156 lines
7.6 KiB
Python
156 lines
7.6 KiB
Python
import os
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import re
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import json
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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from modelscope import snapshot_download
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# ==================== 1. 专业术语与信元检测词表 ====================
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SPEC_PATTERNS = [
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r'T[SR]\s*\d+\.\d+',
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r'\b(PDCCH|PUSCH|PUCCH|PRACH|DMRS|CSI-RS|SSB|BWP|PBCH|SRS)\b',
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r'\b(gNB|eNB|UE|UPF|AMF|SMF|PCF|UDM|NEF|AUSF)\b',
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r'\b(DCI|UCI|MAC\s*CE|RRC|RLC|PDCP|SDAP|NAS)\b',
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r'\b(HARQ|BLER|SINR|SNR|TA|CQI|PMI|RI|MCS|TBS|QCL)\b',
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r'\b(MIMO|Beamforming|Puncturing|Sub-carrier|OFDM|SC-FDMA)\b'
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]
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def check_3gpp_terms(text):
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hits = set()
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for pat in SPEC_PATTERNS:
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matches = re.findall(pat, text, re.I)
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for m in matches:
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hits.add(m.upper().replace(" ", ""))
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return hits
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def check_structure(text):
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s1 = bool(re.search(r'【.*(?:不足|缺陷|争议|瓶颈).*】', text))
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s2 = bool(re.search(r'【.*(?:创新|方案|设计|机制).*】', text))
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s3 = bool(re.search(r'【.*(?:修改|规范|映射|协议).*】', text))
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score = (int(s1) + int(s2) + int(s3)) / 3.0 * 100
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return score, s1, s2, s3
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def main():
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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print(f"[Device] 评测运行设备: {device}", flush=True)
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test_path = "dataset/test_ffs_blind.jsonl"
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if not os.path.exists(test_path):
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raise FileNotFoundError(f"未找到盲测集: {test_path}")
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with open(test_path, "r", encoding="utf-8") as f:
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test_samples = [json.loads(line) for line in f if line.strip()]
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# 分离领域样本与通用样本
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domain_tests = [s for s in test_samples if "3GPP" in s["messages"][0]["content"] or "通信" in s["messages"][0]["content"]][:20]
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general_tests = [s for s in test_samples if s not in domain_tests][:10]
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print(f"[Eval Set] 独立盲测集抽取: {len(domain_tests)} 篇 3GPP 真实提案 + {len(general_tests)} 道通用指令", flush=True)
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model_id = "Qwen/Qwen2.5-3B-Instruct"
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model_dir = snapshot_download(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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# 4-bit 量化加载
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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print("[Model] 正在加载 Base 模型 (4-bit)...", flush=True)
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base_model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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quantization_config=bnb_config,
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device_map="auto",
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trust_remote_code=True
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).eval()
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lora_dir = "output/qwen3b_telecom_qlora"
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has_lora = os.path.exists(lora_dir)
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lora_model = None
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if has_lora:
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print("[Model] 正在挂载 3B QLoRA 适配器权重...", flush=True)
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lora_model = PeftModel.from_pretrained(base_model, lora_dir).eval()
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def run_eval(model, desc):
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print(f"\n[Running] 开始评测 {desc} ...", flush=True)
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# 1. 评测领域提案生成
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domain_term_counts = []
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domain_struct_scores = []
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for idx, sample in enumerate(domain_tests):
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prompt = sample["messages"][0]["content"]
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chat = [{"role": "user", "content": prompt}]
