--- 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.

Fx-Work benchmark results

## 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.