Blurdus 7b V0.1

🚀 Blurdus-7b-v0.1

Blurdus-7b-v0.1 是一个通过合并多个模型而得到的模型。它借助 LazyMergekit 工具，将多个基础模型进行合并，在文本生成任务上展现出了一定的性能。

🚀 快速开始

安装依赖

!pip install -qU transformers accelerate

运行示例代码

from transformers import AutoTokenizer
import transformers
import torch

model = "222gate/Blurdus-7b-v0.1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

✨ 主要特性

Blurdus-7b-v0.1 模型通过合并以下模型而成：

• 222gate/Blurred-Beagle-7b-slerp
• 222gate/BrurryDog-7b-v0.1
• liminerity/Blur-7b-v1.21

📦 安装指南

运行以下命令安装所需依赖：

!pip install -qU transformers accelerate

💻 使用示例

基础用法

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "222gate/Blurdus-7b-v0.1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

📚 详细文档

🧩 配置信息

models:
  - model: 222gate/Blurred-Beagle-7b-slerp
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: 222gate/BrurryDog-7b-v0.1
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: liminerity/Blur-7b-v1.21
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: udkai/Turdus
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Open LLM Leaderboard 评估结果

详细结果可查看 此处

📄 许可证

本项目采用 Apache-2.0 许可证。