--- title: qwen-7b-chat-agent canonical_url: "https://www.modelscope.cn/models/flashyzy/qwen-7b-chat-agent" md_url: "https://www.modelscope.cn/models/flashyzy/qwen-7b-chat-agent.md" repository: flashyzy/qwen-7b-chat-agent last_updated: 2024-01-28 parameters: 17.9M tensor_type: - F32 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 33 stars: 3 --- # qwen-7b-chat-agent > qwen-7b-chat-agent - flashyzy 在 ModelScope 开源的模型。模型中文名：小小Agent，英文名：SmallSmallAgent 模型简介 该模型“小小Agent”是由西安交通大学”大模型实训队“在2024年1月份于阿里与魔搭社区举行的“大模型进校园”实战训练营·西交大站中微调开发得到的Agent模型。他主要有如下几个功能： - **Repository**: flashyzy/qwen-7b-chat-agent - **Parameters**: 17.9M - **Downloads**: 33 - **Stars**: 3 - **Last updated**: 2024-01-28 Source: https://www.modelscope.cn/models/flashyzy/qwen-7b-chat-agent --- # 模型中文名：小小Agent，英文名：SmallSmallAgent ## 模型简介 该模型“小小Agent”是由西安交通大学”大模型实训队“在2024年1月份于阿里与魔搭社区举行的“大模型进校园”实战训练营·西交大站中微调开发得到的Agent模型。他主要有如下几个功能： 1⃣️调用多种API，包括语⾳⽣成, 图像⽣成, python解析器(数学计算/代码运⾏)等多种API🤒 2⃣️具有自我认知能力😊 3⃣️具有多轮对话能力😤 ## 实验环境 我们在Modelscope提供的单卡A10环境上进行微调 ## 训练方法 1⃣️数据集：采用了damo-agent-mini-zh数据集，并将自我认知能力的数据集采样数设置为500 2⃣️微调的模型：我们基于Qwen-7b-chat模型进行微调 3⃣️ 训练过程 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 4⃣️ 微调框架版本 - SWIFT 1.5.1 ## 推理效果 ### 自我认知能力 自我认知能力 ### 图片生成能力 123 ### 语音生成能力 123 ### python解释器API调用能力+多轮对话能力 123 ## 示例代码 1⃣️sft.sh PYTHONPATH=../../.. \ CUDA_VISIBLE_DEVICES=0 \ python ../../../llm_sft.py \ --model_id_or_path qwen/Qwen-7B-Chat \ --model_revision master \ --sft_type lora \ --tuner_backend swift \ --template_type qwen \ --system '你是名为小小agent的大语言模型，是由大模型实训队于2024年1月开发的。你是一个helpful assistant。' \ --dtype AUTO \ --output_dir output \ --dataset damo-agent-mini-zh \ --train_dataset_sample 20000 \ --num_train_epochs 1 \ --max_length 4096 \ --check_dataset_strategy warning \ --quantization_bit 4 \ --bnb_4bit_comp_dtype AUTO \ --lora_rank 8 \ --lora_alpha 32 \ --lora_dropout_p 0.05 \ --lora_target_modules ALL \ --gradient_checkpointing true \ --batch_size 1 \ --weight_decay 0.01 \ --learning_rate 1e-4 \ --gradient_accumulation_steps 8 \ --max_grad_norm 0.5 \ --warmup_ratio 0.03 \ --eval_steps 100 \ --save_steps 100 \ --save_total_limit 2 \ --logging_steps 10 \ --use_flash_attn true \ --push_to_hub false \ --hub_model_id push_test \ --hub_private_repo true \ --hub_token 'dc2af58d-7c8a-4f45-8bcb-b0dc9be32f2e' \ 2⃣️sft.py ''' import inspect import os from functools import partial from typing import Any, Dict, Union import json import numpy as np import torch from modelscope import BitsAndBytesConfig, GenerationConfig from swift.trainers import Seq2SeqTrainer, Seq2SeqTrainingArguments from swift.utils import (check_json_format, compute_acc_metrics, compute_nlg_metrics, get_dist_setting, get_logger, get_main, get_model_info, is_ddp_plus_mp, is_dist, is_master, plot_images, preprocess_logits_for_metrics, seed_everything, show_layers) from .tuner import prepare_model from .utils import (TEMPLATE_MAPPING, LazyLLMDataset, SftArguments, Template, add_self_cognition_dataset, dataset_map, get_additional_saved_files, get_dataset, get_model_tokenizer, get_template, get_time_info, print_example, set_generation_config, sort_by_max_length, stat_dataset) logger = get_logger() def llm_sft(args: SftArguments) -> Dict[str, Union[str, Any]]: logger.info(f'args: {args}') print(f'device_count: {torch.cuda.device_count()}') rank, local_rank, world_size, local_world_size = get_dist_setting() print(f'rank: {rank}, local_rank: {local_rank}, ' f'world_size: {world_size}, local_world_size: {local_world_size}') seed_everything(args.seed) # Loading Model and Tokenizer model_kwargs = {'low_cpu_mem_usage': True} if is_dist() and not is_ddp_plus_mp(): model_kwargs['device_map'] = {'': local_rank} else: model_kwargs['device_map'] = 'auto' if args.load_in_8bit or args.load_in_4bit: quantization_config = BitsAndBytesConfig( args.load_in_8bit, args.load_in_4bit, bnb_4bit_compute_dtype=args.bnb_4bit_compute_dtype, bnb_4bit_quant_type=args.bnb_4bit_quant_type, bnb_4bit_use_double_quant=args.bnb_4bit_use_double_quant) logger.info(f'quantization_config: {quantization_config.