick start with Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ulamai/Ulam-1-Small"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Prove that the sum of the first n odd integers is n squared.",
+    }
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        do_sample=False,
+        max_new_tokens=2048,
+    )
+
+completion = output[0, inputs["input_ids"].shape[-1]:]
+print(tokenizer.decode(completion, skip_special_tokens=True))
+```
+
+The checkpoint may emit explicit reasoning delimiters such as `<think>...</think>`. Applications that expose only a final response should parse or route those spans deliberately rather than assuming they are absent.
+
+## Serving with vLLM
+
+```bash
+vllm serve ulamai/Ulam-1-Small \
+  --dtype bfloat16 \
+  --max-model-len 16384 \
+  --served-model-name ulamai/Ulam-1-Small
+```
+
+Example OpenAI-compatible request:
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "ulamai/Ulam-1-Small",
+    "messages": [{"role": "user", "content": "Solve x^2 - 5x + 6 = 0."}],
+    "temperature": 0,
+    "max_tokens": 2048
+  }'
+```
+
+The 131,072-token architectural setting does not imply that every deployment can serve that length. Choose `--max-model-len` from available accelerator memory and validate it for the intended workload. Long proof generations may hit the configured output cap.
+
+### Release validation
+
+The merged release passed exact greedy-token equivalence against the parent-plus-adapter model on four public fixtures covering arithmetic, algebra, proof reasoning, and 762-token multi-turn context formatting. Every fixture also passed BF16 absolute and normalized logit tolerances with cosine similarity above 0.9998. A native Transformers GPU smoke test used the 2,048-token quick-start cap, and a fresh vLLM 0.25.1 load passed `/health`, `/v1/models`, and `/v1/chat/completions`. Transformers completed at EOS after 1,387 new tokens with `trust_remote_code=False`; vLLM served the model natively as `Qwen2ForCausalLM` while configured with a 16,384-token serving window. See `equivalence_receipt.json`, `transformers_smoke_receipt.json`, and `vllm_smoke_receipt.json`.
+
+The GPU validation host was an NVIDIA DGX Spark with an NVIDIA GB10 and 121 GiB of host-visible unified memory. The Transformers run used Python 3.12.13, PyTorch 2.11.0+cu130, and Transformers 5.13.1. This is a tested compatibility configuration, not a minimum-memory claim. The BF16 weight shards alone occupy about 5.75 GiB; model state, CUDA kernels, KV cache, graph capture, and the requested context require additional memory. A minimum accelerator configuration has not yet been characterized.
+
+## Intended uses
+
+- mathematical problem solving with human review;
+- exploratory proof-style and research reasoning;
+- research on compact reasoning models;
+- local inference through standard Transformers or vLLM APIs;
+- commercial and non-commercial use under the MIT License.
+
+## Out-of-scope uses
+
+- treating generated proofs, theorem claims, or counterexamples as formally verified;
+- unsupervised theorem announcements or autonomous research publication;
+- high-stakes medical, legal, financial, or safety decisions;
+- factual citation without checking primary sources;
+- tool calling, API orchestration, or autonomous agents without separate testing;
+- redistributing benchmark prompts, references, private reviewer material, or hidden evaluations;
+
+See [RESPONSIBLE_USE.md](RESPONSIBLE_USE.md) for deployment guidance.
+
+## Training lineage
+
+```text
+Qwen2.5-3B
+  -> Qwen2.5-Coder-3B
+  -> WeiboAI/VibeThinker-3B
+  -> Ulam rlvr_math full-weight stage
+  -> Ulam SFT checkpoint 535
+  -> Ulam SFT-v2 step 409
+  -> canonical V-SAO seed 101 adapter
+  -> prompt-balanced DPO checkpoint 20
+  -> merged standalone Ulam-1-Small
+```
+
+The released artifact is not the checkpoint-20 adapter applied directly to VibeThinker. Every retained final adapter was trained and evaluated against the exact SFT-v2 step-409 parent. The release manifest binds the public files to the parent lineage and adapter hashes.
+
+### Training datasets
+
+The training lineage uses material derived from:
+
+- [`ulamai/verified-research-reasoning-trajectories`](https://huggingface.co/datasets/ulamai/verified-research-reasoning-trajectories): research-level proof-process, review, reward, and preference views;
+- [`ulamai/verified-math-olympiad-trajectories`](https://huggingface.co/datasets/ulamai/verified-math-olympiad-trajectories): directly used olympiad and mathematical trajectory material transformed into supervised and verifier-backed training views.
+
+The public repositories contain inspection samples and schemas, not necessarily every private training row. Exact public revisions and their role in the release are recorded in `provenance.json`.
+
+### Post-training objectives
+
+The full pipeline combines:
+
+- full-weight outcome learning;
+- supervised proof refinement;
+- verified reward optimization;
+- reviewed process supervision;
