Instructions to use rubenz-org/llama_3_2_1b_instruct_MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubenz-org/llama_3_2_1b_instruct_MLC with Transformers:

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

pipe = pipeline("text-generation", model="rubenz-org/llama_3_2_1b_instruct_MLC")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rubenz-org/llama_3_2_1b_instruct_MLC")
model = AutoModelForCausalLM.from_pretrained("rubenz-org/llama_3_2_1b_instruct_MLC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rubenz-org/llama_3_2_1b_instruct_MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rubenz-org/llama_3_2_1b_instruct_MLC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rubenz-org/llama_3_2_1b_instruct_MLC",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rubenz-org/llama_3_2_1b_instruct_MLC

SGLang

How to use rubenz-org/llama_3_2_1b_instruct_MLC 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 "rubenz-org/llama_3_2_1b_instruct_MLC" \
    --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": "rubenz-org/llama_3_2_1b_instruct_MLC",
		"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 "rubenz-org/llama_3_2_1b_instruct_MLC" \
        --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": "rubenz-org/llama_3_2_1b_instruct_MLC",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rubenz-org/llama_3_2_1b_instruct_MLC with Docker Model Runner:
```
docker model run hf.co/rubenz-org/llama_3_2_1b_instruct_MLC
```

llama_3_2_1b_instruct_MLC / params_shard_11.bin

rubenz-org

Upload params_shard_11.bin with huggingface_hub

226087a verified 10 months ago

Download

History Blame

25.4 MB

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:: 3f183e59d21a03243a297d8183621124d51a833e18e559aeb91ecc6b5ce6a889
Size of remote file:: 25.4 MB
SHA256:: 6635931a7f1a1f896275a7845890a53200759b6aba23290ba1743229b9809128

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