Instructions to use medmekk/Llama-3.2-1B-ao-int8da8w with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use medmekk/Llama-3.2-1B-ao-int8da8w with Transformers:

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

pipe = pipeline("text-generation", model="medmekk/Llama-3.2-1B-ao-int8da8w")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("medmekk/Llama-3.2-1B-ao-int8da8w")
model = AutoModel.from_pretrained("medmekk/Llama-3.2-1B-ao-int8da8w")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use medmekk/Llama-3.2-1B-ao-int8da8w with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "medmekk/Llama-3.2-1B-ao-int8da8w"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "medmekk/Llama-3.2-1B-ao-int8da8w",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/medmekk/Llama-3.2-1B-ao-int8da8w

SGLang

How to use medmekk/Llama-3.2-1B-ao-int8da8w 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 "medmekk/Llama-3.2-1B-ao-int8da8w" \
    --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": "medmekk/Llama-3.2-1B-ao-int8da8w",
		"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 "medmekk/Llama-3.2-1B-ao-int8da8w" \
        --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": "medmekk/Llama-3.2-1B-ao-int8da8w",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use medmekk/Llama-3.2-1B-ao-int8da8w with Docker Model Runner:
```
docker model run hf.co/medmekk/Llama-3.2-1B-ao-int8da8w
```

Llama-3.2-1B-ao-int8da8w / pytorch_model.bin

medmekk

Upload folder using huggingface_hub

2c93618 verified about 1 year ago

download

history blame contribute delete

1.5 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:: 3588f67e32608cd7e42cf1a559a737a5a0029fbec2a1383bcabad63ba6ce2004
Size of remote file:: 1.5 GB
SHA256:: 8d2a08e59911be20c889c802a5ca0bec4974b457cbb4a49bf69c5da62db114ec

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