+- privileged-prefix distillation;
+- verified direct preference optimization.
+
+The final DPO training set contained 98 preference pairs, with 24 validation pairs. Checkpoint 20 was selected from a dose curve rather than from terminal training loss alone.
+
+## Evaluation
+
+### SIMOBench
+
+SIMOBench contains 126 synthetic olympiad-style problems graded from 0 to 7, for a maximum of 882. Each checkpoint received one stochastic generation per problem at temperature 0.7, top-p 0.95, and a 65,536-token output allowance.
+
+| Checkpoint | Primary itemized audit | Mean | Score-7 proofs | Scores >=5 | Cap hits | Secondary regrade |
+|---|---:|---:|---:|---:|---:|---:|
+| V-SAO checkpoint 177 | **681/882** | **5.405** | **34** | **98** | 1 | **729/882** |
+| OPSD checkpoint 248 | 678/882 | 5.381 | 29 | 96 | **0** | 695/882 |
+| **DPO checkpoint 20 (released)** | **667/882** | **5.294** | **31** | **94** | **4** | **704/882** |
+
+The primary paired comparison between V-SAO and DPO is 21 V-SAO wins, 12 DPO wins, and 93 ties, for a 14-point V-SAO margin. Both passes were model-assisted single-judge audits of byte-identical generations. They agree on the leader but differ materially in totals; no inter-rater agreement statistic is available.
+
+### ErdosBench
+
+The separate 226-item research audit uses A/B/C/D/F/M grades mapped to 4/2.7/1.7/1/0/0 points.
+
+| Checkpoint | A | B | C | D | F | M | Mean | Self-declared strong claims rejected |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| **DPO checkpoint 20 (released)** | **6** | 122 | 95 | 2 | **1** | **0** | **2.287** | **1/17 (5.9%)** |
+| V-SAO checkpoint 177 | 3 | **126** | 90 | 2 | 4 | 1 | 2.244 | 4/16 (25.0%) |
+| OPSD checkpoint 248 | 2 | 91 | 128 | 0 | 2 | 3 | 2.085 | 1/5 (20.0%) |
+
+For DPO versus V-SAO, the paired mean difference is 0.043 points per item, with an item-bootstrap 95% interval of `[-0.062, 0.148]` and a two-sided sign-flip `p=0.435`. The aggregate mean difference is therefore uncertain. The two-sided Fisher exact test for the observed strong-claim rejection counts gives `p=0.175`; under independent Jeffreys priors, the posterior probability that DPO's rejection rate is lower is 0.939.
+
+The 226-item package makes the arithmetic reproducible but does not include all raw responses and prompt bytes needed for independent mathematical re-adjudication.
+
+### Development-time checkpoint-selection endpoint
+
+Checkpoint 20 scored 7/83 on an automatic private ErdosBench endpoint, reproduced exactly under an independent fresh model load. The DPO dose curve was step 10 = 5/83, step 20 = 7/83, step 30 = 3/83, and step 49 = 3/83. This endpoint was repeatedly inspected during selection: 7/83 is exploratory model-selection evidence, not an unbiased generalization estimate.
+
+Machine-readable summaries and claim boundaries are in `evaluation_results.json`.
+
+## Limitations
+
+- Outputs are not formal proofs and can contain subtle or decisive mathematical errors.
+- The selected checkpoint made one rejected and five C-grade self-declared strong claims in the 226-item audit.
+- Aggregate DPO-versus-V-SAO ErdosBench superiority is not statistically established.
+- SIMOBench uses one generation seed per item and model-assisted single-judge grading.
+- The evaluated 65,536-token generation budget is larger than many practical serving budgets.
+- The earlier 139-item and later 226-item ErdosBench artifacts have no supplied item mapping.
+- The private 83-item endpoint was consumed during checkpoint selection.
+- Raw model output may expose `<think>` reasoning spans or fail the intended terminal answer schema.
+- The model was not validated for autonomous tools or agentic API use.
+- Compact parameter count limits broad factual knowledge compared with larger general-purpose models.
+
+## Reproducibility and integrity
+
+- `release_manifest.json` inventories the model, tokenizer, and exact source hashes.
+- `provenance.json` describes the public lineage without private storage paths.
+- `evaluation_results.json` contains publishable aggregate results and limitations.
+- `checksums.sha256` binds every released file except itself.
+
+Use the immutable `v1.0.0` tag for reproducible downloads.
+
+## License
+
+Ulam-1-Small, including its released model weights, is released by Ulam AI under the [MIT License](LICENSE). Upstream lineage, attribution, pinned revisions, and material modifications are recorded in [NOTICE](NOTICE) and `provenance.json`; archived upstream license text is retained under `UPSTREAM_LICENSES/`.
+
+## Citation
+
+```bibtex
+@misc{ulam2026ulamsmall,
+  title        = {Ulam-1-Small: Post-Training for Mathematical Reasoning},
+  author       = {{Ulam AI}},
+  year         = {2026},
+  howpublished = {Hugging Face model release},
+  url          = {https://huggingface.co/ulamai/Ulam-1-Small}
+}
+```
+
+The complete paper is included under `paper/`.
+
+## Contact
+
+For model questions, licensing, or responsible disclosure, contact Ulam AI through [ulam.ai](https://ulam.ai/).

