README.md

---
library_name: peft
base_model: Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash
license: apache-2.0
tags:
  - reasoning
  - coding
  - agentic
  - qwen3.5
  - deepseek-v4
  - lora
  - qlora
  - sft
  - chain-of-thought
  - galaxy-nexus
language:
  - en
  - zh
  - ko
  - ja
  - es
  - ru
pipeline_tag: text-generation
---

# Galaxy Nexus Coder-Reasoner v1

**A QLoRA fine-tune of Qwen3.5-9B-DeepSeek-V4-Flash for agentic coding + reasoning.**

## Model Details

| | |
|---|---|
| **Base model** | [Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash](https://huggingface.co/Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash) |
| **Architecture** | Qwen3.5 (hybrid Gated DeltaNet + Gated Attention, `qwen3_5_text`) |
| **Parameters** | 9B total / 29M trainable (LoRA, 0.53%) |
| **Fine-tuning** | QLoRA (4-bit NF4, double quant, BF16 compute) |
| **LoRA config** | r=16, α=32, dropout=0.05, all linear layers |
| **Training data** | 2,893 DeepSeek-R1 reasoning traces (code + math) |
| **Hardware** | NVIDIA L4 (24GB, Ada Lovelace) on AWS g6.xlarge |
| **Runtime** | SGLang v0.5.15 (RadixAttention + FP8 + multi-LoRA) |
| **License** | Apache 2.0 |

## Training Data

This adapter was trained on a curated mix of **DeepSeek-R1 chain-of-thought reasoning traces** from the [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts) dataset:

- **Code subset** (3,000 sampled): DeepSeek-R1 reasoning traces on competitive programming problems (Python + C++), sourced from CodeForces
- **Math subset** (2,000 sampled): DeepSeek-R1 reasoning traces on mathematical problems

After length filtering (≤8192 tokens), **2,893 examples** were used for training.

## Why This Model

The base model (Qwen3.5-9B-DeepSeek-V4-Flash) is already a reasoning model distilled from DeepSeek-V4. We fine-tuned it further to:

1. **Specialize for agentic coding** — AWS, Terraform, Docker, Kubernetes, Linux, Python
2. **Reinforce step-by-step reasoning** before code generation
3. **Align to the Galaxy Nexus system prompt** for consistent agent behavior

## Training Results

| Metric | Step 1 | Step 50 | Step 100 |
|--------|--------|---------|----------|
| **Loss** | 0.8839 | 0.5212 | ~0.50 |
| **Token accuracy** | 73.8% | 83.2% | ~84% |
| **Improvement** | — | -41% loss | -43% loss |

Loss decreased 43% over 100 steps, with token accuracy climbing from 73.8% to ~84%.

## Benchmark Results

Base model (Qwen3.5-9B-DeepSeek-V4-Flash) scored **10/11 (91%)** on our agentic-coding benchmark suite covering Python, Docker, Terraform, Kubernetes, Linux, and reasoning tasks.

## Serving with SGLang

```bash
# Serve base + adapter via SGLang multi-LoRA
python3 -m sglang.launch_server \
  --model-path Qwen3.5-9B-DeepSeek-V4-Flash \
  --enable-lora \
  --max-lora-rank 16 \
  --lora-paths gnx-coder=/path/to/galaxy-coder-reasoner-v1 \
  --quantization fp8 \
  --kv-cache-dtype fp8 \
  --trust-remote-code \
  --mem-fraction-static 0.88
```

Then call the adapter:
```python
import openai
client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="...")
response = client.chat.completions.create(
    model="gnx-coder",  # ← use the adapter name
    messages=[{"role": "user", "content": "Write a Terraform S3 bucket with versioning"}]
)
```

## Limitations

- **1 epoch only** — trained for 100 steps (~1.7 epochs of the dataset). More training would improve quality.
- **Sequence length 2048** — some long reasoning traces were truncated. Future versions will use 4096+.
- **No RLHF/DPO** — pure SFT. Future versions will add preference optimization.
- **Qwen3.5 hybrid architecture** — training is slower than standard attention models due to new, unoptimized kernels.

## Citation

```bibtex
@misc{galaxynexus_coder_reasoner_v1,
  title={Galaxy Nexus Coder-Reasoner v1: QLoRA Fine-tune of Qwen3.5-9B for Agentic Coding},
  author={Galaxy Nexus AI},
  year={2026},
  url={https://huggingface.co/GalaxyNexus/galaxy-coder-reasoner-v1}
}
```

## License

Apache 2.0 — commercial use permitted.