Instructions to use maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="maum-ai/Llama-3-MAAL-8B-Instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maum-ai/Llama-3-MAAL-8B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("maum-ai/Llama-3-MAAL-8B-Instruct-v0.1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1

SGLang

How to use maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 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 "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1" \
    --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": "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1",
		"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 "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1" \
        --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": "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1
```

New discussion

Resources

View closed (0)

MAAL-8B 모델을 잘 사용하고 있습니다. 학습을 해보고 싶은데 데이터 포맷을 어떻게 구성해야 하나요?

👍 1

#2 opened almost 2 years ago by

Kim3

마음의 8b모델을 잘 사용하고 있습니다. 혹시, 한국어 llama3 70b는 공개하실 예정이 없으신가요?

#1 opened almost 2 years ago by

keisey