Sync Occamy model card and assets

Sync README, LICENSE, and referenced assets from Accio-Lab/occamy; remove the obsolete ossutil report.

.gitattributes

@@ -34,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/occamy.png filter=lfs diff=lfs merge=lfs -text
+assets/occamy-main-results.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -14,109 +14,129 @@ tags:
 ---
 
 <div align="center">
-
-# Occamy-1.0
-
-**Open Pareto-frontier 35B Intelligence for Co-work**
-
-[Model weights](https://huggingface.co/Accio-Lab/Occamy-1.0) · [GitHub](https://github.com/Accio-Lab/occamy) · [Training infrastructure](https://github.com/Accio-Lab/Dressage)
-
+  <picture>
+    <img src="assets/accio.png" width="34%" alt="Accio">
+  </picture>
+  &nbsp;&nbsp;&nbsp;&nbsp;
+  <picture>
+    <img src="assets/occamy.png" width="13%" alt="Occamy logo">
+  </picture>
+  <h1>Occamy-1.0</h1>
+  <p><strong>Open Pareto-frontier 35B Intelligence for Co-work</strong></p>
 </div>
 
-## Overview
-
-Occamy-1.0 is an open, cost-efficient co-work model developed by the Accio Team. It is built by further post-training [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) for long-horizon, stateful agent workloads across terminals, files, structured APIs, and productivity tools.
+<hr>
 
-Co-work tasks can invoke a model dozens or hundreds of times. Their practical cost therefore depends on the complete episode, not only on the price of a single response. Occamy-1.0 is designed to deliver reliable task execution at a compact 35B-A3B scale. Across our co-work evaluation suite, it is consistently among the strongest comparably sized models and remains competitive with substantially larger systems on several tasks. Under the evaluation and pricing protocol described in our technical report, Occamy lies near the low-cost knee of the observed cost-performance Pareto frontier.
+<div align="center" style="line-height: 1;">
+  <a href="https://accio-lab.github.io/occamy/"><img alt="Project Website" src="https://img.shields.io/badge/Website-Occamy--1.0-087F6A"></a>
+  <a href="https://huggingface.co/Accio-Lab/Occamy-1.0"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Occamy--1.0-FFD21E"></a>
+  <a href="https://github.com/Accio-Lab/Dressage"><img alt="Dressage" src="https://img.shields.io/badge/Training-Dressage-087F6A"></a>
+  <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue"></a>
+</div>
 
-## Highlights
+<p align="center">
+  <a href="https://accio-lab.github.io/occamy/">Project Website</a> &nbsp;|&nbsp;
+  <a href="https://huggingface.co/Accio-Lab/Occamy-1.0">Model Weights</a> &nbsp;|&nbsp;
+  <a href="https://github.com/Accio-Lab/Dressage">Training Framework</a>
+</p>
 
-- **Built for co-work:** optimized for multi-step, stateful work rather than isolated question answering.
-- **Long-horizon execution:** trained to preserve progress across tool calls, failures, context compaction, pruning, and other harness-level history rewrites.
-- **Broad agentic capability:** strong co-work performance while retaining competitive tool-calling, coding, and instruction-following ability.
-- **Cost-efficient serving:** 35B total parameters with 3B activated per token, a practical operating point for self-hosted agent systems.
-- **Open ecosystem:** Apache-2.0 model weights, an open GitHub repository, and an open-source version of our multi-harness training infrastructure.
+## 1. Model Introduction
 
-## Model details
+Occamy-1.0 is a compact agentic model purpose-built for real-world co-work: long-horizon, stateful tasks that require coordinated use of search, code, tools, files, structured APIs, and productivity software. Starting from the post-trained [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) checkpoint, Occamy concentrates further training on reliable execution, persistent state tracking, recovery, and follow-through rather than relearning general capabilities from scratch.
 
