初始化项目，由ModelHub XC社区提供模型

Model: Strangefrost/CloneOllama-selfplay-coder-0.5B
Source: Original Platform

README.md

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+---
+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