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byesher
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Model Details
Model Provider
byesher
Model Tree
Qwen/Qwen2.5-Coder-14B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-Coder-14B"adapter = "byesher/plan-then-code-lora-14b" # or a local adapter path tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter) messages = [{"role": "user", "content": "【题目】\n{题目}\n\n请先写出分步伪代码（算法计划），再写出完整可运行的 Python 程序。"}]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))| Setting | pass@1 | compile | case_pass |
|---|---|---|---|
| Bare Coder, chat prompt | 6.2% (20/324) | 26.2% (85/324) | 6.6% (256/3894) |
| Bare Coder, comment-completion | 36.7% (119/324) | 99.4% (322/324) | 34.1% (1327/3894) |
| SFT (plan-then-code) | 45.7% (148/324) | 100% (324/324) | 36.7% (1429/3894) |
Relative to the "comment-completion" baseline, SFT improves pass@1 by +9.0pp (McNemar test, p < 0.001).
| Setting | Passed | Compile error | Wrong output (mismatch) | Runtime error | Timeout |
|---|---|---|---|---|---|
| Bare Coder, chat prompt | 20 | 239 | 41 | 22 | 2 |
| Bare Coder, comment-completion | 119 | 2 | 172 | 26 | 5 |
| SFT (plan-then-code) | 148 | 0 | 167 | 8 | 1 |
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-Coder-14B"adapter = "byesher/plan-then-code-lora-14b" # or a local adapter path tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter) messages = [{"role": "user", "content": "【题目】\n{题目}\n\n请先写出分步伪代码（算法计划），再写出完整可运行的 Python 程序。"}]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))