-| Item | Value |
-|---|---|
-| Developer | Accio Team |
-| Base checkpoint | Qwen3.6-35B-A3B |
-| Architecture | Mixture-of-Experts, 35B total / 3B activated |
-| Context length | 262,144 tokens |
-| Precision | BF16 |
-| Primary focus | Long-horizon co-work and tool-using agents |
-| License | Apache-2.0 |
+### Key Features
 
-The checkpoint retains the Qwen3.6 multimodal architecture, but the current Occamy release and reported evaluation focus on text- and tool-based agent execution. Browser and GUI interaction are not currently supported or claimed.
+- **Co-work specialization:** Designed for sustained execution across multi-step professional workflows, not isolated question answering.
+- **Compact inference footprint:** A 35B-total, 3B-active Mixture-of-Experts model that keeps long-running agent workloads practical.
+- **Long-horizon continuity:** Designed to keep work coherent across tool calls, delegated runs, and history rewrites such as context compaction.
+- **Broad agentic capability:** Co-work gains are accompanied by strong tool calling, terminal coding, and instruction following.
+- **Execution-grounded training:** Supervised fine-tuning spans general agentic work, long-horizon interaction, software engineering, and tool-call grounding.
+- **Open training stack:** The multi-harness reinforcement-learning infrastructure used to train Occamy is released as [Dressage](https://github.com/Accio-Lab/Dressage).
 
-## Training overview
+> [!NOTE]
+> Occamy is optimized for common co-work workloads, not as a replacement for frontier models on every task. Retrieval-heavy and simulated-user tasks still have headroom, and native browser or desktop visual interaction is not part of the current co-work training interface.
 
-Occamy-1.0 uses a specialization-and-consolidation recipe rather than training from a base model:
+## 2. Model Summary
 
-1. **Marathon Expert:** full-parameter SFT followed by Hierarchical Decoupled Policy Optimization (HDPO), targeting sustained execution over long, stateful episodes.
-2. **Sprint Expert:** full-parameter SFT over a broader distribution of shorter-horizon agentic workloads, including coding, information gathering, and structured tool use.
-3. **Capability harmonization:** model merging combines the complementary experts, followed by Single-Rollout Asynchronous Optimization (SAO) on a broad co-work task mixture.
+<div align="center">
+<table>
+<tbody>
+<tr><td align="center"><strong>Architecture</strong></td><td align="center">Mixture-of-Experts causal model with vision encoder</td></tr>
+<tr><td align="center"><strong>Total Parameters</strong></td><td align="center">35B</td></tr>
+<tr><td align="center"><strong>Activated Parameters</strong></td><td align="center">3B</td></tr>
+<tr><td align="center"><strong>Number of Layers</strong></td><td align="center">40</td></tr>
+<tr><td align="center"><strong>Number of Experts</strong></td><td align="center">256</td></tr>
+<tr><td align="center"><strong>Activated Experts</strong></td><td align="center">8 routed + 1 shared</td></tr>
+<tr><td align="center"><strong>Base Architecture Context</strong></td><td align="center">262,144 tokens</td></tr>
+<tr><td align="center"><strong>SFT Sequence Length</strong></td><td align="center">131,072 tokens</td></tr>
+<tr><td align="center"><strong>Starting Checkpoint</strong></td><td align="center"><a href="https://huggingface.co/Qwen/Qwen3.6-35B-A3B">Qwen3.6-35B-A3B</a></td></tr>
+<tr><td align="center"><strong>Post-training</strong></td><td align="center">Full-parameter SFT, HDPO, model merging, and SAO</td></tr>
+</tbody>
+</table>
+</div>
 
-Training data are execution-grounded: tasks are paired with runnable environments, observable state transitions, realized tool behavior, trajectory evidence, and task-level grading. Multi-harness adapters preserve harness-specific token, tool, rewrite, and state semantics while exposing a common trajectory and replay contract to the learner.
+Architecture fields follow the starting checkpoint's published model card. Occamy post-trains the language backbone without changing the architecture; the vision encoder and projector are frozen during SFT. The released checkpoint configuration remains the source of truth for serving limits.
 
