---
license: apache-2.0
base_model: unsloth/Qwen3-4B
tags:
  - code
  - coding
  - full-stack
  - frontend
  - backend
  - agent
  - qwen3
  - lora
  - unsloth
  - fine-tuned
language:
  - en
pipeline_tag: text-generation
library_name: peft
---

# NanoCoder V1 🧠⚡

A **4B parameter** full-stack coding assistant fine-tuned from **Qwen3-4B** using Unsloth + LoRA.
Trained through a multi-phase pipeline with joint domain training and validation-driven checkpoint selection.

Best checkpoint: **step 150** — combined score **73.6%** across all skill domains.

---

## 📊 Benchmarks

| Benchmark            | Score       | Notes                                       |
|----------------------|-------------|---------------------------------------------|
| **HumanEval pass@1** | **49.4%**   | 164 problems, executed against test cases  |
| **LiveCodeBench**    | **13.3%**   | Execution eval on 30 problems (public tests)|
| Frontend (custom)    | 58.3%       | React, Next.js, TypeScript, Tailwind, a11y |
| Backend (custom)     | 87.5%       | FastAPI, Express, PostgreSQL, JWT, MongoDB |
| Agent (custom)       | 75.0%       | Thought → Action → Patch → Reasoning format|
| **Combined**         | **73.6%**   | Averaged across skill domains              |

---

## 🎯 What it does well

- **Backend** — API design, auth (JWT/bcrypt), SQL/NoSQL, N+1 fixes, CORS
- **Debugging agent** — structured reasoning (`### Thought → ### Action → ### Patch → ### Reasoning`)
- **Full-stack integration** — connects frontend + backend flows
- **Bug pattern recognition** — race conditions, memory leaks, type errors

## ⚠️ Known limitations

- Frontend scores lower than backend (weakest domain in v1)
- Not a replacement for larger models (7B+) on hard competitive programming
- English-only

---

## 🚀 Usage

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="usernamebetter/nanocoder-v1",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

SYSTEM = "You are NanoCoder, an expert Senior Full-Stack Engineer and debugging agent."

prompt = (
    f"<|im_start|>system\n{SYSTEM}<|im_end|>\n"
    f"<|im_start|>user\nFix this React hydration error: useState(Date.now())<|im_end|>\n"
    f"<|im_start|>assistant\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

---

## 🏗️ Training pipeline

Multi-phase joint training from Qwen3-4B base:

1. **Phase 1** — General coding (Magicoder-Evol-Instruct)
2. **Phase 2** — Frontend specialization
3. **Phase 3** — Fullstack (frontend + backend interleaved)
4. **Phase 4** — Agent reasoning training
5. **Final** — Joint retrain from base with all domains mixed (this checkpoint)

### Training configuration
- **Base**: Qwen3-4B (4-bit quantized)
- **LoRA**: r=32, alpha=32, dropout=0
- **LR**: 1e-5 with cosine scheduler
- **Steps**: 400 (best checkpoint at step 150)
- **Batch**: 2 × grad accum 4 = effective 8
- **Optimizer**: adamw_8bit
- **Dataset**: ~13.7k samples interleaved
  - 🤖 Agent (synthetic + real): 40%
  - 🎨 Frontend: 35%
  - ⚙️  Backend: 15%
  - 🐛 Bug fixing: 10%

### Data sources
- ise-uiuc/Magicoder-Evol-Instruct-110K
- sahil2801/CodeAlpaca-20k
- nickrosh/Evol-Instruct-Code-80k-v1
- iamtarun/code_instructions_120k_alpaca
- m-a-p/CodeFeedback-Filtered-Instruction
- bigcode/self-oss-instruct-sc2-exec-filter-50k
- HuggingFaceH4/CodeAlpaca_20K
- TokenBender/code_instructions_122k_alpaca_style
- Custom synthetic agent examples with structured reasoning format

---

## 🧪 Prompt format

Uses Qwen chat template:

```
<|im_start|>system
You are NanoCoder, an expert Senior Full-Stack Engineer and debugging agent.
<|im_end|>
<|im_start|>user
{your question}
<|im_end|>
<|im_start|>assistant
```

For debugging tasks, the model responds in structured format:

```
### Thought:
{root cause analysis}

### Action:
{what to do}

### Patch:
{code fix}

### Reasoning:
{why it works}
```

---

## 📅 Roadmap

- ✅ **v1**: Joint multi-domain training (this release)
- 🚧 **v2**: Frontend boost + reasoning domain + label smoothing + cosine restarts
- 🚧 **v3**: DPO alignment + tool calling
- 🚧 **GGUF**: Q4_K_M / Q5_K_M / Q8_0 exports

---

## 🙏 Credits

- **Base model**: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
- **Fine-tuning framework**: [Unsloth](https://github.com/unslothai/unsloth)
- **Training**: Kaggle T4 + Google Colab T4

---

## 📄 License

Apache-2.0 (inherited from Qwen3-4B base).