RELEASE_NOTES.md

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+# Ulam-1-Small v1.0.0
+
+Initial standalone BF16 release of Ulam-1-Small.
+
+## Canonical artifact
+
+- Architecture: `Qwen2ForCausalLM`
+- Parameters: 3,085,938,688
+- Parent: exact Ulam SFT-v2 step-409 full-weight checkpoint
+- Adapter: prompt-balanced DPO checkpoint 20
+- Adapter SHA-256:
+  `e1c337d180f513b652226ed8fc3fb8b9900d5572e1e68b4c068b1193dd016842`
+- Format: merged safetensors, no private parent or PEFT adapter required
+
+## Selection disclosure
+
+Checkpoint 20 is the research-first release choice. V-SAO checkpoint 177
+outperforms it on both SIMOBench grading passes and remains the stronger
+closed-proof specialist. DPO checkpoint 20 is selected for its separate
+ErdosBench research profile and safer observed strong-claim tail.
+
+## Compatibility
+
+- Standard Transformers model; no `trust_remote_code=True`
+- Standard vLLM serving path
+- Exact tokenizer and chat template from the inference parent
+- Parent-plus-adapter versus merged-model equivalence passed
+- GPU Transformers smoke passed with the documented 2,048-token cap
+- vLLM 0.25.1 health, model-list, and chat-completions smoke passed
+
+## License
+
+Ulam-1-Small, including its released model weights, is released by Ulam AI
+under the MIT License. Upstream lineage and attribution are preserved in
+`NOTICE` and `provenance.json`.

RESPONSIBLE_USE.md

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+# Responsible use
+
+Ulam-1-Small is a research assistant for mathematical reasoning, not a formal
+proof checker or autonomous theorem-certification system.
+
+## Required human review
+
+Independently check any generated theorem, proof, counterexample, literature
+claim, or numerical conclusion before relying on it. Claims labeled solved,
+refuted, likely false, or counterexample found deserve especially strict
+review. Prefer machine-checkable certificates or formalization where possible.
+
+## Output handling
+
+Raw generations may include `<think>...</think>` spans, partial terminal
+answers, or long self-corrections. Public applications should choose and test a
+clear policy for retaining, separately routing, or suppressing reasoning spans.
+Do not represent hidden or discarded reasoning as independently verified.
+
+## Prohibited or unsupported use
+
+- high-stakes medical, legal, financial, or safety decisions;
+- unsupervised public theorem announcements;
+- autonomous tool or API use without dedicated validation;
+- extracting or republishing private benchmark content;
+- presenting outputs as citations to sources the model did not inspect;
+
+## Evaluation boundaries
+
+Published benchmark figures are checkpoint-selection evidence under their
+recorded protocols. They are not guarantees for a different prompt template,
+serving cap, runtime, seed, quantization, or downstream fine-tune. Rerun
+admission tests after changing any of those conditions.

SECURITY.md

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+# Security policy
+
+Do not include credentials, private prompts, hidden benchmark material, or
+personal data in a public report.
+
+Report suspected malicious files, credential exposure, unsafe serialization,
+or other security issues privately through the contact channel at
+https://ulam.ai/. Include the model revision or immutable Hub commit, affected
+filename, reproduction steps, and impact. Please do not open a public
+discussion containing secrets or restricted evaluation content.
+
+The canonical release uses safetensors weights and ordinary Transformers model
+classes. Loading the model must not require `trust_remote_code=True`.

