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360Zhinao (360智脑)

🤗 Hugging Face   |    🤖 ModelScope   |    💬 WeChat (微信)  

Feel free to visit 360Zhinao's official website https://ai.360.com for more experience.


# Models Introduction 🎉🎉🎉We open-source the 360Zhinao model series: - **360Zhinao-7B-Base** - **360Zhinao-7B-Chat-4K** - **360Zhinao-7B-Chat-32K** - **360Zhinao-7B-Chat-360K** The characteristics of the 360Zhinao open-source models are: - **Base Model:** Leveraging a high-quality corpus of 3.4 trillion Tokens which mainly consist of Chinese, English and code, we achieved competitive performance on relevant benchmark evaluations of the same model scale. - **Chat Model:** Powerful chat capabilities and three different sequence lengths of 4K, 32K and 360K. 360K (about 500k Chinese characters) is the longest sequcence length among open-sourced Chinese models until now.
# News and Updates - 2024.04.11 We release **360Zhinao-7B** 1.0 version, include the base model and three chat model with sequence lengths of 4K, 32K adn 360K.
# Table of contents - [Download URL](#Download-URL) - [Model Evaluation](#Model-Evaluation) - [Quickstart](#Quickstart) - [Model Inference](#Model-Inference) - [Model Finetune](#Model-Finetune) - [License](#License)
# Download URL See the following table for this release and download links: | Size | Model | BF16 | Int4| |-|-|-|-| | 7B | 360Zhinao-7B-Base | 🤖 🤗 | | | 7B | 360Zhinao-7B-Chat-4K | 🤖 🤗 | 🤖 🤗 | | 7B | 360Zhinao-7B-Chat-32K | 🤖 🤗 | 🤖 🤗 | | 7B | 360Zhinao-7B-Chat-360K | 🤖 🤗 | 🤖 🤗 |
# Model Evaluation ## Base Model We evaluate the performance of our model on the OpenCompass evaluation datasets, including C-Eval, AGIEval, MMLU, CMMLU, HellaSwag, MATH, GSM8K, HumanEval, MBPP, BBH, LAMBADA. The ablity evaluated of model include natural language understanding, knowledge, mathematical computation and reasoning, code generation, logical reasoning, etc. |
Model
| AVG | CEval | AGIEval | MMLU | CMMLU | HellaSwag | MATH | GSM8K | HumanEval | MBPP | BBH | LAMBADA | |:----------------------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:| | Baichuan2-7B | 41.49 | 56.3 | 34.6 | 54.7 | 57 | 67 | 5.4 | 24.6 | 17.7 | 24 | 41.8 | 73.3 | | Baichuan-7B | 31.94 | 44.7 | 24.6 | 41.5 | 44.6 | 68.4 | 2.5 | 9.6 | 9.1 | 6.4 | 32.8 | 67.1 | | ChatGLM3-6B | **58.67** | 67 | 47.4 | 62.8 | 66.5 | 76.5 | 19.2 | 61 | 44.5 | **57.2** | **66.2** | 77.1 | | DeepSeek-7B | 39.8 | 45 | 24 | 49.3 | 46.8 | 73.4 | 4.2 | 18.3 | 25 | 36.4 | 42.8 | 72.6 | | InternLM2-7B | 58.01 | 65.7 | 50.2 | 65.5 | 66.2 | 79.6 | 19.9 | **70.6** | 41.5 | 42.4 | 64.4 | 72.1 | | InternLM-7B | 39.33 | 53.4 | 36.9 | 51 | 51.8 | 70.6 | 6.3 | 31.2 | 13.4 | 14 | 37 | 67 | | LLaMA-2-7B | 33.27 | 32.5 | 21.8 | 46.8 | 31.8 | 74 | 3.3 | 16.7 | 12.8 | 14.8 | 38.2 | 73.3 | | LLaMA-7B | 30.35 | 27.3 | 20.6 | 35.6 | 26.8 | 74.3 | 2.9 | 10 | 12.8 | 16.8 | 33.5 | 73.3 | | Mistral-7B-v0.1 | 47.67 | 47.4 | 32.8 | 64.1 | 44.7 | 78.9 | 11.3 | 47.5 | 27.4 | 38.6 | 56.7 | 75 | | MPT-7B | 30.06 | 23.5 | 21.3 | 27.5 | 25.9 | 75 | 2.9 | 9.1 | 17.1 | 22.8 | 35.6 | 70 | | Qwen1.5-7B | 55.12 | 73.57 | **50.8** | 62.15 | 71.84 | 72.62 | **20.36** | 54.36 | **53.05** | 36.8 | 