Instructions to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP")
model = AutoModelForMultimodalLM.from_pretrained("AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Use Docker

docker model run hf.co/AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

LM Studio
Jan

vLLM

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

SGLang

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Ollama:
```
ollama run hf.co/AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
```
Unsloth Desktop

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Docker Model Runner:
```
docker model run hf.co/AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M
```

Lemonade

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AMAImedia/Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.6-35B-A3B-occamy-1.0-BF16-GGUF-MTP / README.md

AMAImedia

Update README.md

dedcf1b verified 3 days ago

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model:
	- Qwen/Qwen3.6-35B-A3B
	- Accio-Lab/occamy-1.0
	tags:
	- agent
	- agentic
	- co-work
	- tool-use
	- long-context
	- mixture-of-experts
	- coding
	- 35B-A3B
	- Qwen3_6
	- Qwen3.6
	- Qwen3.6-35B-A3B
	language:
	- en
	- ru
	- zh
	- vi
	- kk
	- ja
	- af
	- am
	- ar
	- as
	- ast
	- az
	- be
	- bg
	- bn
	- bs
	- ca
	- ceb
	- ckb
	- cs
	- cy
	- da
	- de
	- el
	- es
	- et
	- eu
	- fa
	- ff
	- fi
	- fil
	- fr
	- ga
	- gl
	- gn
	- gu
	- ha
	- he
	- hi
	- hr
	- hu
	- hy
	- id
	- ig
	- is
	- it
	- jv
	- ka
	- kam
	- kea
	- km
	- kmr
	- kn
	- ko
	- ky
	- lb
	- lg
	- ln
	- lo
	- lt
	- luo
	- lv
	- mi
	- mk
	- ml
	- mn
	- mr
	- ms
	- mt
	- mvy
	- my
	- ne
	- nl
	- "no"
	- nso
	- ny
	- oc
	- om
	- "or"
	- pa
	- pl
	- ps
	- pt
	- qxp
	- ro
	- rw
	- sd
	- sk
	- skr
	- sl
	- sn
	- so
	- sr
	- sv
	- sw
	- ta
	- te
	- tg
	- th
	- ti
	- tk
	- tr
	- ug
	- uk
	- umb
	- ur
	- uz
	- wo
	- xh
	- yo
	- yue
	- zu
	---

	<!-- apex-banner -->
	<div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
	<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation funds the next large quant.</h2>
	<p style="font-size: 16px; margin: 0 0 15px 0;">I host free GGUF or MoE quants as independent research.<br>
	Local hardware: <b>Mechrevo Kuangshi GM7AG0M</b> — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.<br>
	Good for imatrix and 0.6–35B-class work in RAM. <b>9B+ and searches need rented H200/Blackwell</b>, typically $100 per quant.</p>
	<p style="font-size: 20px; margin: 0;">
	<a href="https://boosty.to/amaimediacom" style="color: white; text-decoration: underline;">🎉 Boosty🦄</a> \|
	<a href="https://donatex.gg/widgets/donation-goal/89bc59e8-b69c-467a-9aa0-5e1c3f8842d6" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee🦄</a> \|
	<a href="https://www.donationalerts.com/r/djbionicl" style="color: white; text-decoration: underline;">⭐ DonationAlerts🦄</a>
	</p>
	<p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Thanks to Hugging Face for extra storage.🦄</p>
	</div>

	---

	## NOESIS / AMAImedia

	Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform
	(framework: Deterministic Hybrid Control Framework for Frozen Neural Operators - DHCF-FNO).

	- Founder: Ilia Bolotnikov
	- Organization: [AMAImedia.com](https://www.amaimedia.com)
	- X (Twitter): [@AMAImediacom](https://x.com/AMAImediacom)
	- LinkedIn: [Ilia Bolotnikov](https://www.linkedin.com/in/ilia-bolotnikov)
	- Telegram: [@AMAImediacom](https://t.me/AMAImediacom)
	- Release date: 2026-09-21

	## AMAImedia

	- Original repository: [Accio-Lab/occamy-1.0](https://huggingface.co/Accio-Lab/occamy-1.0)
	- GGUF: [Accio-Lab/occamy-1.0-GGUF](https://huggingface.co/Accio-Lab/occamy-1.0-GGUF)
	- MTP: [Accio-Lab/occamy-1.0-MTP](https://huggingface.co/Accio-Lab/occamy-1.0-MTP)
	![occamy-1.0](assets/AMAImedia-occamy-1.0.jpg)