UPSTREAM_LICENSES/QWEN-RESEARCH-LICENSE

@@ -0,0 +1,54 @@
+Qwen RESEARCH LICENSE AGREEMENT
+
+Qwen RESEARCH LICENSE AGREEMENT Release Date: September 19, 2024
+
+By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+
+1. Definitions
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+5. Intellectual Property
+    a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
+    b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.
+    c. If you commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against us or any entity alleging that the Materials or any output therefrom, or any part of the foregoing, infringe any intellectual property or other right owned or licensable by you, then all licenses granted to you under this Agreement shall terminate as of the date such lawsuit or other proceeding is commenced or brought.
+
+6. Disclaimer of Warranty and Limitation of Liability
+    a. We are not obligated to support, update, provide training for, or develop any further version of the Qwen Materials or to grant any license thereto.
+    b. THE MATERIALS ARE PROVIDED "AS IS" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. WE MAKE NO WARRANTY AND ASSUME NO RESPONSIBILITY FOR THE SAFETY OR STABILITY OF THE MATERIALS AND ANY OUTPUT THEREFROM.
+    c. IN NO EVENT SHALL WE BE LIABLE FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.
+    d. You will defend, indemnify and hold harmless us from and against any claim by any third party arising out of or related to your use or distribution of the Materials.
+
+7. Survival and Termination.
+    a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+    b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 6 and 8 shall survive the termination of this Agreement.
+
+8. Governing Law and Jurisdiction.
+    a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+    b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
+
+9. Other Terms and Conditions.
+    a. Any arrangements, understandings, or agreements regarding the Material not stated herein are separate from and independent of the terms and conditions of this Agreement. You shall request a separate license from us, if you use the Materials in ways not expressly agreed to in this Agreement.
+    b. We shall not be bound by any additional or different terms or conditions communicated by you unless expressly agreed.

UPSTREAM_LICENSES/VIBETHINKER-LICENSE-DECLARATION.md

@@ -0,0 +1,16 @@
+# WeiboAI/VibeThinker-3B license declaration
+
+Pinned repository: `WeiboAI/VibeThinker-3B`
+
+Pinned revision: `77bd2cced09193c8b9a59a32bd8577bbd1f3e01c`
+
+At this revision, the Hugging Face repository metadata declares `license: mit`
+and the model card states: “The model repository is licensed under the MIT
+License.” The repository does not contain a standalone `LICENSE` file.
+
+Source:
+https://huggingface.co/WeiboAI/VibeThinker-3B/tree/77bd2cced09193c8b9a59a32bd8577bbd1f3e01c
+
+VibeThinker's declared base model is `Qwen/Qwen2.5-Coder-3B`. The historical
+Qwen Research License is therefore retained in `UPSTREAM_LICENSES/` as a
+lineage and attribution record.

build_receipt.json

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+{
+  "checks": [
+    "exact_adapter_sha256",
+    "exact_adapter_config_sha256",
+    "adapter_tensor_count_504",
+    "adapter_finite",
+    "parent_parameter_count",
+    "active_adapter_default",
+    "safe_merge",
+    "merged_parameter_count",
+    "merged_parameters_finite",
+    "safe_serialization"
+  ],
+  "completed_at": "2026-08-19T09:47:24.119274+00:00",
+  "dtype": "bfloat16",
+  "operation": "PEFT merge_and_unload with safe_merge=True",
+  "parameter_count": 3085938688,
+  "schema_version": "ulam-public-model-build-receipt-v1",
+  "software": {
+    "accelerate": "1.13.0",
+    "peft": "0.20.0",
+    "platform": "macOS-15.3.1-arm64-arm-64bit",
+    "python": "3.12.12",
+    "safetensors": "0.8.0",
+    "tokenizers": "0.22.2",
+    "torch": "2.13.0",
+    "transformers": "5.14.1"
+  },
+  "source_adapter": {
+    "config": {
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+      "name": "adapter_config.json",
+      "sha256": "1844cc34bf0e72af07815539b0040100e5cbf579b465c8ade89731bf6b74671f"
+    },
+    "name": "prompt-balanced DPO checkpoint 20",
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+    }
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+  "source_parent": {
+    "bytes": 6183389350,
+    "files": [
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+        "bytes": 2427,
+        "name": "chat_template.jinja",
+        "sha256": "44d5f08f3f72b837eaad09f13a54c1f9f4eb58d75240334548b7fd52a5437fa5"
+      },
+      {
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+        "name": "config.json",
+        "sha256": "72cd201be9cdea809814c42139c4f6974f9aa9f6d542c157bb02be78bc88d079"
+      },
+      {
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+        "name": "generation_config.json",
+        "sha256": "5b7c65dc5a45131b787a9cabb07348f77677b048139dc208db1746c84dc08751"
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+        "name": "model-00002-of-00002.safetensors",
+        "sha256": "8fbe5a0583ea2d2c55a9a27c0cb11768c75d7cb3b6bd2fd72c2fdb4fab80d54b"
+      },
+      {
+        "bytes": 35617,
+        "name": "model.safetensors.index.json",
+        "sha256": "328342baeeef765d522811c403a61da788c648930a38a7c5ab1a048a7578845a"
+      },
+      {
+        "bytes": 11422263,
+        "name": "tokenizer.json",
+        "sha256": "b17e16899b7fab7e695509f84bac5f10ed12804f0a590e935941e5af7f092f7f"
+      },
+      {
+        "bytes": 407,
+        "name": "tokenizer_config.json",
+        "sha256": "6b3ae98ce1845e699633801657f761ef091a60a71ca468d47cb456bc6b011598"
+      }
+    ],
+    "identity": "ulam/sft-v2-step-409",
+    "lineage_sha256": "1b90254f53af00976d71d369e4c37bb7cdd18053e862d26c1568ac0fdeefbbfc",
+    "object_count": 8
+  },
+  "status": "PASSED"
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- '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 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 %}

