--- title: firefly-baichuan2-13b canonical_url: "https://www.modelscope.cn/models/YeungNLP/firefly-baichuan2-13b" md_url: "https://www.modelscope.cn/models/YeungNLP/firefly-baichuan2-13b.md" repository: YeungNLP/firefly-baichuan2-13b last_updated: 2023-10-22 model_type: - baichuan architectures: - BaichuanForCausalLM inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 120 stars: 0 --- # firefly-baichuan2-13b > firefly-baichuan2-13b - YeungNLP 在 ModelScope 开源的模型。使用Firefly项目微调baichuan2-13b-base。训练数据约为一百万多轮对话数据，包括项目分享的moss数据+2万条school math数据。 YeungNLP/firefly-baichuan2-13b 是 ModelScope 魔搭社区上的机器学习模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: YeungNLP/firefly-baichuan2-13b - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 120 - **Stars**: 0 - **Last updated**: 2023-10-22 Source: https://www.modelscope.cn/models/YeungNLP/firefly-baichuan2-13b --- 使用[Firefly](https://github.com/yangjianxin1/Firefly)项目微调baichuan2-13b-base。训练数据约为一百万多轮对话数据，包括项目分享的moss数据+2万条school math数据。 更多详情见项目：[Firefly](https://github.com/yangjianxin1/Firefly) CMMLU榜单： | 模型 | CMMLU | |---------------------------|------------| | Baichuan2-13B-Chat | 58.4 | | **firefly-baichuan2-13b** | **56.83** | | WeMix-LLaMA2-70B | 56 | | ChatGPT | 53.9 | | InternLM-20B-Chat | 52.2 | | Baichuan-13B-Chat | 50.7 | | chinese-alpaca-2-13b | 45.17 | | LLaMA-2-70B-Chat | 43.3 | | openbuddy-llama2-13b-v8.1 | 41.66 | | belle-llama2-13b | 41.57 | | ziya-llama-13b | 39.9 | | chinese-alpaca-plus-13b | 39.9 | | flagalpha-llama2-13b-chat | 39.20 | | llama-2-13b-chat | 38.65 | | yayi-13b-llama2 | 30.73 | | linly-llama2-13b | 26.32 | 单轮对话: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch """ 单轮对话，不具有对话历史的记忆功能 """ def main(): model_name = 'YeungNLP/firefly-baichuan2-13b' max_new_tokens = 500 top_p = 0.9 temperature = 0.35 repetition_penalty = 1.0 device = 'cuda' model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map='auto' ).to(device).eval() tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, # llama不支持fast use_fast=False if model.config.model_type == 'llama' else True ) # QWenTokenizer比较特殊，pad_token_id、bos_token_id、eos_token_id均为None。eod_id对应的token为<|endoftext|> if tokenizer.__class__.__name__ == 'QWenTokenizer': tokenizer.pad_token_id = tokenizer.eod_id tokenizer.bos_token_id = tokenizer.eod_id tokenizer.eos_token_id = tokenizer.eod_id text = input('User：') while True: text = text.strip() # chatglm使用官方的数据组织格式 if model.config.model_type == 'chatglm': text = '[Round 1]\n\n问：{}\n\n答：'.format(text) input_ids = tokenizer(text, return_tensors="pt", add_special_tokens=False).input_ids.to(device) # 为了兼容qwen-7b，因为其对eos_token进行tokenize，无法得到对应的eos_token_id else: input_ids = tokenizer(text, return_tensors="pt", add_special_tokens=False).input_ids.to(device) bos_token_id = torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long).to(device) eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long).to(device) input_ids = torch.concat([bos_token_id, input_ids, eos_token_id], dim=1) with torch.no_grad(): outputs = model.generate( input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id ) outputs = outputs.tolist()[0][len(input_ids[0]):] response = tokenizer.decode(outputs) response = response.strip().replace(tokenizer.eos_token, "").strip() print("Firefly：{}".format(response)) text = input('User：') if __name__ == '__main__': main() ``` 多轮对话: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch def main(): model_name = 'YeungNLP/firefly-baichuan2-13b' device = 'cuda' max_new_tokens = 500 # 每轮对话最多生成多少个token history_max_len = 1000 # 模型记忆的最大token长度 top_p = 0.9 temperature = 0.35 repetition_penalty = 1.0 # 加载模型 model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map='auto' ).to(device).eval() tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, # llama不支持fast use_fast=False if model.config.model_type == 'llama' else True ) # QWenTokenizer比较特殊，pad_token_id、bos_token_id、eos_token_id均为None。eod_id对应的token为<|endoftext|> if tokenizer.__class__.__name__ == 'QWenTokenizer': tokenizer.pad_token_id = tokenizer.eod_id tokenizer.bos_token_id = tokenizer.eod_id tokenizer.eos_token_id = tokenizer.eod_id # 记录所有历史记录 if model.config.model_type != 'chatglm': history_token_ids = torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long) else: history_token_ids = torch.tensor([[]], dtype=torch.long) # 开始对话 utterance_id = 0 # 记录当前是第几轮对话，为了契合chatglm的数据组织格式 user_input = input('User：') while True: utterance_id += 1 # chatglm使用官方的数据组织格式 if model.config.model_type == 'chatglm': user_input = '[Round {}]\n\n问：{}\n\n答：'.format(utterance_id, user_input) user_input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids # firefly的数据组织格式 # 为了兼容qwen-7b，因为其对eos_token进行tokenize，无法得到对应的eos_token_id else: input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long) user_input_ids = torch.concat([input_ids, eos_token_id], dim=1) history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1) model_input_ids = history_token_ids[:, -history_max_len:].to(device) with torch.no_grad(): outputs = model.generate( input_ids=model_input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id ) model_input_ids_len = model_input_ids.size(1) response_ids = outputs[:, model_input_ids_len:] history_token_ids = torch.concat((history_token_ids, response_ids.cpu()), dim=1) response = tokenizer.batch_decode(response_ids) print("Firefly：" + response[0].strip().replace(tokenizer.eos_token, "")) user_input = input('User：') if __name__ == '__main__': main() ```