---
license: apache-2.0
language:
- en
- zh
tags:
- code
- qwen2.5
- coding
- self-play
- fine-tuning
- rocm
- vulkan
- gguf
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-Coder-0.5B
library_name: transformers
---

# CloneOllama Self-Play Coder 0.5B

A coding-focused fine-tune of Qwen2.5-Coder-0.5B, trained via **self-play reinforcement learning** using 4 LLM judges and DeepSeek API evaluation. Trained on an AMD Radeon RX 9070 XT (ROCm 7.2) as part of the [CloneOllama](https://github.com/Maxritz/CloneOllama) project.

## Training Method: Self-Play

```
prompt → Student (Qwen2.5-Coder-0.5B) → generates code
         → Fast Python syntax scorer (1ms)
         → DeepSeek API reasoning judge (~3s)
         → Score ≥ 5.5 → LoRA rank 16 fine-tune
```

- **500 rounds**, 397 accepted (79% acceptance rate)
- **Scores improved**: 5.3 → 5.5 / 10 over training
- **Training data**: Generated live by the student model, no human labeling
- **Prompts**: Python coding, C# coding, algorithms, CS theory

## Benchmarks (RX 9070 XT, Vulkan)

| Metric | Value |
|--------|-------|
| Prompt speed | **619 tokens/s** |
| Generation speed | **540 tokens/s** |
| Model size | 506 MB (Q8_0 GGUF) / 942 MB (FP16 safetensors) |

## Code Quality

| Prompt | Output | 
|--------|--------|
| `def is_palindrome(s):` | `s = s.lower().replace(" ", ""); return s == s[::-1]` ✅ |
| `def fibonacci(n):` | `n = int(n); return [1, n]` |
| `def reverse_list(arr):` | `def reverse(arr, size): arr = [1,2,3,4,5]` |
| `def binary_search(arr, target):` | Partial structure |

## Judges Used During Training

| Judge Model | Format | Gen Speed | 
|-------------|--------|-----------|
| DeepSeek-Coder-V2 16B | Q4_K_M GGUF | 221 t/s |
| VibeThinker-3B | Q6_K GGUF | 177 t/s |
| gemma4-coding | Q4_K_M GGUF | 56 t/s |
| Qwen2.5-Coder-7B | i1-Q4_K_M GGUF | 120 t/s |

All judges ran concurrently as llama-server instances on different ports.

## Files

| File | Size | Format |
|------|------|--------|
| `selfplay-coder-0.5B-Q8_0.gguf` | 531 MB | GGUF Q8_0 (llama.cpp) |
| `model.safetensors` | 942 MB | HuggingFace safetensors |
| `config.json` | — | Model config |
| `tokenizer.json` | 11.4 MB | Qwen2.5 tokenizer |

## Usage

### llama.cpp / Ollama

```bash
llama-server -m selfplay-coder-0.5B-Q8_0.gguf -ngl 99 --port 8080
```

### Transformers (Python)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Strangefrost/CloneOllama-selfplay-coder-0.5B")
tokenizer = AutoTokenizer.from_pretrained("Strangefrost/CloneOllama-selfplay-coder-0.5B")

prompt = "def fibonacci(n):"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))
```

## Training Pipeline

Full pipeline scripts at [`model-kitchen/`](https://github.com/Maxritz/CloneOllama/tree/main/model-kitchen):

| Script | Purpose |
|--------|---------|
| `selfplay_train.py` | Multi-judge self-play training |
| `prune.py` | Structured pruning + PEFT LoRA |
| `train_utils.py` | LoRA, distillation, calibration |
| `merge.py` | SLERP / TIES / DARE merging |
| `upcycle.py` | Dense → MoE conversion |

## Acknowledgments

- Base model: [Qwen/Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B)
- Training platform: CloneOllama on AMD Radeon RX 9070 XT (ROCm 7.2)
- Judge models: DeepSeek-Coder-V2, VibeThinker, gemma4-coding, Qwen2.5-Coder-7B