Instructions to use raicrits/OpenLLama13b_Loquace_ITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raicrits/OpenLLama13b_Loquace_ITA with Transformers:

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

pipe = pipeline("text-generation", model="raicrits/OpenLLama13b_Loquace_ITA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("raicrits/OpenLLama13b_Loquace_ITA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raicrits/OpenLLama13b_Loquace_ITA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raicrits/OpenLLama13b_Loquace_ITA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raicrits/OpenLLama13b_Loquace_ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raicrits/OpenLLama13b_Loquace_ITA

SGLang

How to use raicrits/OpenLLama13b_Loquace_ITA 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 "raicrits/OpenLLama13b_Loquace_ITA" \
    --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": "raicrits/OpenLLama13b_Loquace_ITA",
		"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 "raicrits/OpenLLama13b_Loquace_ITA" \
        --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": "raicrits/OpenLLama13b_Loquace_ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raicrits/OpenLLama13b_Loquace_ITA with Docker Model Runner:
```
docker model run hf.co/raicrits/OpenLLama13b_Loquace_ITA
```

OpenLLama13b_Loquace_ITA / model.safetensors.index.json

SFconvertbot

Adding `safetensors` variant of this model

e34560c verified about 1 year ago

Download

History Blame

35.1 kB

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:: 9e709e1b762754ae8679144519cd5817a94659e74dbaaa8d4c7ee647f0987c7a
Size of remote file:: 35.1 kB
SHA256:: 8598534cc93854483f791f3cc143b9e860b1b38ae6463894a931ec8c9c485a51

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