Occamy-1.0

Open Pareto-frontier 35B Intelligence for Co-work

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Project Website  |  Model Weights  |  Training Framework

1. Model Introduction

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 checkpoint, Occamy concentrates further training on reliable execution, persistent state tracking, recovery, and follow-through rather than relearning general capabilities from scratch.

Key Features

• 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.

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.

2. Model Summary

Architecture	Mixture-of-Experts causal model with vision encoder
Total Parameters	35B
Activated Parameters	3B
Number of Layers	40
Number of Experts	256
Activated Experts	8 routed + 1 shared
Base Architecture Context	262,144 tokens
SFT Sequence Length	131,072 tokens
Starting Checkpoint	Qwen3.6-35B-A3B
Post-training	Full-parameter SFT, HDPO, model merging, and SAO

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.

3. Evaluation Results

Full Evaluation

Benchmark	35B-A3B Models						Large-scale Models
	Occamy-1.0	Qwen3.6 35B-A3B	Agents-A1	Nex-N2-mini	BigBang-1.0	Ornith-1.5	GPT-5.6 Sol	Qwen3.8-Max	DeepSeek V4 Pro (0813)	GLM-5.2
Co-work
Claw-Eval (average)	82.20	69.50	69.90	66.60	63.50	64.40	81.80	83.92	81.70	81.60
Claw-Eval (Pass³)	71.40	54.80	41.70	37.00	40.20	48.70	68.90	73.68	74.50	68.30
WildClawBench	49.16	40.40	30.73	30.31	32.87	45.91	67.20	54.42	37.30	52.14
CommerceAgentBench	37.38	19.60	9.30	16.80	30.80	37.40	49.50	46.30	43.30	39.30
Business Arena	$79,868	$44,751	$33,626	$13,325	$56,477	$66,292	$168,867	$89,423	$40,804	$55,742
GDPval†	1,128	1,004	869	999	951	855	1,741	1,640	1,500	1,452
OfficeQA Pro	48.10	39.10	23.30	46.60	43.60	59.40	74.40	69.20	51.20	66.20
τ³-Bench (Banking)	37.10	11.90	7.20	25.80	10.30	21.70	46.90	54.60	44.30	37.10
Tool calling
AutomationBench (Pass¹)	27.60	7.50	2.20	5.70	14.80	18.50	45.50	43.50	32.00	28.00
AutomationBench (partial)	69.10	39.40	14.70	27.90	47.40	58.00	81.20	81.20	59.70	70.00
BFCL v4	65.40	63.19	57.23	62.81	57.86	68.51	64.33	73.65	67.10	70.33
VitaBench	41.75	34.25	37.00	26.25	46.00	40.25	46.75	52.25	53.50	43.75
Coding
Terminal-Bench 2.1	59.00	49.50	41.60	60.70*	33.70	67.80*	88.80	81.30*	87.90*	82.70
Instruction following
IFEval	91.53	86.90	91.60	91.60	90.50	81.80	95.00	95.02	93.74	93.89

Within each size group, bold denotes the best result and underlining denotes the second-best result. ^* Official model-card or Artificial Analysis result. ^† Reproduced on the public task release.

Cost-Performance

Across Claw-Eval, WildClawBench, AutomationBench, and GDPval, Occamy-1.0 lies near the low-cost knee of the empirical Pareto frontier. Relative to its Qwen3.6-35B-A3B starting checkpoint, it delivers a large aggregate capability gain with only a modest change in measured per-task inference cost. Benchmark scores are equally weighted after per-benchmark min-max normalization, and costs are macro-averaged per task under the frozen pricing protocol used in the report.

4. Training Recipe

Occamy uses staged specialization and consolidation:

Qwen3.6-35B-A3B
  ├─ Marathon Expert: SFT → HDPO ┐
  └─ Sprint Expert: SFT          ├─ Uniform merge → SAO → Occamy-1.0

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.

The deduplicated SFT union across both experts is:

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

Training tasks are grounded in executable environments with observable state transitions and task-level grading. The open-source Dressage stack provides multi-harness execution, token-exact trajectory capture, sandbox integration, and multi-segment conversion for reinforcement learning.

5. Deployment

Occamy-1.0 keeps the Qwen3.6-35B-A3B architecture, so the upstream deployment recipe 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 upstream model card recommends SGLang 0.5.10 or newer for the Qwen3.6 architecture.

python -m sglang.launch_server \
  --model-path Accio-Lab/Occamy-1.0 \
  --port 8000 \
  --tp-size 8 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

vLLM

The upstream model card recommends vLLM 0.19.0 or newer for the Qwen3.6 architecture.

vllm serve Accio-Lab/Occamy-1.0 \
  --port 8000 \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Both commands expose an OpenAI-compatible endpoint at http://localhost:8000/v1.

6. Model Usage

from openai import OpenAI

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 this repository, fix the failing test, and explain the change.",
        }
    ],
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    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 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.

Agent Frameworks

Occamy was trained and evaluated across multiple harnesses, including OpenClaw, Hermes Agent, and Accio Work. It can be integrated with other tool-using agent frameworks through the same OpenAI-compatible API.

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7. License

This repository is released under the Apache License 2.0. See the Hugging Face model card for the terms that apply to the model weights.

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8. Contact Us

For questions or feedback, please open an issue.