Chicka Mixtral 3x7b

🚀 Chicka-Mistral-3x7b模型

Chicka-Mistral-3x7b是一个基于混合专家（Mixture of Experts）技术融合的大语言模型，它整合了三个基于Mistral架构的模型，在多种自然语言处理任务中展现出卓越的性能。

🚀 快速开始

使用以下Python代码示例，你可以快速加载并使用Chicka-Mistral-3x7b模型进行对话生成：

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("Chickaboo/Chicka-Mistral-3x7b")
tokenizer = AutoTokenizer.from_pretrained("Chickaboo/Chicka-Mixtral-3x7b")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

✨ 主要特性

• 混合专家架构：融合了三个基于Mistral的模型，分别在对话、代码和数学领域具有专长，实现了多领域能力的增强。
• 高性能表现：在多个基准测试中表现出色，如ARC、Hellaswag、TruthfulQA等，展现了其在知识理解、推理和生成方面的强大能力。

📦 安装指南

本README未提供具体安装步骤，你可以参考transformers库的官方文档进行模型的安装和使用。

💻 使用示例

基础用法

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("Chickaboo/Chicka-Mistral-3x7b")
tokenizer = AutoTokenizer.from_pretrained("Chickaboo/Chicka-Mixtral-3x7b")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

📚 详细文档

模型描述

该模型是一个基于混合专家（Mixture of Experts）技术融合的大语言模型，由三个基于Mistral的模型组成：

• 基础模型/对话专家：openchat/openchat-3.5-0106
• 代码专家：beowolx/CodeNinja-1.0-OpenChat-7B
• 数学专家：meta-math/MetaMath-Mistral-7B

以下是合并过程中使用的Mergekit配置：

base_model: openchat/openchat-3.5-0106
experts:
  - source_model: openchat/openchat-3.5-0106
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    - "I want"
  - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
    - "C#"
    - "C++"
    - "debug"
    - "runtime"
    - "html"
    - "command"
    - "nodejs"
  - source_model: meta-math/MetaMath-Mistral-7B
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"
    - "calculate"
    - "arithmetic"
    - "algebra"

开放大语言模型排行榜

📄 许可证

本模型采用MIT许可证。