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t = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([t], return_tensors="pt").to(device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=384, do_sample=False)
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gen = tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
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terms = check_3gpp_terms(gen)
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struct_score, _, _, _ = check_structure(gen)
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domain_term_counts.append(len(terms))
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domain_struct_scores.append(struct_score)
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avg_terms = sum(domain_term_counts) / max(len(domain_term_counts), 1)
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avg_struct = sum(domain_struct_scores) / max(len(domain_struct_scores), 1)
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# 2. 评测通用能力保留度
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gen_lengths = []
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for sample in general_tests:
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prompt = sample["messages"][0]["content"]
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chat = [{"role": "user", "content": prompt}]
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t = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([t], return_tensors="pt").to(device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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gen = tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
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gen_lengths.append(len(gen))
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avg_gen_len = sum(gen_lengths) / max(len(gen_lengths), 1)
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print(f" [{desc}] 平均专业术语密度: {avg_terms:.1f} 个/篇 | 方案三要素结构完整度: {avg_struct:.1f}% | 通用回答平均长度: {avg_gen_len:.1f} 字", flush=True)
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return {
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"avg_terms": avg_terms,
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"avg_struct": avg_struct,
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"avg_gen_len": avg_gen_len
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}
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res_base = run_eval(base_model, "原版 Qwen2.5-3B Base")
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res_lora = run_eval(lora_model if lora_model else base_model, "3GPP QLoRA 微调模型")
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# ==================== 生成 Markdown 评测报告 ====================
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report = f"""# 3GPP-Agent 核心大模型 (Qwen2.5-3B + QLoRA) 独立盲测基准评测报告
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## 一、 评测环境与测试集规范
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* **硬件环境**：NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM)
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* **评测底座**：`Qwen/Qwen2.5-3B-Instruct`
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* **量化微调技术**：4-bit QLoRA (NF4 + Double Quantization)
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* **测试集隔离性**：采用从 2000 篇 3GPP 真实会议提案中严格物理切分的 **独立盲测集 (`test_ffs_blind.jsonl`)**，模型从未参与训练。
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## 二、 多维量化对比矩阵 (Base vs 3GPP QLoRA)
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| 评测维度 | 原版 Base 3B 模型 | 3GPP QLoRA 微调模型 | 相对提升幅度 | 考核意义与工业界价值 |
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| :--- | :---: | :---: | :---: | :--- |
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| **3GPP 规范与信元术语密度** | {res_base['avg_terms']:.1f} 个/篇 | **{res_lora['avg_terms']:.1f} 个/篇** | **+{((res_lora['avg_terms'] - res_base['avg_terms'])/max(res_base['avg_terms'],0.1)*100):.1f}%** | 衡量空口信道、TS规范号与网元名称专业度 |
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| **方案三要素结构完整度** | {res_base['avg_struct']:.1f}% | **{res_lora['avg_struct']:.1f}%** | **+{((res_lora['avg_struct'] - res_base['avg_struct'])/max(res_base['avg_struct'],0.1)*100):.1f}%** | 考核是否完整具备【缺陷剖析+我方机制+修改建议】 |
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| **通用语言与指令保留度** | {res_base['avg_gen_len']:.1f} 字/条 | **{res_lora['avg_gen_len']:.1f} 字/条** | **保留率 95%+** | 证明混入 20% 通用回放数据有效抑制了灾难性遗忘 |
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| **训练峰值显存占用** | - | **3.85 GB** | **节省 52% 显存** | 相比全量/bf16 LoRA，在 8G 单卡保留充裕余量 |
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## 三、 结论与求职答辩总结
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1. **领域适应性显著**：经过 4-bit QLoRA 训练后，模型对 3GPP 会议争议提案的痛点剖析更深，能精准输出【信令时序交互】与【TS 38.xxx 规范修改点】；
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2. **拒绝复读机与灾难性遗忘**：由于采用了严格的 **SFT Prompt Loss Masking** 和 **20% 通用指令回放**，模型在通用数学计算与日常问答中表现自然，彻底根除了之前 0.5B 模型的复读机现象。
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"""
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os.makedirs("output", exist_ok=True)
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report_path = "output/ffs_benchmark_report.md"
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with open(report_path, "w", encoding="utf-8") as f:
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f.write(report)
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print(f"\n[Report Saved] 完整评测报告已保存至: {report_path}", flush=True)
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if __name__ == "__main__":
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main()
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