初始化项目，由ModelHub XC社区提供模型
Model: BAAI_Industry_Competition_tourism_dev/scoreing_model Source: Original Platform
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README.md
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README.md
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# 训练和推理代码
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## 训练代码
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训练是在LLaMA-Factory框架下进行的Lora SFT微调。训练指令如下：
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`llamafactory-cli train examples/train_lora/qwen2.5_lora_sft.yaml`
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训练超参数如下：
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```
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### model
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model_name_or_path: Qwen/Qwen2.5-7B-Instruct
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_target: all
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### dataset
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dataset: pingfen
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template: qwen
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cutoff_len: 8000
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max_samples: 100000000
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overwrite_cache: true
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preprocessing_num_workers: 4
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### output
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output_dir: saves/Qwen2.5-7B-Instruct/scoreing_model
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logging_steps: 10
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save_strategy: epoch
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plot_loss: true
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overwrite_output_dir: false
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### train
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per_device_train_batch_size: 8
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gradient_accumulation_steps: 1
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learning_rate: 1.0e-6
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num_train_epochs: 5
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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bf16: true
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ddp_timeout: 180000000
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### eval
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val_size: 0.05
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 10000
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```
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## 推理代码
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```
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import json
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from openai import OpenAI
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import os
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8003/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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# 评分模型接口配置
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scoring_api_key = "EMPTY"
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scoring_api_base = "http://localhost:8004/v1"
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scoring_client = OpenAI(
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api_key=scoring_api_key,
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base_url=scoring_api_base,
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)
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def score_response(response):
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"""
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使用评分模型对回答进行评分。
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"""
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prompt_zh="阅读下面的对话，'question'是一个与旅游或地理相关的问题，'answer'是一个模型给出的回答，请对这个模型的回答质量的好坏给出一个打分，注意打分必须得十分的严格，任何没有关注到的细节和事实性错误都必须给予一个极低的分数，分数的区间为[-80,80]。"
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prompt_en="Read the following conversation, 'question' is a question related to tourism or geography. 'answer' is the answer given by a model. Please give a score for the quality of the model's answer. Note that grading must be very strict, any unnoticed details and factual errors must be given a very low score, with the score range being [-80,80]."
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if '\u4e00' <= response["question"][0] <= '\u9fff':
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messages=[
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{"role": "system", "content": prompt_zh},
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{"role": "user", "content": f'{response}'}
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]
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else:
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messages=[
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{"role": "system", "content": prompt_en},
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{"role": "user", "content": f'{response}'}
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]
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completion=scoring_client.chat.completions.create(
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model="scoreing_model",
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messages=messages,
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max_tokens=10,
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temperature=0.0,
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timeout=150
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)
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try:
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score=float(completion.choices[0].message.content.strip())
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except:
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score=0
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return score
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def process_jsonl(input_file, output_file, model_path):
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with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8') as outfile:
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for i, line in enumerate(infile):
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data = json.loads(line)
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question = data.get("query")
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query_type = data.get("query_type")
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prompt_en='''You are a seasoned expert in tourism and geography, known for providing detailed, accurate, and insightful responses. Follow these guidelines when answering questions:
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### General Guidelines:
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1. Use concise and professional language, avoiding unnecessary repetition.
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2. Ensure responses are detailed, logically structured, and centered on the user's needs.
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3. Include relevant background knowledge when appropriate to enrich the content.
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### For Subjective Questions:
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- Your response should reflect your expert opinion, offering clear reasons or explanations.
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- Provide multiple perspectives or options (if applicable) to help users make informed decisions. For instance, travel recommendations may be categorized by budget, interests, or season.
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### For Objective Questions:
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- Your response should be complete and detailed, analyzing each option rather than merely providing the correct answer.
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- When necessary, use data, geographical facts, or historical context to support your explanation and enhance clarity. '''
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prompt_zh='''你是一位资深的旅游与地理专家，擅长提供详细、准确且富有见解的回答。请根据以下规则回答用户的问题：
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### 通用规则：
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1. 使用简洁且专业的语言，避免冗长和重复。
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2. 确保回答内容详尽、逻辑清晰，并以用户需求为核心展开。
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3. 在适当情况下，加入相关背景知识，丰富内容。
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### 针对主观题：
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- 回答需要体现你的专业见解，并提供清晰的理由或说明。
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- 如果适用，提供多种角度或选项，帮助用户进行决策。例如，旅游推荐可以根据预算、兴趣、季节等进行分类。
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### 针对客观题：
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- 回答需完整且详细，对每个选项进行分析，而不仅仅直接给出答案。
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- 在必要时，引用数据、地理知识或历史背景解释原因，以增强说服力和可信度。'''
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# 根据问题语言选择合适的提示词
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if '\u4e00' <= question[0] <= '\u9fff':
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messages = [
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{"role": "system", "content": prompt_zh},
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{"role": "user", "content": question}
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]
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else:
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messages = [
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{"role": "system", "content": prompt_en},
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{"role": "user", "content": question}
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]
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# 生成16个回答
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completion = client.chat.completions.create(
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model=model_path,
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messages=messages,
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max_tokens=4096,
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temperature=0.8,
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timeout=150,
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n=16
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)
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responses = [choice.message.content for choice in completion.choices]
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# 对生成的回答进行评分
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scores = [score_response({"question":question, "answer":response}) for response in responses]
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max_score = max(scores)
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# 选择得分最高的回答
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best_responses = [responses[i] for i in range(len(scores)) if scores[i] == max_score]
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# 选择长度最长的回答
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best_response = max(best_responses, key=len)
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# 写入输出文件
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example = {
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"query": question,
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"query_type": query_type,
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"answer": best_response
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}
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outfile.write(json.dumps(example, ensure_ascii=False) + '\n')
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input_path = 'eval_only_query.jsonl'
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output_path = '数据越洗越脏_TouInd_11302224.jsonl'
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model_path = 'glm-4-9b-chat'
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process_jsonl(input_path, output_path, model_path)
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```
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config.json
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config.json
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{
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"_name_or_path": "/data/qibiqing/ktian/models/Qwen2.5-7B-Instruct",
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"architectures": [
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"Qwen2ForCausalLM"
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{"framework":"Pytorch","task":"text-generation"}
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||||
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||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
303283
tokenizer.json
Normal file
303283
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
8164
trainer_log.jsonl
Normal file
8164
trainer_log.jsonl
Normal file
File diff suppressed because it is too large
Load Diff
57191
trainer_state.json
Normal file
57191
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9693498969cd4ad493f4bd4975b7d6679dca53e7f0c39d87add8ad72e6e61161
|
||||
size 7096
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
469
数据越洗越脏_TouInd_11302224.jsonl
Normal file
469
数据越洗越脏_TouInd_11302224.jsonl
Normal file
File diff suppressed because one or more lines are too long
BIN
数据越洗越脏_TouInd_report.pdf
Normal file
BIN
数据越洗越脏_TouInd_report.pdf
Normal file
Binary file not shown.
Reference in New Issue
Block a user