Instructions to use suko/Meta-Llama-3-8B-CHT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suko/Meta-Llama-3-8B-CHT with Transformers:

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

pipe = pipeline("text-generation", model="suko/Meta-Llama-3-8B-CHT")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("suko/Meta-Llama-3-8B-CHT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use suko/Meta-Llama-3-8B-CHT 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 suko/Meta-Llama-3-8B-CHT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf suko/Meta-Llama-3-8B-CHT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf suko/Meta-Llama-3-8B-CHT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf suko/Meta-Llama-3-8B-CHT:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf suko/Meta-Llama-3-8B-CHT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf suko/Meta-Llama-3-8B-CHT:Q4_K_M

Use Docker

docker model run hf.co/suko/Meta-Llama-3-8B-CHT:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use suko/Meta-Llama-3-8B-CHT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suko/Meta-Llama-3-8B-CHT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suko/Meta-Llama-3-8B-CHT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/suko/Meta-Llama-3-8B-CHT:Q4_K_M

SGLang

How to use suko/Meta-Llama-3-8B-CHT 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 "suko/Meta-Llama-3-8B-CHT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suko/Meta-Llama-3-8B-CHT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "suko/Meta-Llama-3-8B-CHT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suko/Meta-Llama-3-8B-CHT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use suko/Meta-Llama-3-8B-CHT with Ollama:
```
ollama run hf.co/suko/Meta-Llama-3-8B-CHT:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use suko/Meta-Llama-3-8B-CHT with Docker Model Runner:
```
docker model run hf.co/suko/Meta-Llama-3-8B-CHT:Q4_K_M
```

Lemonade

How to use suko/Meta-Llama-3-8B-CHT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull suko/Meta-Llama-3-8B-CHT:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3-8B-CHT-Q4_K_M

List all available models

lemonade list

Atomic Chat

LLAMA 3 8B with capable to output Traditional Chinese

✨ Recommend using LMStudio for this model

I tried using Ollama to run it, but it became quite delulu.

So for now, I'm sticking with LMStudio :)The performance isn't actually that great, but it's capable of answering some basic questions. Sometimes it just acts really dumb though :(

LLAMA 3.1 can actually output pretty well Chinese, so this repo can be ignored.

Downloads last month: 72

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for suko/Meta-Llama-3-8B-CHT

Base model

meta-llama/Meta-Llama-3-8B

Quantized

unsloth/llama-3-8b-bnb-4bit

Quantized

(872)

this model

suko
/

Meta-Llama-3-8B-CHT

LLAMA 3 8B with capable to output Traditional Chinese

✨ Recommend using LMStudio for this model

Model tree for suko/Meta-Llama-3-8B-CHT

Dataset used to train suko/Meta-Llama-3-8B-CHT