| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.6-27B |
| library_name: transformers |
| language: |
| - en |
| - zh |
| tags: |
| - agents |
| - agentic-systems |
| - harness-generation |
| - tool-use |
| - code-generation |
| - jit-agent |
| --- |
| |
| # JIT-Agent-27B |
|
|
| **JIT-Agent-27B** is a harness intelligence model for synthesizing executable, |
| task-conditioned agent harnesses. Given a task, an available tool registry, a |
| shared runtime protocol, and natural-language descriptions of reference |
| harnesses, the model generates a complete operational scaffold tailored to the |
| task at hand. |
|
|
| Instead of directly solving the task, JIT-Agent writes the system through which |
| another foundation model acts: how it maintains memory, forms and updates plans, |
| executes actions, and orchestrates tools and skills. |
|
|
| ## Checkpoint Overview |
|
|
| This repository contains the initial research release of JIT-Agent-27B. The |
| checkpoint builds on the Stage-I harness-customization model and is further |
| trained through distillation from the final research checkpoint. |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |---|---| |
| | Base model | [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) | |
| | Parameters | 27.36B | |
| | Weight precision | BF16 | |
| | Architecture context length | 262,144 tokens | |
| | Recommended serving context | 163,840 tokens | |
| | Primary input | Task, tools, protocol, and reference-harness descriptions | |
| | Primary output | Four Python modules and one YAML prompt configuration | |
|
|
| JIT-Agent generates harnesses under a fixed four-module protocol: |
|
|
| - `memory.py`: constructs and updates the agent's working context; |
| - `planning.py`: forms directives and manages plan state; |
| - `action.py`: implements the task-execution loop; |
| - `tool_policy.py`: controls tool and skill exposure; |
| - `prompt.yaml`: defines the prompts consumed by the generated modules. |
|
|
| The model emits these files using the following tagged format: |
|
|
| ```text |
| <<<PYTHON_MEMORY>>> |
| ... |
| <<<END_PYTHON_MEMORY>>> |
| |
| <<<PYTHON_PLANNING>>> |
| ... |
| <<<END_PYTHON_PLANNING>>> |
| |
| <<<PYTHON_ACTION>>> |
| ... |
| <<<END_PYTHON_ACTION>>> |
| |
| <<<PYTHON_TOOL_POLICY>>> |
| ... |
| <<<END_PYTHON_TOOL_POLICY>>> |
| |
| <<<YAML>>> |
| ... |
| <<<END_YAML>>> |
| ``` |
|
|
| ## Recommended Usage |
|
|
| The checkpoint is designed to be used with the |
| [JIT-Agent runtime](https://github.com/bingreeky/JIT), which constructs the full |
| generation prompt, validates the structured output, installs the resulting |
| harness, and executes it against an off-the-shelf agentic model. |
|
|
| ### 1. Set up the runtime |
|
|
| ```bash |
| git clone https://github.com/bingreeky/JIT.git |
| cd JIT |
| |
| conda env create -f environment.yml |
| conda activate jit |
| ``` |
|
|
| ### 2. Serve the checkpoint |
|
|
| The repository provides a vLLM launcher with the recommended serving settings: |
|
|
| ```bash |
| MODEL=JIT-Agent/jit-27b \ |
| TP=4 \ |
| bash scripts/serve_meta_model.sh |
| ``` |
|
|
| This exposes an OpenAI-compatible endpoint at `http://localhost:8000/v1`. |
|
|
| ### 3. Generate and execute a task-conditioned harness |
|
|
| ```bash |
| python -m scripts.run_jit \ |
| --bench xbench \ |
| --meta-model jit \ |
| --meta-base http://localhost:8000/v1 \ |
| --harness-refs desc \ |
| --max-samples 5 |
| ``` |
|
|
| The released checkpoint should be used with description references: |
|
|
| ```text |
| --harness-refs desc |
| ``` |
|
|
| In this mode, the model receives natural-language design descriptions of the |
| reference harnesses rather than their source code. This is also the default mode |
| of the released runtime. |
|
|
| For best-of-N inference, the runtime generates three candidate harnesses at |
| temperature 1.0 and selects one before task execution. |
|
|
| ## Intended Use |
|
|
| JIT-Agent-27B is intended for research on: |
|
|
| - task-adaptive agent architecture generation; |
| - model–harness co-design; |
| - modular agent runtimes; |
| - memory, planning, action, and tool-policy composition; |
| - best-of-N harness synthesis; |
| - harness transfer across executor models and task domains. |
|
|
| It is a harness generator rather than a general-purpose chat model. Direct |
| chat-style prompting without the accompanying protocol and runtime context is |
| unlikely to produce valid executable harnesses. Also see [here](https://huggingface.co/papers/2608.25593). |
|
|
| ## License |
|
|
| The checkpoint is released under the Apache License 2.0. It is derived from |
| [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B), which is also |
| released under Apache 2.0. |
|
|
