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Model: Strangefrost/CloneOllama-selfplay-coder-0.5B Source: Original Platform
2026-08-28 01:54:18 +08:00
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+selfplay-coder-0.5B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
--- a/README.md
+++ b/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
--- a/added_tokens.json
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--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
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+  "layer_types": [
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+    "full_attention",
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
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+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.53.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
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+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.53.1"
+}
--- a/merges.txt
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--- a/selfplay-coder-0.5B-Q8_0.gguf
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--- a/vocab.json
+++ b/vocab.json