Instructions to use safe049/ParuMaid-Claude-Chinese-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use safe049/ParuMaid-Claude-Chinese-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="safe049/ParuMaid-Claude-Chinese-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("safe049/ParuMaid-Claude-Chinese-8B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use safe049/ParuMaid-Claude-Chinese-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "safe049/ParuMaid-Claude-Chinese-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "safe049/ParuMaid-Claude-Chinese-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/safe049/ParuMaid-Claude-Chinese-8B

SGLang

How to use safe049/ParuMaid-Claude-Chinese-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "safe049/ParuMaid-Claude-Chinese-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "safe049/ParuMaid-Claude-Chinese-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "safe049/ParuMaid-Claude-Chinese-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "safe049/ParuMaid-Claude-Chinese-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use safe049/ParuMaid-Claude-Chinese-8B with Docker Model Runner:
```
docker model run hf.co/safe049/ParuMaid-Claude-Chinese-8B
```

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using safe049/ParuMaid-Llama3-Chinese-8B as a base.

Models Merged

The following models were included in the merge:

Undi95/Meta-Llama-3.1-8B-Claude

Configuration

The following YAML configuration was used to produce this model:

# 使用 ties 方法进行模型合并的配置文件

base_model: "safe049/ParuMaid-Llama3-Chinese-8B"  # 设置 ParuMaid-Llama3-Chinese-8B 作为基础模型

models:
  - model: "safe049/ParuMaid-Llama3-Chinese-8B"
  - model: "Undi95/Meta-Llama-3.1-8B-Claude"
    parameters:
      density: 0.5  # 控制合并中的稀疏度
      weight: 0.5  # 合并权重，控制该模型对输出的影响

merge_method: ties  # 选择 ties 作为合并方法

parameters:
  normalize: false  # 是否进行归一化处理
  int8_mask: true  # 使用 int8 掩码以降低内存占用
dtype: float16  # 设置为半精度浮点数以减少显存需求

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for safe049/ParuMaid-Claude-Chinese-8B

Undi95/Meta-Llama-3.1-8B-Claude

safe049/ParuMaid-Llama3-Chinese-8B

Merge model

this model

Paper for safe049/ParuMaid-Claude-Chinese-8B

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19