Instructions to use endless-frontier/Fx-Work with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use endless-frontier/Fx-Work with Transformers:

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

pipe = pipeline("image-text-to-text", model="endless-frontier/Fx-Work")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("endless-frontier/Fx-Work")
model = AutoModelForMultimodalLM.from_pretrained("endless-frontier/Fx-Work", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use endless-frontier/Fx-Work with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "endless-frontier/Fx-Work"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "endless-frontier/Fx-Work",
		"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/endless-frontier/Fx-Work

SGLang

How to use endless-frontier/Fx-Work 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 "endless-frontier/Fx-Work" \
    --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": "endless-frontier/Fx-Work",
		"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 "endless-frontier/Fx-Work" \
        --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": "endless-frontier/Fx-Work",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use endless-frontier/Fx-Work with Docker Model Runner:
```
docker model run hf.co/endless-frontier/Fx-Work
```

Fx-Work / README.md

xinyuzhu

Update WorkGenesis citation with arXiv reference

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	---
	language:
	- en
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.6-35B-A3B
	tags:
	- qwen3.6
	- multimodal
	- mixture-of-experts
	- agentic-work
	- occupational-work
	- tool-use
	---

	# Fx-Work

	Fx-Work is a 35B-class multimodal language model for professional work in realistic file-and-tool environments. It is designed to interpret heterogeneous work materials, reason over operational constraints, use tools when required, and produce deliverables that satisfy explicit professional requirements.

	Fx-Work is built on Qwen3.6-35B-A3B and post-trained with 20K tool-interaction trajectories. The trajectories are generated from occupational work constructed with real-world artifacts, occupational knowledge, actionable requests, and itemized evaluation criteria. Execution-guided verification is used during data construction to identify and repair inconsistencies between the work request, reference materials, and rubric.

	## Main Results

	Fx-Work achieves the strongest results among the evaluated models at or below the 35B scale on all five reported metrics. It also surpasses DeepSeek-V4-Pro-Preview (1.6T) on four of the five metrics.

	<p align="center">
	<a href="https://huggingface.co/endless-frontier/Fx-Work/blob/main/main_figure_fx_work.pdf">
	<img src="https://huggingface.co/endless-frontier/Fx-Work/resolve/main/main_figure_fx_work.png"
	alt="Fx-Work benchmark results" width="100%">
	</a>
	</p>

	## Benchmark Results

	Results on GDPvalAA-v2, APEX-Agents-AA, and JobBench. GDPval Rubric is the normalized rubric score; GDPval Elo uses the August 4, 2026 snapshot; APEX-Agents reports pass@1; and JobBench reports the Main and Easy splits. AVG is the mean of normalized GDPval Elo, APEX-Agents-AA, and JobBench Main. `--` indicates that a result was not publicly available or was not tested. Bold marks the best value among the models at or below the 35B scale.