-[Dressage](https://github.com/Accio-Lab/Dressage) is the open-source release of the infrastructure family used for multi-harness agentic training, token-exact trajectory capture, and state replay.
+## 3. Evaluation Results
 
-## Evaluation
+<div align="center">
+  <picture>
+    <img src="assets/occamy-main-results.png" width="100%" alt="Occamy-1.0 results on co-work, tool-use, coding, and business benchmarks">
+  </picture>
+</div>
 
-<p align="center">
-  <img src="occamy-main-results-9.png" alt="Occamy-1.0 results across nine agentic benchmarks" width="100%">
-</p>
+| Benchmark | Occamy-1.0 | Qwen3.6<br>35B-A3B | Agents-A1 | Nex-N2-mini | BigBang-1.0 | Ornith-1.5 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Claw-Eval (average) | **82.2** | 69.5 | 69.9 | 66.6 | 63.5 | 64.4 |
+| Claw-Eval (Pass³) | **71.4** | 54.8 | 41.7 | 37.0 | 40.2 | 48.7 |
+| WildClawBench | **49.16** | 40.4 | 30.73 | 30.31 | 32.87 | 45.91 |
+| CommerceAgentBench | **37.40** | 19.6 | 9.3 | 16.8 | 30.8 | **37.40** |
+| Business Arena | **$79,868** | $44,751 | $33,626 | $13,325 | $56,477 | $66,292 |
+| GDPval<sup>†</sup> | **1,128** | 1,004 | 869 | 999 | 951 | 855 |
+| OfficeQA Pro | 48.1 | 39.1 | 23.3 | 46.6 | 43.6 | **59.4** |
+| τ³-Bench (Banking) | **37.1** | 11.9 | 7.2 | 25.8 | 10.3 | 21.7 |
+| AutomationBench (Pass¹) | **27.6** | 7.5 | 2.2 | 5.7 | 14.8 | 18.5 |
+| AutomationBench (partial) | **69.1** | 39.4 | 14.7 | 27.9 | 47.4 | 58.0 |
+| BFCL v4 | 65.40 | 63.19 | 57.23 | 62.81 | 57.86 | **68.51** |
+| VitaBench | 41.75 | 34.25 | 37.00 | 26.25 | **46.00** | 40.25 |
+| Terminal-Bench 2.1 | 59.0 | 49.5 | 41.6 | 60.7<sup>*</sup> | 33.7 | **67.8<sup>*</sup>** |
+| IFEval | 91.53 | 86.90 | **91.60** | **91.60** | 90.50 | 81.80 |
 
-<p align="center"><em>Occamy-1.0 main results. Dark teal shows Occamy-1.0; the lighter segment shows the Qwen3.6-35B-A3B starting checkpoint where available.</em></p>
+**Bold:** Best result in each row; ties are both bolded. <sup>*</sup> Official model-card result. <sup>†</sup> Reproduced on the public task release.
 
-The figure provides a cross-model overview. The table below records the current Occamy-1.0 results; exact harness versions, budgets, sampling settings, judge models, and comparison rules are documented in the technical report.
+## 4. Training Recipe
 
-| Category | Benchmark | Metric | Occamy-1.0 |
-|---|---|---:|---:|
-| Co-work | Claw-Eval | Average | 82.20 |
-| Co-work | Claw-Eval | Pass³ | 71.40 |
-| Co-work | WildClawBench | avg@3 | 49.16 |
-| Co-work | CommerceAgentBench | Pass¹ | 37.38 |
-| Co-work | Business Arena | Avg. final net worth | $79,868 |
-| Co-work | GDPval | Score | 1128 |
-| Co-work | OfficeQA Pro | Accuracy | 48.10 |
-| Co-work | τ³-Bench (Banking) | Pass¹ | 37.10 |
-| Tool calling | AutomationBench | Pass¹ / Partial | 27.60 / 69.10 |
-| Tool calling | BFCL v4 | Score | 65.40 |
-| Tool calling | VitaBench | Score | 41.75 |
-| Coding | Terminal-Bench 2.1 | Accuracy | 59.00 |
-| Instruction following | IFEval | Score | 91.53 |
+Occamy uses staged specialization and consolidation:
 
-We do not currently report a standalone search benchmark. Search and information gathering may appear inside co-work tasks, but they should not be interpreted as a separate, controlled search-capability evaluation.
+```text
+Qwen3.6-35B-A3B
+  ├─ Marathon Expert: SFT → HDPO ┐
+  └─ Sprint Expert: SFT          ├─ Uniform merge → SAO → Occamy-1.0
+```
 
-### Efficiency and reliability
+The Marathon Expert learns sustained execution and accuracy-conditioned efficiency, while the Sprint Expert preserves broader agentic capability. A uniform parameter-space merge combines both experts into one checkpoint with no inference-time routing or ensembling, and a final Single-Rollout Asynchronous Optimization (SAO) stage refines the merged policy on a broad co-work mixture.
 
