README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-4B
tags:
- agentic-coding
- reasoning
- tool-use
- on-device
- laptop-scale
- sft
- reinforcement-learning
library_name: transformers
pipeline_tag: text-generation
datasets:
- nvidia/Nemotron-Post-Training-Dataset-v2
---

# James Silberrad Brown Center for AI Research 

The **James Silberrad Brown Center for Artificial Intelligence (JSBCAI)** is an interdisciplinary research hub at San Diego State University dedicated to advancing artificial intelligence through foundational research, applied innovation, and student-driven inquiry.


# Aztec-Coder-4B

**An agentic coding model that runs on your laptop.**

Aztec-Coder-4B is a 4B-parameter model, fine-tuned from Qwen3.5-4B, that investigates bugs, edits files, runs commands, and verifies its own fixes in real software repositories. Agentic coding at this level has required 27B+ models. This one fits on a consumer GPU.

## Results

We reserved 121 real software bugs that the model never saw during training. Before fine-tuning, it solved 10% of them. After training, it solved **83%**, verified by running each project's hidden test suite. The model learned to fix bugs in general, not just the ones it practiced on.

| Benchmark | Qwen3.5-4B (base) | **Aztec-Coder-4B** | Aztec-Coder-4B-NVFP4 |
|---|---|---|---|
| **Generalization test** (121 unseen bugs, tests run to verify) | 10.1% | **82.9%** | 38.0%* |
| **Live-60** (60 real-world engineering tasks, solved end-to-end in containers) | 15.0% | **21.7%** | 15.0% |
| **Instruction-following** (IFEval) | 84.66 | **87.21** | 86.37 |
| MMLU-Pro | 64.0% | **70.0%** | 66.85% |
| Terminal-Bench 1.0 (core, 80 tasks) | 33.8% | **33.8%** | 18.8% |
| Terminal-Bench 2.1 (89 tasks) | coming soon | coming soon | coming soon |

The instruction-following score *improved* over the base model. The coding gains cost nothing on general quality. Gains of this kind usually trade one for the other.

*NVFP4 generalization: 12/32 on a 32-instance subset (the same slice our comparisons use). The quantization costs roughly half the generalization capability.

Our decontamination protocol is published with the model: none of these benchmark problems overlap the training data.

## What it does

- **Investigates and fixes bugs in real repositories.** The model explores a codebase, reads the failing code, writes a patch, and runs the tests to check itself, inside a sandboxed container.
- **Thinks before each action.** Like much larger reasoning models, it reasons between tool calls.
- **Runs on consumer hardware.** ~8GB VRAM in BF16; ~5GB as the NVFP4 quantized variant.

## How we trained it

Three stages, each with a plain-language summary:

1. **Seed demonstrations.** GLM-5.3, a frontier 744B open model, generated roughly 1,875 coding trajectories. We verified every one by running the actual tests before using it. These demonstrations taught our model the *format* of agentic coding: how to use tools, when to run tests, what a working solution looks like.

2. **Reinforcement learning on real bugs.** The model then practiced on 237 curated software engineering problems: ones it could sometimes solve, but not reliably. For each problem, the model repeatedly attempted a fix. Solutions that made the real hidden tests pass were reinforced; failures were not. This phase, 145 batches of on-policy GRPO, built the actual problem-solving ability.

3. **Generalization checks.** At every stage boundary, we re-tested the model on problems it had never trained on. The 10.1% to 82.9% jump above is the result.

### Training data

- **[NVIDIA Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2)**: a portion of its general instruction-following, structured-output, and tool-use data anchored the model's general capabilities.
- **Our distilled seed set**: ~1,875 verified coding trajectories generated by GLM-5.3 (Z.AI), each confirmed by running the real test suite before use.
- **Open SWE datasets**: a blend of open software-engineering problem sets provided the RL practice pool.
- **The RL phase used no static data at all**: the model generated fresh attempts each batch, and only test-verified outcomes became training signal.

## Usage

```python
from vllm import LLM
llm = LLM(model="jsbaicenter/Aztec-Coder-4B", max_model_len=131072)
```

Recommended sampling: `temperature 1.0, top_p 0.95`. The model uses the Qwen3.5 chat template with interleaved thinking (the `qwen3` reasoning parser in vLLM) and `qwen3_coder` tool-call format.

A quantized NVFP4 variant (~5GB) and an MTP-boosted speculative decoding head (for faster inference) are available from the same organization.

## Limitations

- A 4B model has 4B knowledge: obscure facts and extreme-domain reasoning still favor larger models.
- We tuned the agent loop for sandboxed container environments; other deployment contexts are untested.
- Safety behaviors come from the base model; the RL phase optimized test-passing only, with no safety-specific training. See the base model card.

## Lineage & credits

- **Base model**: [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache-2.0)
- **Demonstration generator**: GLM-5.3 (Z.AI), served locally
- **General training data**: [nvidia/Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2)
- **Quantization**: [NVIDIA ModelOpt](https://github.com/NVIDIA/Model-Optimizer) (NVFP4)
- **Technical report**: coming soon, covering the full methodology, the ablations, and our negative results on teacher-logprob distillation.

## License

Apache-2.0, matching the base model.

## Citation

```bibtex
@misc{jsbai_coder_4b,
  title={Aztec-Coder-4B: Agentic Coding at Laptop Scale via Teacher-Seeded RL},
  author={James Silberrad Brown Center for AI},
  year={2026},
  publisher={HuggingFace}
}
```