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

Libraries

How to use jkhouja/Llama-3.2-1B-Instruct_ft with Transformers:

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

pipe = pipeline("text-generation", model="jkhouja/Llama-3.2-1B-Instruct_ft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkhouja/Llama-3.2-1B-Instruct_ft")
model = AutoModelForCausalLM.from_pretrained("jkhouja/Llama-3.2-1B-Instruct_ft")

Inference
Local Apps Settings

vLLM

How to use jkhouja/Llama-3.2-1B-Instruct_ft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jkhouja/Llama-3.2-1B-Instruct_ft

SGLang

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

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

Llama-3.2-1B-Instruct_ft / model.safetensors

jkhouja

Upload LlamaForCausalLM

7ba872f verified over 1 year ago

Download

History Blame

2.47 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:: 759cf8077c135830d65b6fcf239675296dc37bca8154ec78b3936d4971ef4f93
Size of remote file:: 2.47 GB
SHA256:: 6310bc2b965a417791af4139e4920d5a84224449213ba873521f42dce4810678

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