Instructions to use destinyzxj/llama-3-chinese-8b-instruct-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with Transformers:

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

pipe = pipeline("text-generation", model="destinyzxj/llama-3-chinese-8b-instruct-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("destinyzxj/llama-3-chinese-8b-instruct-v3")
model = AutoModelForCausalLM.from_pretrained("destinyzxj/llama-3-chinese-8b-instruct-v3", device_map="auto")
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

llama.cpp

How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
# Run inference directly in the terminal:
llama cli -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
# Run inference directly in the terminal:
llama cli -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

Use Docker

docker model run hf.co/destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

LM Studio
Jan

vLLM

How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "destinyzxj/llama-3-chinese-8b-instruct-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "destinyzxj/llama-3-chinese-8b-instruct-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

SGLang

How to use destinyzxj/llama-3-chinese-8b-instruct-v3 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 "destinyzxj/llama-3-chinese-8b-instruct-v3" \
    --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": "destinyzxj/llama-3-chinese-8b-instruct-v3",
		"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 "destinyzxj/llama-3-chinese-8b-instruct-v3" \
        --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": "destinyzxj/llama-3-chinese-8b-instruct-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with Ollama:
```
ollama run hf.co/destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
```
Unsloth Desktop
Docker Model Runner
How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with Docker Model Runner:
```
docker model run hf.co/destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0
```

Lemonade

How to use destinyzxj/llama-3-chinese-8b-instruct-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull destinyzxj/llama-3-chinese-8b-instruct-v3:Q4_0

Run and chat with the model

lemonade run user.llama-3-chinese-8b-instruct-v3-Q4_0

List all available models

lemonade list

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3-Chinese-8B-Instruct-v3

[👉👉👉 Chat with Llama-3-Chinese-8B-Instruct-v3 @ HF Space]

This repository contains Llama-3-Chinese-8B-Instruct-v3, which is further finetuned based on a mix of the following models:

Note: This is an instruction (chat) model, which can be used for conversation, QA, etc.

Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3

Others

For GGUF model (llama.cpp compatible), please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3-gguf
If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3

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Safetensors

Model size

8B params

Tensor type

BF16

F16

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Quantizations

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