__dict__}') model_kwargs['quantization_config'] = quantization_config kwargs = {} if args.use_flash_attn is not None: kwargs['use_flash_attn'] = args.use_flash_attn if args.model_cache_dir is not None: kwargs['model_dir'] = args.model_cache_dir model, tokenizer = get_model_tokenizer(args.model_type, args.torch_dtype, model_kwargs, **kwargs) logger.debug(f'device_map: {dict(model.hf_device_map)}') logger.info(f'model_config: {model.config}') generation_config = GenerationConfig( max_new_tokens=args.max_new_tokens, temperature=args.temperature, top_k=args.top_k, top_p=args.top_p, do_sample=args.do_sample, repetition_penalty=args.repetition_penalty, num_beams=args.num_beams, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id) logger.info(f'generation_config: {generation_config}') set_generation_config(model, generation_config) # Preparing LoRA model, callbacks = prepare_model(model, args) show_layers(model) model_info = get_model_info(model) logger.info(model_info) logger.info(model) # Loading Dataset random_state = np.random.RandomState(args.dataset_seed) train_dataset, val_dataset = get_dataset( args.dataset, args.dataset_test_ratio, random_state, check_dataset_strategy=args.check_dataset_strategy) val_dataset_sample = args.val_dataset_sample if train_dataset is not None and args.train_dataset_sample >= 0: train_dataset_sample = min(args.train_dataset_sample, train_dataset.shape[0]) if train_dataset.shape[0] > train_dataset_sample: logger.info(f'train_dataset_sample: {train_dataset_sample}') train_idxs = random_state.permutation(train_dataset_sample) train_dataset = train_dataset.select(train_idxs) if val_dataset_sample is None: val_dataset_sample = max( int(train_dataset_sample * args.dataset_test_ratio), 1) if val_dataset is not None and val_dataset_sample is not None and val_dataset_sample >= 0: if val_dataset.shape[0] > val_dataset_sample: logger.info(f'val_dataset_sample: {val_dataset_sample}') val_dataset = val_dataset.select(range(val_dataset_sample)) # add self-cognition dataset if args.self_cognition_sample > 0: train_dataset = add_self_cognition_dataset(train_dataset, args.self_cognition_sample, args.model_name, args.model_author) logger.info(f'train_dataset: {train_dataset}') logger.info(f'val_dataset: {val_dataset}') template_kwargs = {} template_info = TEMPLATE_MAPPING[args.template_type] use_model = template_info.get('use_model', False) if use_model: template_kwargs['model'] = model template: Template = get_template(args.template_type, tokenizer, args.system, args.max_length, args.truncation_strategy, **template_kwargs) args.system = template.default_system logger.info(f'system: {args.system}') logger.info(f'args.lazy_tokenize: {args.lazy_tokenize}') if not args.lazy_tokenize: dataset_info = {} logger.info(f'Using num_proc: {args.preprocess_num_proc}') train_dataset = dataset_map(train_dataset, template.encode, args.preprocess_num_proc) if val_dataset is not None: val_dataset = dataset_map(val_dataset, template.encode, args.preprocess_num_proc) if args.test_oom_error: train_dataset = sort_by_max_length(train_dataset, 20000) # Data analysis td0, tkwargs0 = train_dataset.data[0] print_example(td0, tokenizer, tkwargs0) dataset_info['train_dataset'] = stat_dataset(train_dataset) if val_dataset is not None: dataset_info['val_dataset'] = stat_dataset(val_dataset) else: dataset_info = None td0, tkwargs0 = template.encode(train_dataset[0]) print_example(td0, tokenizer, tkwargs0) train_dataset = LazyLLMDataset(train_dataset, template) val_dataset = LazyLLMDataset(val_dataset, template) padding_to = args.max_length if args.sft_type == 'longlora' else None data_collator = partial(template.data_collator, padding_to=padding_to) # Setting training_args evaluation_strategy = args.evaluation_strategy load_best_model_at_end = True if val_dataset is None: evaluation_strategy = 'no' if evaluation_strategy == 'no': load_best_model_at_end = False additional_saved_files = [] if args.sft_type == 'full': additional_saved_files = get_additional_saved_files(args.model_type) kwargs = {} parameters = inspect.signature( Seq2SeqTrainingArguments.