	---

	<div align="center">
	<picture>
	<img src="assets/accio.svg" width="34%" alt="Accio">
	</picture>

	<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>

	<hr>

	<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="https://arxiv.org/pdf/2609.11977"><img alt="Technical Report" src="https://img.shields.io/badge/%F0%9F%93%84%20Report-Occamy--1.0-B31B1B"></a>
	<a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue"></a>
	</div>

	<p align="center">
	<a href="https://accio-lab.github.io/occamy/">Project Website</a>  \|
	<a href="https://huggingface.co/Accio-Lab/Occamy-1.0">Model Weights</a>  \|
	<a href="https://github.com/Accio-Lab/Dressage">Training Framework</a>
	</p>

	## 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](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.

	### 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](https://github.com/Accio-Lab/Dressage).

	> [!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.

	## 2. Model Summary

	<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>

	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

	<div align="center">
	<picture>
	<img src="assets/occamy-main-results.svg" width="100%" alt="Occamy-1.0 results on co-work, tool-use, coding, and business benchmarks">
	</picture>
	</div>

	### Full Evaluation

	<table>
	<thead>
	<tr>
	<th rowspan="2" align="left" bgcolor="#EAF6F3">Benchmark</th>
	<th colspan="6" align="center" bgcolor="#EAF6F3"><font color="#087F6A">35B-A3B Models</font></th>
	<th colspan="4" align="center" bgcolor="#EAF6F3"><font color="#087F6A">Large-scale Models</font></th>
	</tr>
	<tr>
	<th align="center" bgcolor="#087F6A"><font color="#FFFFFF">Occamy-1.0</font></th>
	<th align="center">Qwen3.6<br>35B-A3B</th>
	<th align="center">Agents-A1</th>
	<th align="center">Nex-N2-mini</th>
	<th align="center">BigBang-1.0</th>
	<th align="center">Ornith-1.5</th>
	<th align="center">GPT-5.6 Sol</th>
	<th align="center">Qwen3.8-Max</th>
	<th align="center">DeepSeek V4<br>Pro (0813)</th>
	<th align="center">GLM-5.2</th>
	</tr>
	</thead>
	<tbody>
	<tr><td colspan="11" bgcolor="#DDF1EC"><font color="#087F6A"><strong>Co-work</strong></font></td></tr>
	<tr><td>Claw-Eval (average)</td><td align="right"><strong>82.20</strong></td><td align="right">69.50</td><td align="right"><u>69.90</u></td><td align="right">66.60</td><td align="right">63.50</td><td align="right">64.40</td><td align="right"><u>81.80</u></td><td align="right"><strong>83.92</strong></td><td align="right">81.70</td><td align="right">81.60</td></tr>
	<tr><td>Claw-Eval (Pass³)</td><td align="right"><strong>71.40</strong></td><td align="right"><u>54.80</u></td><td align="right">41.70</td><td align="right">37.00</td><td align="right">40.20</td><td align="right">48.70</td><td align="right">68.90</td><td align="right"><u>73.68</u></td><td align="right"><strong>74.50</strong></td><td align="right">68.30</td></tr>
	<tr><td>WildClawBench</td><td align="right"><strong>49.16</strong></td><td align="right">40.40</td><td align="right">30.73</td><td align="right">30.31</td><td align="right">32.87</td><td align="right"><u>45.91</u></td><td align="right"><strong>67.20</strong></td><td align="right"><u>54.42</u></td><td align="right">37.30</td><td align="right">52.14</td></tr>
	<tr><td>CommerceAgentBench</td><td align="right"><u>37.38</u></td><td align="right">19.60</td><td align="right">9.30</td><td align="right">16.80</td><td align="right">30.80</td><td align="right"><strong>37.40</strong></td><td align="right"><strong>49.50</strong></td><td align="right"><u>46.30</u></td><td align="right">43.30</td><td align="right">39.30</td></tr>
	<tr><td>Business Arena</td><td align="right"><strong>$79,868</strong></td><td align="right">$44,751</td><td align="right">$33,626</td><td align="right">$13,325</td><td align="right">$56,477</td><td align="right"><u>$66,292</u></td><td align="right"><strong>$168,867</strong></td><td align="right"><u>$89,423</u></td><td align="right">$40,804</td><td align="right">$55,742</td></tr>
	<tr><td>GDPval<sup>†</sup></td><td align="right"><strong>1,128</strong></td><td align="right"><u>1,004</u></td><td align="right">869</td><td align="right">999</td><td align="right">951</td><td align="right">855</td><td align="right"><strong>1,741</strong></td><td align="right"><u>1,640</u></td><td align="right">1,500</td><td align="right">1,452</td></tr>