40.01 | 70.74 | | Qwen-7B | 49.53 | 63.4 | 45.3 | 59.7 | 62.5 | 75 | 13.3 | 54.1 | 27.4 | 31.4 | 45.2 | 67.5 | | XVERSE-7B | 34.27 | 61.1 | 39 | 58.4 | 60.8 | 73.7 | 2.2 | 11.7 | 4.9 | 10.2 | 31 | 24 | | Yi-6B | 47.8 | 73 | 44.3 | 64 | **73.5** | 73.1 | 6.3 | 39.9 | 15.2 | 23.6 | 44.9 | 68 | | **360Zhinao-7B** | 56.15 | **74.11** | 49.49 | **67.44** | 72.38 | **83.05** | 16.38 | 53.83 | 35.98 | 42.4 | 43.95 | **78.59** | The above results could be viewed or reproduced on [Opencompass](https://rank.opencompass.org.cn/leaderboard-llm). ## Chat Models We adopted a two-stage approach to train the long context models. **First stage**: We increased RoPE base and extended the context length to 32K. - Firstly, we performed Continual Pretraining on approximately 5B tokens with a 32K context window. - Then during the SFT stage, we fine-tuned the model using long data from various sources, including high-quality human-labeled 32K data. **Second stage**: We extended the context length to 360K, training with the following data: - A small amount of high-quality human-labeled super-long data. - Due to the scarcity of annotated super-long data, we constructed various forms of synthetic data. - Multi-Doc QA: Similar to [Ziya-Reader](https://arxiv.org/abs/2311.09198), we generated multi-document QA pairs based on 360's database. Multiple QA pairs are constructed for one row of Multi-Doc QA data input, resulting in a multi-turn format and significantly improving the training efficiency. - Single-Doc QA: Similar to [LLama2 Long](https://arxiv.org/abs/2309.16039), we constructed multi-turn QA data based on different segments within one row of long-text input. We evaluated our models across various lengths and benchmarks. - ### Long Context Benchmarks We evaluated our 32K and 360K models on [LongBench](https://github.com/THUDM/LongBench), a multi-task bilingual benchmark for long contexts. We report results on Chinese tasks that are the most relevant to downstream applications: Single/Multi-Doc QA, Summarization, Few-Shot Learning and Code Completion. | Model | Avg | 单文档QA | 多文档QA | 摘要 | Few-shot学习 | 代码补全 | | :------------------------ |:---------:|:--------:|:---------:|:---------:|:------------:|:---------:| | GPT-3.5-Turbo-16k | 37.84 | 61.2 | 28.7 | 16 | 29.2 | 54.1 | | ChatGLM2-6B-32k | 37.16 | 51.6 | 37.6 | 16.2 | 27.7 | 52.7 | | ChatGLM3-6B-32k | 44.62 | **62.3** | 44.8 | 17.8 | 42 | 56.2 | | InternLM2-Chat-7B | 42.20 | 56.65 | 29.15 | **17.99** | 43.5 | **63.72** | | Qwen1.5-Chat-7B | 36.75 | 52.85 | 30.08 | 14.28 | 32 | 54.55 | | Qwen1.5-Chat-14B | 39.80 | 60.39 | 27.99 | 14.77 | 37 | 58.87 | | 360Zhinao-7B-Chat-32K | **45.18** | 57.18 | **48.06** | 15.03 | **44** | 61.64 | - ### 360Zhinao-7B-Chat-360K on "NeedleInAHaystack" [NeedleInAHaystack](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) places one small piece of information in different positions of long text and queries this information as a test of LLM's long-context capabilities. 360Zhinao-7B-Chat-360K could achieve over 98% accuracy on both English and Chinese NeedleInAHaystack tasks. - English version(same as [NeedleInAHaystack](https://github.com/gkamradt/LLMTest_NeedleInAHaystack))