checksums.sha256

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config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "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",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention"
+  ],
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+  "model_type": "qwen2",
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+  "num_key_value_heads": 2,
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+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
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+    "rope_type": "default"
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+  "sliding_window": null,
+  "tie_word_embeddings": true,
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+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

equivalence_receipt.json

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+  "status": "PASSED"
+}

evaluation_results.json

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+{
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+  "model": "ulamai/Ulam-1-Small",
+  "release": "v1.0.0",
+  "selected_checkpoint": "prompt-balanced DPO checkpoint 20",
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+      "claim": "V-SAO checkpoint 177 is stronger than the released DPO checkpoint 20 for closed, reference-backed proof completion under this audit."
+    },
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+        "use": "ranking sensitivity only"
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+      "claim": "DPO has the highest observed mean and a safer observed strong-claim tail, but the aggregate mean difference is statistically uncertain and the strong-claim subset is small."
+    },
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+      "exact_fresh_load_replication": true,
+      "claim": "Exploratory model-selection evidence on a consumed private endpoint; not an unbiased generalization estimate."
+    }
+  ],
+  "claim_boundaries": [
+    "No result establishes broad mathematical state of the art.",
+    "No result establishes formal proof correctness.",
+    "SIMOBench totals come from model-assisted single-judge audits without inter-rater agreement.",
+    "The 226-item ErdosBench package lacks all raw bytes required for independent mathematical re-adjudication.",
+    "The 83-item private endpoint was repeatedly inspected during model selection.",
+    "Changing the chat template, parser, output cap, seed, runtime, or quantization requires new admission tests."
+  ],
+  "source_receipts": [
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+      "name": "SIMOBench primary manifest",
+      "sha256": "4258bdb36374139042f13dea09f74029009c2da1f92c02f828ec261b9180c81b"
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+      "name": "SIMOBench secondary SHA256SUMS",
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+    },
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+      "sha256": "b884d500891e68f071b006ddd4f3dfb4d75dd32638aff15547c9e9e266d1f945"
+    },
+    {
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+      "sha256": "0151195a6ca2a5d4a14b058ec5d92115155bc1b35bad013362fb7450dc100a5d"
+    }
+  ]
+}

generation_config.json

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+  "eos_token_id": 151643,
+  "max_new_tokens": 4096,
+  "pad_token_id": 151643,
+  "transformers_version": "5.14.1"
+}

inference_fixture.json

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+      "id": "short_arithmetic",
+      "messages": [
+        {
+          "content": "Compute 37 times 41 and give one short arithmetic check.",
+          "role": "user"
+        }
+      ]
+    },
+    {
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+      "id": "algebra",
+      "messages": [
+        {
+          "content": "Solve x squared minus 5x plus 6 equals zero over the real numbers.",
+          "role": "user"
+        }
+      ]
+    },
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+      "id": "proof_style",
+      "messages": [
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+          "content": "Prove that the sum of the first n odd integers is n squared.",
+          "role": "user"
+        }
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+          "role": "user"
+        },
+        {
+          "content": "I have read the ledger and will use only its definitions.",
+          "role": "assistant"
+        },
+        {
+          "content": "Derive a closed form for c_k and then evaluate c_25, following the requested format.",
+          "role": "user"
+        }
+      ]
+    }
+  ],
+  "filled_by": "scripts/hf_release/validate_equivalence.py",
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+    "max_new_tokens": 16
+  },
+  "purpose": "multi-case merged-versus-adapter and clean-download validation",
+  "schema_version": "ulam-public-inference-fixture-v2"
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