	<tr><td>OfficeQA Pro</td><td align="right"><u>48.10</u></td><td align="right">39.10</td><td align="right">23.30</td><td align="right">46.60</td><td align="right">43.60</td><td align="right"><strong>59.40</strong></td><td align="right"><strong>74.40</strong></td><td align="right"><u>69.20</u></td><td align="right">51.20</td><td align="right">66.20</td></tr>
	<tr><td>τ³-Bench (Banking)</td><td align="right"><strong>37.10</strong></td><td align="right">11.90</td><td align="right">7.20</td><td align="right"><u>25.80</u></td><td align="right">10.30</td><td align="right">21.70</td><td align="right"><u>46.90</u></td><td align="right"><strong>54.60</strong></td><td align="right">44.30</td><td align="right">37.10</td></tr>
	<tr><td colspan="11" bgcolor="#DDF1EC"><font color="#087F6A"><strong>Tool calling</strong></font></td></tr>
	<tr><td>AutomationBench (Pass¹)</td><td align="right"><strong>27.60</strong></td><td align="right">7.50</td><td align="right">2.20</td><td align="right">5.70</td><td align="right">14.80</td><td align="right"><u>18.50</u></td><td align="right"><strong>45.50</strong></td><td align="right"><u>43.50</u></td><td align="right">32.00</td><td align="right">28.00</td></tr>
	<tr><td>AutomationBench (partial)</td><td align="right"><strong>69.10</strong></td><td align="right">39.40</td><td align="right">14.70</td><td align="right">27.90</td><td align="right">47.40</td><td align="right"><u>58.00</u></td><td align="right"><strong>81.20</strong></td><td align="right"><strong>81.20</strong></td><td align="right">59.70</td><td align="right"><u>70.00</u></td></tr>
	<tr><td>BFCL v4</td><td align="right"><u>65.40</u></td><td align="right">63.19</td><td align="right">57.23</td><td align="right">62.81</td><td align="right">57.86</td><td align="right"><strong>68.51</strong></td><td align="right">64.33</td><td align="right"><strong>73.65</strong></td><td align="right">67.10</td><td align="right"><u>70.33</u></td></tr>
	<tr><td>VitaBench</td><td align="right"><u>41.75</u></td><td align="right">34.25</td><td align="right">37.00</td><td align="right">26.25</td><td align="right"><strong>46.00</strong></td><td align="right">40.25</td><td align="right">46.75</td><td align="right"><u>52.25</u></td><td align="right"><strong>53.50</strong></td><td align="right">43.75</td></tr>
	<tr><td colspan="11" bgcolor="#DDF1EC"><font color="#087F6A"><strong>Coding</strong></font></td></tr>
	<tr><td>Terminal-Bench 2.1</td><td align="right">59.00</td><td align="right">49.50</td><td align="right">41.60</td><td align="right"><u>60.70<sup></sup></u></td><td align="right">33.70</td><td align="right"><strong>67.80<sup></sup></strong></td><td align="right"><strong>88.80</strong></td><td align="right">81.30<sup></sup></td><td align="right"><u>87.90<sup></sup></u></td><td align="right">82.70</td></tr>
	<tr><td colspan="11" bgcolor="#DDF1EC"><font color="#087F6A"><strong>Instruction following</strong></font></td></tr>
	<tr><td>IFEval</td><td align="right"><u>91.53</u></td><td align="right">86.90</td><td align="right"><strong>91.60</strong></td><td align="right"><strong>91.60</strong></td><td align="right">90.50</td><td align="right">81.80</td><td align="right"><u>95.00</u></td><td align="right"><strong>95.02</strong></td><td align="right">93.74</td><td align="right">93.89</td></tr>
	</tbody>
	</table>

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

	### Cost-Performance

	<div align="center">
	<picture>
	<img src="assets/aggregate-cost-performance.png" width="100%" alt="Aggregate inference cost versus normalized performance across four co-work benchmarks">
	</picture>
	</div>

	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:

	```text
	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](https://github.com/Accio-Lab/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](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 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 \
	--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](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 \
	--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

	```python
	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](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.

	---

	## 7. License

	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.

	---

	## 8. Contact Us

	For questions or feedback, please open an [issue](https://github.com/Accio-Lab/occamy/issues).