**needle**:The best thing to do in San Francisco is eat a sandwich and sit in Dolores Park on a sunny day. **query**:What is the best thing to do in San Francisco? - Chinese version

We constructed the Chinese version following the [SuperCLUE-200K benchmark](https://mp.weixin.qq.com/s/QgoRf2LB-7vc3vTFOHJkpw): **haystack**:Chinese novels. **needle**:(in Chinese) 王莽是一名勤奋的店员,他每天凌晨就起床,赶在第一缕阳光照亮大地之前到达店铺,为即将开始的一天做准备。他清扫店铺,整理货架,为顾客提供方便。他对五金的种类和用途了如指掌,无论顾客需要什么,他总能准确地找到。\n然而,他的老板刘秀却总是对他吹毛求疵。刘秀是个挑剔的人,他总能在王莽的工作中找出一些小错误,然后以此为由扣他的工资。他对王莽的工作要求非常严格,甚至有些过分。即使王莽做得再好,刘秀也总能找出一些小问题,让王莽感到非常沮丧。\n王莽虽然对此感到不满,但他并没有放弃。他知道,只有通过自己的努力,才能获得更好的生活。他坚持每天早起,尽管他知道那天可能会再次被刘秀扣工资。他始终保持微笑,尽管他知道刘秀可能会再次对他挑剔。 **query**:(in Chinese) 王莽在谁的手下工作?
# Quickstart Simple examples to illustrate how to use 360Zhinao-7B-Base and 360Zhinao-7B-Chat quickly using 🤖 ModelScope and 🤗 Transformers ## Dependency Installation - python 3.8 and above - pytorch 2.0 and above - transformers 4.37.2 and above - CUDA 11.4 and above are recommended. ```shell pip install -r requirements.txt ``` We recommend installing Flash-Attention (which currently supports flash attention 2) to increase your performance and reduce your memory footprint. (flash-attention is optional and will work without installation) >flash-attn >= 2.3.6 ```shell FLASH_ATTENTION_FORCE_BUILD=TRUE pip install flash-attn==2.3.6 ``` ## 🤗 Transformers ### Demonstration of Base Model Inference This code demonstrates fast inference with 360Zhinao-7B-Base models using transformers. ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers.generation import GenerationConfig MODEL_NAME_OR_PATH = "qihoo360/360Zhinao-7B-Base" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME_OR_PATH, device_map="auto", trust_remote_code=True) generation_config = GenerationConfig.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) inputs = tokenizer('中国二十四节气\n1. 立春\n2. 雨水\n3. 惊蛰\n4. 春分\n5. 清明\n', return_tensors='pt') inputs = inputs.to(model.device) pred = model.generate(input_ids=inputs["input_ids"], generation_config=generation_config) print("outputs:\n", tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` ### Demonstration of Chat Model Inference This code demo uses transformers to quickly use the 360Zhinao-7B-Chat-4K model for inference. ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers.generation import GenerationConfig MODEL_NAME_OR_PATH = "qihoo360/360Zhinao-7B-Chat-4K" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME_OR_PATH, device_map="auto", trust_remote_code=True) generation_config = GenerationConfig.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) messages = [] #round-1 messages.append({"role": "user", "content": "介绍一下刘德华"}) response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) messages.append({"role": "assistant", "content": response}) print(messages) #round-2 messages.append({"role": "user", "content": "他有什么代表作?"}) response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) messages.append({"role": "assistant", "content": response}) print(messages) ``` ## 🤖 ModelScope ### Demonstration of Base Model Inference This code demonstrates using ModelScope to quickly use the 360Zhinao-7B-Base model for inference. ```python from modelscope import AutoModelForCausalLM, AutoTokenizer from modelscope import GenerationConfig MODEL_NAME_OR_PATH = "qihoo360/360Zhinao-7B-Base" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME_OR_PATH, device_map="auto", trust_remote_code=True) generation_config = GenerationConfig.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) inputs = tokenizer('中国二十四节气\n1. 立春\n2. 雨水\n3. 惊蛰\n4. 春分\n5. 清明\n', return_tensors='pt') inputs = inputs.to(model.device) pred = model.generate(input_ids=inputs["input_ids"], generation_config=generation_config) print("outputs:\n", tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) ``` ### Demonstration of Chat Model Inference This code demonstrates using ModelScope to quickly use the 360Zhinao-7B-Chat-4K model for inference. ```python from modelscope import AutoModelForCausalLM, AutoTokenizer from modelscope import GenerationConfig MODEL_NAME_OR_PATH = "qihoo360/360Zhinao-7B-Chat-4K" tokenizer = AutoTokenizer.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME_OR_PATH, device_map="auto", trust_remote_code=True) generation_config = GenerationConfig.from_pretrained( MODEL_NAME_OR_PATH, trust_remote_code=True) messages = [] #round-1 messages.append({"role": "user", "content": "介绍一下刘德华"}) response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) messages.append({"role": "assistant", "content": response}) print(messages) #round-2 messages.append({"role": "user", "content": "他有什么代表作?"}) response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) messages.append({"role": "assistant", "content": response}) print(messages) ``` ## CLI Demo Use terminal interaction for a fast experience ```shell python cli_demo.py ```

