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| base_model: | |
| - willcb/Qwen3-14B | |
| license: apache-2.0 | |
| datasets: | |
| - Danau5tin/terminal-tasks | |
| tags: | |
| - agent | |
| - code | |
| - multi-agent | |
| # Orca-Agent-v0.1 | |
|  | |
| In depth details behind the training, including the training code, is **all open sourced [here](https://github.com/Danau5tin/Orca-Agent-RL)** | |
| ## Description | |
| Orca-Agent-v0.1 is an orchestration agent that acts as the brain of the operation - it receives the user's task but never touches code directly. Instead, it: | |
| - Analyses the task and breaks it into focused subtasks | |
| - Dispatches explorer agents to understand the system | |
| - Delegates implementation work to coder agents with precise instructions | |
| - Verifies all changes through additional explorer agents | |
| - Maintains the context store with all discovered knowledge | |
| ## Performance | |
| | Orchestrator | Subagent | Terminal Bench | | |
| |--------------|----------|--------| | |
| | Qwen3-Coder-480B | Qwen3-Coder-480B | 19.7% | | |
| | Orca-Agent-v0.1-14B | Qwen3-Coder-30B | 18.25% | | |
| | Qwen3-14B | Qwen3-Coder-30B | 7.0% | | |
| This places Orca-Agent-v0.1 (14B) + Qwen3-Coder-Flash (30B MoE), within striking distance of [Qwen3-Coder-480B](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) using the same architecture which placed #26 on TerminalBench when it was published recently in [my other project](https://github.com/Danau5tin/multi-agent-coding-system). | |
| ## Example model output | |
| ```xml | |
| <think> | |
| Okay, now with task_003 identifying a backup, the next step would be to attempt recovery using this identified file. | |
| </think> | |
| <task_create> | |
| agent_type: 'coder' | |
| title: 'Attempt recovery using the identified backup file' | |
| description: | | |
| Execute the following steps to recover using the backup identified in task_003: | |
| 1. Move the identified backup file (/tmp/terraform_work/.terraform.tfstate.tmp) to the target location (/infrastructure/recovered_state.json). | |
| 2. Verify the operation by checking: | |
| - The existence of the new file at the target location | |
| - The size matches the original backup file | |
| - The file permissions are set appropriately (rw-r--r--) | |
| max_turns: 10 | |
| context_bootstrap: | |
| - path: '/tmp/terraform_work/.terraform.tfstate.tmp' | |
| reason: 'The backup file identified in task_003' | |
| context_refs: | |
| - 'task_003' | |
| </task_create> | |
| ``` | |
| ## Model training Overview | |
| - Full fine-tune of Qwen3-14b | |
| - 32x H100s | |
| - 16x for training | |
| - 8x inference for Orca-Agent | |
| - 8x inference for subagent (Qwen3-Coder-30B-A3B) | |
| - Trained with GRPO + curriculum learning | |
| - Batch size 256, 64 rollouts per task | |
| - More details [here](https://github.com/Danau5tin/Orca-Agent-RL) | |
| ## Serving model | |
| **vLLM** | |
| ```bash | |
| vllm serve Danau5tin/Orca-Agent-v0.1 | |
| ``` | |
| **SGLang** | |
| ```bash | |
| python -m sglang.launch_server \ | |
| --model-path Danau5tin/Orca-Agent-v0.1 | |
| ``` | |
| The agent's orchestration code can be found [here](https://github.com/Danau5tin/multi-agent-coding-system). |