Instructions to use GreenBitAI/LLaMA-3B-4bit-groupsize32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GreenBitAI/LLaMA-3B-4bit-groupsize32 with Transformers:

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

pipe = pipeline("text-generation", model="GreenBitAI/LLaMA-3B-4bit-groupsize32")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GreenBitAI/LLaMA-3B-4bit-groupsize32")
model = AutoModelForCausalLM.from_pretrained("GreenBitAI/LLaMA-3B-4bit-groupsize32")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GreenBitAI/LLaMA-3B-4bit-groupsize32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GreenBitAI/LLaMA-3B-4bit-groupsize32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GreenBitAI/LLaMA-3B-4bit-groupsize32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GreenBitAI/LLaMA-3B-4bit-groupsize32

SGLang

How to use GreenBitAI/LLaMA-3B-4bit-groupsize32 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 "GreenBitAI/LLaMA-3B-4bit-groupsize32" \
    --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": "GreenBitAI/LLaMA-3B-4bit-groupsize32",
		"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 "GreenBitAI/LLaMA-3B-4bit-groupsize32" \
        --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": "GreenBitAI/LLaMA-3B-4bit-groupsize32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GreenBitAI/LLaMA-3B-4bit-groupsize32 with Docker Model Runner:
```
docker model run hf.co/GreenBitAI/LLaMA-3B-4bit-groupsize32
```

GreenBit LLaMA

This is GreenBitAI's pretrained 4-bit LLaMA 3B model with advanced compression design and lossless performance to FP16 models.

Please refer to our Github page for the code to run the model and more information.

Model Description

Developed by: GreenBitAI
Model type: Causal (Llama)
Language(s) (NLP): English
License: Apache 2.0

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