Instructions to use argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF

SGLang

How to use argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF 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 "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF" \
    --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": "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF",
		"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 "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF" \
        --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": "argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/argearriojas/Llama-3.2-3B-Instruct-Q5_K_M-GGUF
```

Llama-3.2-3B-Instruct-Q5_K_M-GGUF / llama-3.2-3b-instruct-q5_k_m.gguf

argearriojas

Upload llama-3.2-3b-instruct-q5_k_m.gguf with huggingface_hub

f6352c3 verified over 1 year ago

Download

History Blame GGUF Editor new Delete

2.32 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:: 023da3e9945aba72ad2445978e9525d58c6e5c4411c88798b0294c95934986d7
Size of remote file:: 2.32 GB
SHA256:: 782f6d3246312ebfc5b3db83f2beaf5abf4371311206e9dc2c57703b564f55a0

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