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"

⚡ Each donation funds the next large quant.

I host free GGUF or MoE quants as independent research.
Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.
Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant.

🎉 Boosty🦄 | ☕ Buy Me a Coffee🦄 | ⭐ DonationAlerts🦄

💚 Thanks to Hugging Face for extra storage.🦄

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
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @AMAImediacom
Release date: 2026-09-18

AMAImedia

Original repository: Accio-Lab/occamy-1.0
GGUF: Accio-Lab/occamy-1.0-GGUF
MTP: Accio-Lab/occamy-1.0-MTP

Occamy-1.0

Open Pareto-frontier 35B Intelligence for Co-work

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

Occamy-1.0 results on co-work, tool-use, coding, and business benchmarks

Full Evaluation

Benchmark	35B-A3B Models						Large-scale Models
Benchmark	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

Aggregate inference cost versus normalized performance across four co-work benchmarks

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.