BlossomLM
中英双语对话式大型语言模型
Stars: 55
BlossomLM is a series of open-source conversational large language models. This project aims to provide a high-quality general-purpose SFT dataset in both Chinese and English, making fine-tuning accessible while also providing pre-trained model weights. **Hint**: BlossomLM is a personal non-commercial project.
README:
Blossom是一系列开源的对话式大型语言模型。
本项目旨在提供一套高质量的中英双语通用SFT数据,让微调变得触手可及,同时提供训练后的模型权重。
Hint: BlossomLM是个人非商业化项目。
Demo模型为blossom-v5-34b,出于资源限制,使用了4bit量化部署,效果会有一定下降。
模型 | 参数量 | 预训练模型 |
---|---|---|
blossom-v5-34b GGUF 🌼演示 | 340亿 | 01-ai/Yi-34B |
blossom-v5-14b GGUF 🤗演示 | 140亿 | Qwen/Qwen1.5-14B |
blossom-v5-9b GGUF 🤗演示 | 90亿 | 01-ai/Yi-9B |
blossom-v5-4b GGUF 🤗演示 | 40亿 | Qwen/Qwen1.5-4B |
blossom-v5-mistral-7b GGUF 🤗演示 | 70亿 | mistralai/Mistral-7B-v0.1 |
blossom-v5-llama3-8b GGUF 🤗演示 | 80亿 | meta-llama/Meta-Llama-3-8B |
安装Ollama后即可一键启动,你可以打开模型列表查看全部可用模型(4b~34b)。
ollama run azure99/blossom-v5
如果希望将模型权重完全放置到GPU上,可以使用带有gpu后缀的tag。
ollama run azure99/blossom-v5:gpu
使用下面的命令进行安装,通过python web_demo.py
启动网页Demo。
注意:在安装pytorch时,请务必参考官方文档。
对于个人本地化部署场景,推荐使用Ollama;对于高并发场景,推荐使用vLLM。
git clone https://github.com/Azure99/BlossomLM.git
cd BlossomLM/inference/transformers
pip install -r requirements.txt
python web_demo.py
数据集 | 类型 | 数据量 |
---|---|---|
blossom-chat-v3 | 多轮通用对话 | 5K |
blossom-math-v4 | 包含推理过程的数学题目 | 10K |
blossom-orca-v3 | 解释型指令 | 40K |
blossom-wizard-v3 | 更复杂的指令 | 20K |
任何评估都具有局限性,不能完整反映模型的真实能力,许多模型使用评估集进行训练,进而在测试中取得极高的成绩,结果仅供参考。
模型 | 专业 | 中文 | 任务 | 数学 | 写作 | 问答 | 扮演 | 逻辑 | 推理 | 语言 | 总分 |
---|---|---|---|---|---|---|---|---|---|---|---|
gpt-4-0613 | 7.56 | 6.76 | 7.16 | 6.49 | 7.31 | 7.26 | 7.48 | 6.33 | 6.41 | 7.25 | 6.83 |
blossom-v5-34b | 8.35 | 7.20 | 7.02 | 5.47 | 7.85 | 8.44 | 7.76 | 6.09 | 5.78 | 7.77 | 6.78 |
blossom-v5-14b | 7.70 | 6.98 | 6.88 | 5.42 | 7.46 | 8.34 | 7.43 | 5.83 | 5.63 | 7.47 | 6.55 |
blossom-v5-9b | 7.41 | 7.06 | 7.08 | 4.93 | 7.56 | 8.78 | 7.56 | 5.42 | 5.18 | 7.57 | 6.38 |
yi-34b-chat-0205 | 7.63 | 7.55 | 6.95 | 4.40 | 7.66 | 7.94 | 7.43 | 5.76 | 5.08 | 7.53 | 6.30 |
gpt-3.5-turbo-0613 | 6.29 | 5.60 | 6.01 | 4.90 | 7.27 | 6.97 | 6.98 | 4.79 | 4.85 | 6.52 | 5.68 |
spark_desk_v2(讯飞星火) | 5.96 | 6.29 | 5.76 | 4.53 | 7.25 | 6.37 | 7.03 | 4.62 | 4.58 | 6.44 | 5.51 |
qwen-14b-chat | 5.98 | 5.84 | 6.46 | 4.54 | 6.47 | 6.71 | 6.38 | 4.50 | 4.52 | 6.31 | 5.41 |
qwen-7b-chat | 5.12 | 5.52 | 6.01 | 3.51 | 6.28 | 5.89 | 6.16 | 3.80 | 3.65 | 5.83 | 4.74 |
chatglm2-6b | 5.15 | 5.12 | 5.24 | 3.28 | 6.83 | 6.68 | 5.95 | 3.35 | 3.31 | 5.83 | 4.57 |
模型 | 第一轮 | 第二轮 | 总分 |
---|---|---|---|
gpt-4 | 8.96 | 9.02 | 8.99 |
blossom-v5-14b | 8.73 | 7.61 | 8.17 |
blossom-v5-34b | 8.38 | 7.66 | 8.02 |
gpt-3.5-turbo | 8.08 | 7.81 | 7.94 |
blossom-v5-9b | 8.26 | 7.33 | 7.80 |
blossom-v5-mistral-7b | 7.81 | 7.40 | 7.60 |
zephyr-7b-beta | - | - | 7.34 |
vicuna-33b-v1.3 | 7.46 | 6.78 | 7.12 |
qwen-14b-chat | - | - | 6.96 |
Mistral-7B-Instruct-v0.1 | - | - | 6.84 |
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