-On the combined Claw-Eval T/C tasks, Occamy improves task success while using less interaction and producing fewer execution failures than its starting checkpoint under the same protocol.
+The deduplicated SFT union across both experts is:
 
-| Metric | Qwen3.6-35B-A3B | Occamy-1.0 |
-|---|---:|---:|
-| Claw-Eval average | 69.5 | **82.2** |
-| Trial success rate | 62.81% | **77.55%** |
-| Tokens per trajectory | 185,999 | **149,713** |
-| Tool calls per trajectory | 14.24 | **12.07** |
-| Trace wall time | 74.58 s | **39.96 s** |
-| Timeout rate | 9.88% | **2.18%** |
+| Data source | Trajectories | Average length | Tokens |
+| --- | ---: | ---: | ---: |
+| General agentic | 5,418 | 37.7K | 204.1M |
+| Long-horizon interactive agents | 923 | 95.8K | 88.4M |
+| Terminal and software engineering | 1,228 | 35.1K | 43.1M |
+| Tool-call grounding | 7,429 | 9.1K | 67.7M |
+| **Overall** | **14,998** | **26.9K** | **403.3M** |
 
-For the aggregate cost-performance analysis, Claw-Eval, WildClawBench, AutomationBench, and GDPval are min-max normalized across the compared systems and weighted equally. Per-task cost is computed from measured token usage using a common input, cache-read, and output pricing protocol. This is an inference-cost estimate for comparison, not a complete deployment total-cost-of-ownership analysis.
+Training tasks are grounded in executable environments with observable state transitions and task-level grading. The open-source [Dressage](https://github.com/Accio-Lab/Dressage) stack provides multi-harness execution, token-exact trajectory capture, sandbox integration, and multi-segment conversion for reinforcement learning.
 
-## Quickstart
+## 5. Deployment
 
-Occamy-1.0 uses the supplied <code>chat_template.jinja</code>. We recommend serving it behind an OpenAI-compatible endpoint with a recent version of SGLang or vLLM.
+Occamy-1.0 keeps the Qwen3.6-35B-A3B architecture, so the [upstream deployment recipe](https://huggingface.co/Qwen/Qwen3.6-35B-A3B#deployment) is the reference serving path. The examples below mirror that recipe with eight-way tensor parallelism and its full context length; adjust both to fit your hardware and confirm them against the released Occamy checkpoint configuration.
 
 ### SGLang
 
-The following example uses tensor parallelism across eight GPUs and enables Qwen reasoning and tool-call parsing:
-
-~~~bash
-uv pip install "sglang[all]>=0.5.10"
+The upstream model card recommends [SGLang](https://github.com/sgl-project/sglang) 0.5.10 or newer for the Qwen3.6 architecture.
 
+```bash
 python -m sglang.launch_server \
   --model-path Accio-Lab/Occamy-1.0 \
   --port 8000 \
@@ -125,13 +145,13 @@ python -m sglang.launch_server \
   --context-length 262144 \
   --reasoning-parser qwen3 \
   --tool-call-parser qwen3_coder
-~~~
+```
 
 ### vLLM
 
-~~~bash
-uv pip install "vllm>=0.19.0" --torch-backend=auto
+The upstream model card recommends [vLLM](https://github.com/vllm-project/vllm) 0.19.0 or newer for the Qwen3.6 architecture.
 
+```bash
 vllm serve Accio-Lab/Occamy-1.0 \
   --port 8000 \
   --tensor-parallel-size 8 \
@@ -139,78 +159,55 @@ vllm serve Accio-Lab/Occamy-1.0 \
   --reasoning-parser qwen3 \
   --enable-auto-tool-choice \
   --tool-call-parser qwen3_coder
-~~~
+```
 
-If memory is limited, reduce the context length. Long-horizon agent performance can depend on the available context and the harness's compaction policy, so results may differ from the reported configuration.
+Both commands expose an OpenAI-compatible endpoint at `http://localhost:8000/v1`.
 