## Web Demo You can also use web interaction for a quick experience ```shell streamlit run web_demo.py ```

## API Demo Start command ```shell python openai_api.py ``` Request parameter ```shell curl 'http://localhost:8360/v1/chat/completions' \ -H 'Content-Type: application/json' \ -d '{ "max_new_tokens": 200, "do_sample": true, "top_k": 0, "top_p": 0.8, "temperature": 1.0, "repetition_penalty": 1.0, "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "你好"} ] }' ```
# Model Inference ## Quantization We provide quantization schemes based on AutoGPTQ and open source the Int4 quantization models. ## Deployment ### vLLM Installation If you want to deploy and accelerate inference, we recommend using `vLLM==0.3.3`。 If you are using **CUDA 12.1 and PyTorch 2.1**, you can install vLLM directly with the following command. ```shell pip install vllm==0.3.3 ``` Otherwise, please refer to the official vLLM [Installation Instructions](https://docs.vllm.ai/en/latest/getting_started/installation.html)。 >Once the installation is complete, you will need to do the following 1. Copy the vllm/zhinao.py file to the vllm/model_executor/models directory corresponding to your env environment. 2. Copy the vllm/serving_chat.py file to the vllm/entrypoints/openai corresponding to your env environment. 3. Then add a line to vllm/model_executor/models/\_\_init\_\_.py ```shell "ZhinaoForCausalLM": ("zhinao", "ZhinaoForCausalLM"), ``` ### vLLM Service Start Starting the service ```shell python -m vllm.entrypoints.openai.api_server \ --served-model-name 360Zhinao-7B-Chat-4K \ --model qihoo360/360Zhinao-7B-Chat-4K \ --trust-remote-code \ --tensor-parallel-size 1 \ --max-model-len 4096 \ --host 0.0.0.0 \ --port 8360 ``` Use curl to request the service ```shell curl http://localhost:8360/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "360Zhinao-7B-Chat-4K", "max_tokens": 200, "top_k": -1, "top_p": 0.8, "temperature": 1.0, "presence_penalty": 0.0, "frequency_penalty": 0.0, "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "你好"} ], "stop": [ "", "<|im_end|>", "<|im_start|>" ] }' ``` Use python to request the service ```python from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8360/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="360Zhinao-7B-Chat-4K", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "你好"}, ], stop=[ "", "<|im_end|>", "<|im_start|>" ], presence_penalty=0.0, frequency_penalty=0.0 ) print("Chat response:", chat_response) ``` > Notice: If you need to enable repetition penalty, recommended to use *presence_penalty* and *frequency_penalty* parameters. >
# Model Finetune ## Training data Training Data: data/training_data_sample.json. The sample data is 10,000 pieces sampled from [multiturn_chat_0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) and format converted. Data Format: ```json [ { "id": 1, "conversations": [ { "from": "system", "value": "You are a helpful assistant." }, { "from": "user", "value": "您好啊" }, { "from": "assistant", "value": "你好!我今天能为您做些什么?有什么问题或需要帮助吗? 我在这里为您提供服务。" } ] } ] ``` ## Fine-tuning scripts ```shell set -x HOSTFILE=hostfile DS_CONFIG=./finetune/ds_config_zero2.json # PARAMS LR=5e-6 EPOCHS=3 MAX_LEN=4096 BATCH_SIZE=4 NUM_NODES=1 NUM_GPUS=8 MASTER_PORT=29500 IS_CONCAT=False # Whether to concatenate to maximum length (MAX_LEN) DATA_PATH="./data/training_data_sample.json" MODEL_PATH="qihoo360/360Zhinao-7B-Base" OUTPUT_DIR="./outputs/" deepspeed --hostfile ${HOSTFILE} \ --master_port ${MASTER_PORT} \ --num_nodes ${NUM_NODES} \ --num_gpus ${NUM_GPUS} \ finetune.py \ --report_to "tensorboard" \ --data_path ${DATA_PATH} \ --model_name_or_path ${MODEL_PATH} \ --output_dir ${OUTPUT_DIR} \ --model_max_length ${MAX_LEN} \ --num_train_epochs ${EPOCHS} \ --per_device_train_batch_size ${BATCH_SIZE} \ --gradient_accumulation_steps 1 \ --save_strategy steps \ --save_steps 200 \ --learning_rate ${LR} \ --lr_scheduler_type cosine \ --adam_beta1 0.9 \ --adam_beta2 0.95 \ --adam_epsilon 1e-8 \ --max_grad_norm 1.0 \ --weight_decay 0.1 \ --warmup_ratio 0.01 \ --gradient_checkpointing True \ --bf16 True \ --tf32 True \ --deepspeed ${DS_CONFIG} \ --is_concat ${IS_CONCAT} \ --logging_steps 1 \ --log_on_each_node False ``` ```shell bash finetune/ds_finetune.sh ``` - By configuring the **hostfile**, single-machine and multi-machine training can be realized. - By configuring **ds_config**, realize zero2 and zero3 training - By configuring the **fp16**、**bf16** realize mixed precision training, bf16 is recommended to be consistent with the pre-trained model. - By configuring **is_concat**, Whether the training data is concatenated or not is controlled. When the magnitude of the training data is large, the training efficiency can be improved by concatenation.
# License The source code of this warehouse follows the open source license Apache 2.0. The 360 ​Zhinao open source model supports commercial use. If you need to use this model and its derivative models for commercial purposes, please contact us via email (g-zhinao-opensource@360.cn) to apply. For the specific license agreement, please see [《360 Zhinao Open Source Model License》](./360智脑开源模型许可证.txt).