metadata
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 |
| 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 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:
- Specialize for agentic coding — AWS, Terraform, Docker, Kubernetes, Linux, Python
- Reinforce step-by-step reasoning before code generation
- 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
# 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:
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
@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.