-### OpenAI-compatible client
+## 6. Model Usage
 
-~~~python
+```python
 from openai import OpenAI
 
-client = OpenAI(
-    base_url="http://localhost:8000/v1",
-    api_key="EMPTY",
-)
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
 
 response = client.chat.completions.create(
     model="Accio-Lab/Occamy-1.0",
     messages=[
         {
             "role": "user",
-            "content": "Inspect the workspace, fix the failing tests, and summarize the changes.",
+            "content": "Inspect this repository, fix the failing test, and explain the change.",
         }
     ],
-    max_tokens=8192,
+    max_tokens=32768,
     temperature=1.0,
     top_p=0.95,
-    extra_body={"top_k": 20},
+    presence_penalty=1.5,
+    extra_body={
+        "top_k": 20,
+        "chat_template_kwargs": {
+            "enable_thinking": True,
+            "preserve_thinking": True,
+        },
+    },
 )
 
 print(response.choices[0].message.content)
-~~~
-
-For agent deployment, provide tool schemas through the serving API and let the harness own environment state, timeouts, retries, and history compaction. Replacing the supplied template or parser can change tool-call behavior.
+```
 
-## Intended use
+For multi-turn agent runs, retain the complete assistant message returned by the server, including reasoning content and tool calls, then append tool results using the standard OpenAI chat-completions schema. This preserves the execution context that Occamy relies on across long workflows.
 
-Occamy-1.0 is intended for research and development of:
+### Agent Frameworks
 
-- terminal- and file-based co-work agents;
-- structured API and productivity-tool workflows;
-- long-horizon task execution with stateful environments;
-- agentic post-training, replay, and evaluation;
-- coding and repository-level assistance inside sandboxed harnesses.
+Occamy was trained and evaluated across multiple harnesses, including [OpenClaw](https://github.com/openclaw/openclaw), [Hermes Agent](https://github.com/NousResearch/hermes-agent), and Accio Work. It can be integrated with other tool-using agent frameworks through the same OpenAI-compatible API.
 
-Use sandboxing, least-privilege credentials, action validation, and human confirmation for consequential operations.
-
-## Limitations
-
-- Performance is sensitive to the harness, tool schemas, timeout budget, retry policy, context length, and history-rewrite behavior.
-- Browser and GUI interaction are not currently supported or evaluated.
-- The model may issue invalid calls, misread environment state, repeat actions, or stop before completing a task.
-- Benchmark scores obtained with different harnesses or budgets are not directly comparable.
-- The reported cost analysis estimates inference cost under a common pricing protocol; it does not include complete infrastructure, engineering, or operational costs.
-- This release does not provide a safety guarantee for autonomous use in high-impact domains. Users are responsible for task-specific evaluation and safeguards.
-
-## Resources
+---
 
-- **Model:** https://huggingface.co/Accio-Lab/Occamy-1.0
-- **GitHub:** https://github.com/Accio-Lab/occamy
-- **Training infrastructure:** https://github.com/Accio-Lab/Dressage
-- **Technical report and selected training data:** links will be added with the public release.
+## 7. License
 
-## Citation
+This repository is released under the [Apache License 2.0](LICENSE). See the Hugging Face model card for the terms that apply to the model weights.
 
-~~~bibtex
-@misc{accio2026occamy,
-  title        = {Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work},
-  author       = {{Accio Team}},
-  year         = {2026},
-  howpublished = {\url{https://huggingface.co/Accio-Lab/Occamy-1.0}}
-}
-~~~
+---
 
-## License
+## 8. Contact Us
 
-Occamy-1.0 is released under the Apache License 2.0. Please also review the license and acceptable-use terms of the base model.
+For questions or feedback, please open an [issue](https://github.com/Accio-Lab/occamy/issues).

ossutil_output/ossutil_report_20260827_094732.report

@@ -1 +0,0 @@
-# ossutil cp -r /root/models/Qwen3.6-35B-A3B_taskbed_sao_5node_sao-5node-20260826T004243Z_hf_iter124 oss://cogito-us-east/qingcheng/compaction_rl/ckpt_0825_soup2/Qwen3.6-35B-A3B_taskbed_sao_5node_sao-5node-20260826T004243Z_hf_iter124/ --update