	\| Model \| Parameters \| GDPval Rubric \| GDPval Elo \| APEX-Agents pass@1 \| JobBench Main \| JobBench Easy \| AVG \|
	\|:--\|--:\|--:\|--:\|--:\|--:\|--:\|--:\|
	\| Frontier models \| \| \| \| \| \| \| \|
	\| Qwen3.8-Max \| 2.4T-A95B \| 91.53 \| 1739 \| 38.9 \| 52.57 \| 82.81 \| 51.14 \|
	\| Kimi-K3 \| 2.8T-A104B \| 91.64 \| 1687 \| 35.5 \| 43.81 \| 80.72 \| 46.22 \|
	\| GPT-5.5 \| -- \| 90.50 \| 1491 \| 29.9 \| 30.10 \| 78.22 \| 36.52 \|
	\| GLM-5.2 \| 753B-A40B \| 90.34 \| 1510 \| 29.7 \| 33.79 \| 75.49 \| 37.80 \|
	\| DeepSeek-V4-Pro-Preview \| 1.6T-A49B \| 87.17 \| 1304 \| 19.2 \| 18.90 \| 64.08 \| 26.10 \|
	\| DeepSeek-V4-Flash-Preview \| 284B-A13B \| 86.49 \| 1189 \| 15.2 \| 18.62 \| 63.13 \| 22.75 \|
	\| Models at or below 35B \| \| \| \| \| \| \| \|
	\| Nex-N2-mini \| 35B-A3B \| 69.96 \| 1065 \| 15.6 \| 9.39 \| 52.93 \| 17.74 \|
	\| Agents-A1 \| 35B-A3B \| 74.31 \| 877 \| 11.7 \| 7.19 \| 41.74 \| 12.58 \|
	\| Apodex-1.0-mini \| 35B-A3B \| 78.36 \| 973 \| 14.6 \| 7.97 \| 44.38 \| 15.40 \|
	\| Occamy-1.0 \| 35B-A3B \| 79.50 \| 1200 \| 20.5 \| 18.88 \| 65.49 \| 24.79 \|
	\| Qwen3.6-27B \| 27B \| 85.10 \| 1138 \| 16.6 \| 19.18 \| 64.59 \| 22.56 \|
	\| Qwen3.6-35B-A3B \| 35B-A3B \| 82.61 \| 1053 \| 13.4 \| 14.99 \| 56.02 \| 18.68 \|
	\| Fx-Work \| 35B-A3B \| 86.66 \| 1352 \| 25.2 \| 25.19 \| 70.06 \| 31.00 \|



	## Quickstart

	For streamlined integration, we recommend serving Fx-Work through an OpenAI-compatible API using SGLang or vLLM. The repository requires Hugging Face authentication when access is restricted.

	> [!Important]
	> Fx-Work uses a native context length of 262,144 tokens. If GPU memory is limited, reduce the context length; for long-horizon professional work, retaining at least 128K tokens is recommended.

	### SGLang

	[SGLang](https://github.com/sgl-project/sglang) provides an OpenAI-compatible server for high-throughput inference.

	```bash
	python -m sglang.launch_server \
	--model-path endless-frontier/Fx-Work \
	--port 8000 \
	--tp-size 8 \
	--mem-fraction-static 0.8 \
	--context-length 262144 \
	--reasoning-parser qwen3
	```

	### vLLM

	[vLLM](https://github.com/vllm-project/vllm) provides a high-throughput OpenAI-compatible server.

	```bash
	vllm serve endless-frontier/Fx-Work \
	--port 8000 \
	--tensor-parallel-size 8 \
	--max-model-len 262144 \
	--reasoning-parser qwen3
	```

	Both commands expose an endpoint at `http://localhost:8000/v1`. Tool execution is provided by the surrounding agent harness; the model server exposes generation and, where configured, tool-call responses.

	### Transformers

	```python
	import torch
	from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration

	model_id = "endless-frontier/Fx-Work"
	processor = AutoProcessor.from_pretrained(model_id)
	model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	)

	messages = [
	{
	"role": "user",
	"content": "Summarize the key risks and next actions in this work request.",
	}
	]
	inputs = processor.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_tensors="pt",
	return_dict=True,
	).to(model.device)

	with torch.inference_mode():
	output_ids = model.generate(**inputs, max_new_tokens=1024)

	new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]
	print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])
	```

	The processor supports the multimodal image and video message format provided by Qwen3.6.


	## Citation

	If you use Fx-Work, please cite:

	```bibtex
	@article{zhu2026workgenesis,
	title={WorkGenesis: Building the Worlds That Teach Agents to Work},
	author={Zhu, Xinyu and Liu, Fenyi and Cai, Yuzhu and Tang, Shuo and Ye, Rui and Zhang, Linfeng and Chen, Siheng},
	journal={arXiv preprint arXiv:2609.39325},
	year={2026}
	}
	```

	## License

	Fx-Work is released under the Apache 2.0 license, subject to the terms of the base model and third-party components. See the [Qwen3.6-35B-A3B license](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE) for the upstream terms.