JIT-Agent/jit-27b

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
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:

<<<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, 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

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:

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

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:

--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.

License

The checkpoint is released under the Apache License 2.0. It is derived from Qwen/Qwen3.6-27B, which is also released under Apache 2.0.