| --- |
| license: apache-2.0 |
| base_model: |
| - Hcompany/Holo3-35B-A3B |
| library_name: peft |
| pipeline_tag: image-text-to-text |
| tags: |
| - computer-use |
| - gui-agent |
| - multimodal |
| - action-model |
| - lora |
| - gguf |
| - sft |
| - trl |
| - mantis |
| model_name: Mantis |
| --- |
| |
| # Mantis |
|
|
| Mantis is an open-weights computer-use model checkpoint fine-tuned from |
| [Hcompany/Holo3-35B-A3B](https://huggingface.co/Hcompany/Holo3-35B-A3B). It is |
| trained on graded Mantis agent rollouts from Augur, using supervised fine-tuning |
| over real browser/workflow traces rather than generic chat data. |
|
|
| This release includes both the PEFT LoRA adapter and a ready-to-serve |
| `merged.Q8_0.gguf` artifact for llama.cpp-style serving. |
|
|
| ## Why This Is Better For Mantis |
|
|
| Mantis is not a general chat fine-tune. It is specialized for the slow |
| improvement loop of a computer-use agent: |
|
|
| - **Agent-native data**: trained from Mantis rollouts with task context, |
| model I/O, rewards, and action traces. |
| - **Computer-use alignment**: targets GUI/navigation behavior on realistic |
| browser tasks instead of instruction-following only. |
| - **Deployment-ready release**: ships the small adapter plus a merged Q8_0 GGUF, |
| so downstream serving stacks can either compose with the base model or run the |
| merged artifact directly. |
| - **Auditable provenance**: checkpoint id `sft-c3e0d799f432-f00fa0` ties this |
| release to the training data/config hash used by the Mantis trainer registry. |
| |
| The current internal frozen holdout gate did not establish a reliable promotion |
| over the base model, so this page does **not** claim a benchmark win over Holo3. |
| The value of this release is open access to the specialized Mantis adaptation, |
| its reproducible training pipeline, and its serving artifact. |
| |
| ## Files |
| |
| - `adapter_model.safetensors`: PEFT LoRA adapter weights. |
| - `adapter_config.json`: PEFT adapter configuration. |
| - `adapter.gguf`: converted adapter artifact. |
| - `merged.Q8_0.gguf`: merged full-model Q8_0 GGUF for direct serving. |
| - tokenizer/processor files copied from the training artifact. |
| - `training_args.bin`: trainer metadata from the SFT run. |
|
|
| ## Intended Use |
|
|
| Use this checkpoint for research and development of GUI agents, browser |
| automation agents, and Mantis-compatible computer-use systems. |
|
|
| This model may be useful when you need: |
|
|
| - a Holo3-derived checkpoint adapted to Mantis rollouts; |
| - an open adapter for further fine-tuning; |
| - a ready GGUF artifact for serving experiments; |
| - a transparent artifact from a champion/challenger training loop. |
|
|
| ## Limitations |
|
|
| - This model can make incorrect UI decisions and should not be allowed to take |
| high-impact actions without supervision. |
| - The released checkpoint is specialized for Mantis-style workflows; behavior |
| outside that domain may not improve over the base model. |
| - The internal gate found no reliable promotion over the base model on the |
| frozen holdout available at release time. |
| - Computer-use agents can interact with external systems. Use sandboxing, |
| allowlists, rate limits, and human approval for sensitive workflows. |
|
|
| ## Base Model And License |
|
|
| This model is fine-tuned from `Hcompany/Holo3-35B-A3B`, whose model card declares |
| the Apache-2.0 license. This release is also published under Apache-2.0 and |
| retains upstream attribution. |
|
|
| ## Training |
|
|
| - Base model: `Hcompany/Holo3-35B-A3B` |
| - Method: supervised fine-tuning with TRL |
| - Checkpoint id: `sft-c3e0d799f432-f00fa0` |
| - Data source: graded Mantis rollouts pulled from Augur |
| - Training stack: PEFT LoRA + TRL SFT |
|
|
| ## Citation |
|
|
| If you use the base model, cite Holo3: |
|
|
| ```bibtex |
| @misc{hai2025holo3modelfamily, |
| title={Holo3 - Open Foundation Models for Navigation and Computer Use Agents}, |
| author={H Company}, |
| year={2026}, |
| url={https://huggingface.co/Hcompany/Holo3-35B-A3B} |
| } |
| ``` |
|
|
| If you use the trainer stack, cite TRL: |
|
|
| ```bibtex |
| @software{vonwerra2020trl, |
| title={{TRL: Transformers Reinforcement Learning}}, |
| author={von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, |
| license={Apache-2.0}, |
| url={https://github.com/huggingface/trl}, |
| year={2020} |
| } |
| ``` |
|
|
