Instructions to use agentlans/Llama3-zhcn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use agentlans/Llama3-zhcn with Transformers:

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

pipe = pipeline("text-generation", model="agentlans/Llama3-zhcn")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("agentlans/Llama3-zhcn")
model = AutoModelForCausalLM.from_pretrained("agentlans/Llama3-zhcn")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use agentlans/Llama3-zhcn with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "agentlans/Llama3-zhcn"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "agentlans/Llama3-zhcn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/agentlans/Llama3-zhcn

SGLang

How to use agentlans/Llama3-zhcn 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 "agentlans/Llama3-zhcn" \
    --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": "agentlans/Llama3-zhcn",
		"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 "agentlans/Llama3-zhcn" \
        --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": "agentlans/Llama3-zhcn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use agentlans/Llama3-zhcn with Docker Model Runner:
```
docker model run hf.co/agentlans/Llama3-zhcn
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3-zhcn

English

A Merged Llama 3 Model for Enhanced Chinese Understanding

This model is a merge of several pre-trained Llama 3 8B language models specifically focused on Simplified Chinese (China), created using mergekit.

Purpose

Llama3-zhcn aims to deliver a Llama 3 model with deep Chinese cultural, historical, and linguistic comprehension. This model serves dual purposes: handling diverse everyday tasks and providing a solid foundation for additional merging and fine-tuning. As Chinese model development trends away from the Llama series, this merge strives to maintain and improve Llama 3's Chinese language capabilities

Limitations

No Vision Capabilities: This model is based on Llama 3 and does not include vision capabilities found in some later models like 3.1 and 3.2.
Historical Accuracy: While the model possesses a good understanding of Chinese history, fact-checking is still recommended to ensure accuracy.
Translation and Revision: The model is capable of performing translations and revisions in English and Chinese. However, optimal results may require prompt engineering.