Instructions to use yuhuili/EAGLE-LLaMA3-Instruct-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuhuili/EAGLE-LLaMA3-Instruct-70B with Transformers:

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

pipe = pipeline("text-generation", model="yuhuili/EAGLE-LLaMA3-Instruct-70B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuhuili/EAGLE-LLaMA3-Instruct-70B")
model = AutoModelForCausalLM.from_pretrained("yuhuili/EAGLE-LLaMA3-Instruct-70B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuhuili/EAGLE-LLaMA3-Instruct-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuhuili/EAGLE-LLaMA3-Instruct-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuhuili/EAGLE-LLaMA3-Instruct-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yuhuili/EAGLE-LLaMA3-Instruct-70B

SGLang

How to use yuhuili/EAGLE-LLaMA3-Instruct-70B 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 "yuhuili/EAGLE-LLaMA3-Instruct-70B" \
    --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": "yuhuili/EAGLE-LLaMA3-Instruct-70B",
		"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 "yuhuili/EAGLE-LLaMA3-Instruct-70B" \
        --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": "yuhuili/EAGLE-LLaMA3-Instruct-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yuhuili/EAGLE-LLaMA3-Instruct-70B with Docker Model Runner:
```
docker model run hf.co/yuhuili/EAGLE-LLaMA3-Instruct-70B
```

Create README.md

by mimicccccc - opened Oct 29, 2024

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-0

mimicccccc

Oct 29, 2024

add the readme file.

Create README.md971fb57a

yuhuili changed pull request status to merged Oct 29, 2024

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment