| --- |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| model_name: feyospace-v1-small |
| base_model: |
| - Qwen/Qwen3.8-27B |
| tags: |
| - qwen3_5 |
| - conversational |
| - safetensors |
| - cybersecurity |
| --- |
| |
| # Feyospace-v1-small |
|
|
| > [!NOTE] |
| > This is the initial publicly released checkpoint in the Feyospace-v1 model family. Additional models and datasets will be released in future updates. |
|
|
| ## Overview |
|
|
| Feyospace-v1-small is a 28B-parameter model that follows the Qwen3.5 architecture and is post-trained from [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B). |
|
|
| It is developed as part of the Feyospace-v1 project for research on cyber agents, long-context reasoning, and agentic system development. |
|
|
| The model is released in BF16 safetensors format and includes the tokenizer and chat template required for inference. It supports a context length of up to 262,144 tokens. |
|
|
| For a detailed description of the training framework and data construction process, please refer to our paper: |
|
|
| **[Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models](https://arxiv.org/abs/2609.08418)** |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |---|---| |
| | Model ID | `feyospace/feyospace-v1-small` | |
| | Base model | [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) | |
| | Architecture | `Qwen3_5ForConditionalGeneration` | |
| | Training stage | Post-training | |
| | Number of parameters | 28B | |
| | Tensor type | BF16 | |
| | Maximum context length | 262,144 tokens | |
| | Model format | Safetensors | |
|
|
| ## Intended Use |
|
|
| This model is intended for: |
|
|
| - Research on cyber agents and language models |
| - Authorized defensive-security research and education |
| - Benchmarking and evaluation in controlled environments |
| - Long-context reasoning and agentic system development |
| - Further fine-tuning and experimentation |
|
|
| The model should only be used in systems and environments for which the user has appropriate authorization. |
|
|
| ## Quickstart |
|
|
| Install the required dependencies: |
|
|
| ```bash |
| pip install -U torch transformers accelerate |
| ``` |
|
|
| A recent version of Transformers with support for the Qwen3.5 architecture is recommended. No specific version is pinned in this model card. |
|
|
| The following example demonstrates text-only inference: |
|
|
| ```python |
| from transformers import AutoModelForMultimodalLM, AutoProcessor |
| |
| model_id = "feyospace/feyospace-v1-small" |
| |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForMultimodalLM.from_pretrained( |
| model_id, |
| torch_dtype="auto", |
| device_map="auto", |
| ) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| { |
| "type": "text", |
| "text": "Explain how to validate a security fix in an authorized laboratory environment.", |
| } |
| ], |
| } |
| ] |
| |
| inputs = processor.apply_chat_template( |
| messages, |
| add_generation_prompt=True, |
| tokenize=True, |
| return_dict=True, |
| return_tensors="pt", |
| ).to(model.device) |
| |
| generated_ids = model.generate( |
| **inputs, |
| max_new_tokens=512, |
| ) |
| |
| generated_ids = generated_ids[0][inputs["input_ids"].shape[-1]:] |
| |
| response = processor.decode( |
| generated_ids, |
| skip_special_tokens=True, |
| ) |
| |
| print(response) |
| ``` |
|
|
| ## Training and Data |
|
|
| The Feyospace-v1 project presents a data-centric framework for training open-weight cyber agents. The associated data engine constructs executable and resettable environments covering tasks such as: |
|
|
| - Repository-level coding |
| - Vulnerability reproduction |
| - Capture-the-Flag tasks |
| - Linux kernel history |
| - Exploit development |
| - Firmware analysis |
| - Device-backed system tasks |
|
|
| Candidate trajectories are retained only after execution verification and evidence auditing. The paper describes 164,269 audited trajectories used for long-context supervised fine-tuning. |
|
|
| For more information about the training systems and data pipeline, please see the [Feyospace-v1 paper](https://arxiv.org/abs/2609.08418). |
|
|
| ## Limitations and Safety |
|
|
| This model is a research release and may produce inaccurate, incomplete, or unsafe outputs. Its responses should be reviewed by qualified users and validated in isolated, authorized environments. |
|
|
| The model must not be used for: |
|
|
| - Unauthorized access to systems or networks |
| - Real-world exploitation without permission |
| - Malware deployment or harmful cyber activity |
| - Automated high-impact security decisions without human oversight |
|
|
| Users are responsible for complying with all applicable laws, regulations, and organizational policies. |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite: |
|
|
| ```bibtex |
| @misc{feyospace2026, |
| title = {Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models}, |
| author = {Li, Zongjie and others}, |
| year = {2026}, |
| eprint = {2609.08418}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AI}, |
| url = {https://arxiv.org/abs/2609.08418} |
| } |
| ``` |
|
|
