Instructions to use RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16") model = AutoModelForCausalLM.from_pretrained("RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16") 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
- vLLM
How to use RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16
- SGLang
How to use RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16 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 "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16" \ --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": "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16", "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 "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16" \ --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": "RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16 with Docker Model Runner:
docker model run hf.co/RedHatAI/Meta-Llama-3-70B-Instruct-quantized.w4a16
Code example request with vllm
Can anyone give me some example code to use this model with vllm library?
I'm a newbie on LLM and vllm library.
Especially, I want what method or string should be in to quantization parameter:
model = LLM(model="neuralmagic/Meta-Llama-3-70B-Instruct-quantized.w4a16", tensor_parallel_size=4, quantization=)
You can just run with:
from vllm import LLM
model = LLM(model="neuralmagic/Meta-Llama-3-70B-Instruct-quantized.w4a16", tensor_parallel_size=4)
output = model.generate("Hello my name is")
You need not specify the quantization argument since it will be inferred from the checkpoint.
You could use the following code snippet:
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/Meta-Llama-3-70B-Instruct-quantized.w4a16"
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=300)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=4)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)