Instructions to use ctu-aic/Llama-3.1-8B_cp-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ctu-aic/Llama-3.1-8B_cp-mix with Transformers:

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

pipe = pipeline("text-generation", model="ctu-aic/Llama-3.1-8B_cp-mix")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ctu-aic/Llama-3.1-8B_cp-mix")
model = AutoModelForCausalLM.from_pretrained("ctu-aic/Llama-3.1-8B_cp-mix", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ctu-aic/Llama-3.1-8B_cp-mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ctu-aic/Llama-3.1-8B_cp-mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ctu-aic/Llama-3.1-8B_cp-mix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ctu-aic/Llama-3.1-8B_cp-mix

SGLang

How to use ctu-aic/Llama-3.1-8B_cp-mix 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 "ctu-aic/Llama-3.1-8B_cp-mix" \
    --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": "ctu-aic/Llama-3.1-8B_cp-mix",
		"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 "ctu-aic/Llama-3.1-8B_cp-mix" \
        --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": "ctu-aic/Llama-3.1-8B_cp-mix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ctu-aic/Llama-3.1-8B_cp-mix with Docker Model Runner:
```
docker model run hf.co/ctu-aic/Llama-3.1-8B_cp-mix
```

Llama-3.1-8B_cp-mix / model-00003-of-00004.safetensors

mlynatom

Upload LlamaForCausalLM

511c24d verified about 1 year ago

Download

History Blame Contribute Delete

4.92 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 55ba6dcc4a4f390d6d92255148aa0e2ca950e6c3d0a4b78db85f693d3f0939e7
Size of remote file:: 4.92 GB
SHA256:: 2d2432b5df9ab31baa1aa02f7eedbf6229196b81067ce29c9ccd3b1393d818a0

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.