__init__).parameters for key in ['neftune_noise_alpha']: if key in parameters: kwargs[key] = getattr(args, key) training_args = Seq2SeqTrainingArguments( output_dir=args.output_dir, evaluation_strategy=evaluation_strategy, logging_dir=args.logging_dir, per_device_train_batch_size=args.batch_size, per_device_eval_batch_size=args.eval_batch_size, gradient_accumulation_steps=args.gradient_accumulation_steps, learning_rate=args.learning_rate, weight_decay=args.weight_decay, max_grad_norm=args.max_grad_norm, num_train_epochs=args.num_train_epochs, max_steps=args.max_steps, lr_scheduler_type=args.lr_scheduler_type, warmup_ratio=args.warmup_ratio, logging_steps=args.logging_steps, save_strategy=args.save_strategy, save_steps=args.save_steps, save_total_limit=args.save_total_limit, remove_unused_columns=False, bf16=args.bf16, fp16=args.fp16, eval_steps=args.eval_steps, dataloader_num_workers=args.dataloader_num_workers, load_best_model_at_end=load_best_model_at_end, metric_for_best_model='rouge-l' if args.predict_with_generate else 'loss', greater_is_better=args.predict_with_generate, sortish_sampler=True, optim=args.optim, adam_beta1=args.adam_beta1, adam_beta2=args.adam_beta2, hub_model_id=args.hub_model_id, hub_private_repo=args.hub_private_repo, push_hub_strategy=args.push_hub_strategy, hub_token=args.hub_token, push_to_hub=args.push_to_hub, resume_from_checkpoint=args.resume_from_checkpoint, ddp_backend=args.ddp_backend, gradient_checkpointing=args.gradient_checkpointing, predict_with_generate=args.predict_with_generate, generation_config=generation_config, local_rank=local_rank, save_only_model=args.save_only_model, train_sampler_random=args.train_sampler_random, report_to=args.report_to, deepspeed=args.deepspeed, additional_saved_files=additional_saved_files, disable_tqdm=args.disable_tqdm, save_on_each_node=args.save_on_each_node, acc_strategy=args.acc_strategy, save_safetensors=args.save_safetensors, logging_first_step=True, **kwargs) if args.gradient_checkpointing: model.config.use_cache = False # fix transformers==4.36 logger.info('Setting model.config.use_cache: False') model.enable_input_require_grads() if is_dist(): # Compatible with https://github.com/huggingface/transformers/pull/25903 training_args._frozen = False if args.gradient_checkpointing: training_args.ddp_find_unused_parameters = False training_args.ddp_broadcast_buffers = False else: training_args.ddp_find_unused_parameters = True training_args.ddp_broadcast_buffers = True logger.info(f'training_args: {training_args}') trainer_kwargs = {} if args.predict_with_generate: trainer_kwargs['compute_metrics'] = partial( compute_nlg_metrics, tokenizer=tokenizer) else: compute_metrics = partial( compute_acc_metrics, acc_strategy=args.acc_strategy) trainer_kwargs['compute_metrics'] = compute_metrics trainer_kwargs[ 'preprocess_logits_for_metrics'] = preprocess_logits_for_metrics if args.check_model_is_latest is False: trainer_kwargs['check_model'] = False trainer = Seq2SeqTrainer( model=model, args=training_args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=val_dataset, tokenizer=tokenizer, callbacks=callbacks, **trainer_kwargs) trainer.sft_args = args if is_master(): for args_obj, fname in zip([args, training_args], ['sft_args.json', 'training_args.json']): fpath = os.path.join(args.output_dir, fname) logger.info( f'The {args_obj.__class__.__name__} will be saved in: {fpath}') with open(fpath, 'w', encoding='utf-8') as f: json.dump( check_json_format(args_obj.__dict__), f, ensure_ascii=False, indent=2) logging_path = os.path.join(args.output_dir, 'logging.jsonl') logger.info(f'The logging file will be saved in: {logging_path}') trainer.train(training_args.resume_from_checkpoint) last_model_checkpoint = getattr(trainer.state, 'last_model_checkpoint', None) logger.info(f'last_model_checkpoint: {last_model_checkpoint}') logger.info( f'best_model_checkpoint: {trainer.state.best_model_checkpoint}') train_time = get_time_info(trainer.state.log_history, len(train_dataset)) # Visualization if is_master(): images_dir = os.path.join(args.output_dir, 'images') logger.info(f'images_dir: {images_dir}') tb_dir = os.path.join(args.output_dir, 'runs') plot_images(images_dir, tb_dir, ['train/loss'], 0.9) if args.push_to_hub: trainer._add_patterns_to_gitignore(['images/']) trainer.push_to_hub() return { 'memory': trainer.perf['memory'], 'train_time': train_time, 'last_model_checkpoint': last_model_checkpoint, 'best_model_checkpoint': trainer.state.best_model_checkpoint, 'best_metric': trainer.state.best_metric, 'global_step': trainer.state.global_step, 'log_history': trainer.state.log_history, 'model_info': model_info, 'dataset_info': dataset_info, } ''' sft_main = get_main(SftArguments, llm_sft) - BaseModel Class QWenLMHeadModel