初始化项目，由ModelHub XC社区提供模型

Model: bigatuna/Qwen3-0.6B-Sushi-Coder
Source: Original Platform

README.md

@@ -0,0 +1,113 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-0.6B
+tags:
+- code
+- qwen3
+- text-generation
+- sft
+- grpo
+- trl
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+- microsoft/rStar-Coder
+- open-r1/codeforces-cots
+---
+
+# Qwen3-0.6B-Sushi-Coder
+
+<p align="center">
+  <img src="Qwen3-0.6B-Sushi-Coder.png" alt="Qwen3-0.6B-Sushi-Coder" width="400">
+</p>
+
+A 0.6B code generation model fine-tuned from Qwen3-0.6B for Python code generation.
+
+## Training
+
+This model was trained in two stages:
+
+1. **GRPO** using TRL with reward model based on test execution and formatting
+2. **SFT** on [microsoft/rStar-Coder](https://huggingface.co/datasets/microsoft/rStar-Coder) and [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots)
+
+Training was done on HuggingFace Jobs infrastructure using TRL.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "bigatuna/Qwen3-0.6B-Sushi-Coder"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+
+messages = [
+    {"role": "user", "content": "Write a Python function to check if a number is prime."}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.6,
+    top_p=0.95,
+    do_sample=True
+)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+## Recommended Parameters
+
+| Setting | Value |
+|---------|-------|
+| Temperature | 0.6 |
+| Top-p | 0.95 |
+| Top-k | 20 |
+| Max tokens | 2048 |
+
+Note: Avoid greedy decoding (temperature=0) as it can cause repetition issues with Qwen3 models.
+
+## Evaluation
+
+| Model | pass@1 |
+|-------|--------|
+| Qwen/Qwen3-0.6B (base) | 20.1% |
+| **Qwen3-0.6B-Sushi-Coder** | **29.3%** |
+
+Note: The model scores slightly higher with prompt tuning and max_model_len=4000, but the results above represent baseline settings.
+
+The evaluation can be reproduced with the following command:
+
+```bash
+lm_eval \
+  --model vllm \
+  --model_args pretrained=bigatuna/Qwen3-0.6B-Sushi-Coder,tensor_parallel_size=1,dtype=float16,gpu_memory_utilization=0.8,max_model_len=2048,trust_remote_code=True \
+  --tasks humaneval \
+  --batch_size 1 \
+  --confirm_run_unsafe_code
+```
+
+Baseline comparison:
+
+```bash
+lm_eval \
+  --model vllm \
+  --model_args pretrained=Qwen/Qwen3-0.6B,tensor_parallel_size=1,dtype=float16,gpu_memory_utilization=0.8,max_model_len=2048,trust_remote_code=True \
+  --tasks humaneval \
+  --batch_size 1 \
+  --confirm_run_unsafe_code
+```
+
+## Limitations
+
+- Optimized for Python; other languages may have reduced quality
+- Small model size limits complex reasoning
+- May generate plausible but incorrect code for edge cases
+
+## License
+
+Apache 2.0