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)

# 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=40)
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

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.

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 provides an OpenAI-compatible server for high-throughput inference.

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 provides a high-throughput OpenAI-compatible server.

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

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:

@inproceedings{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},
  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 for the upstream terms.

Downloads last month: 16

Safetensors

Model size

36B params

Tensor type

BF16

Model tree for endless-frontier/Fx-Work

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(329)

this model

Quantizations

2 models