Instructions to use GreenBitAI/LLaMA-3B-4bit-groupsize32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
File size: 534 Bytes
fd7ef62 5e4e96b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ---
license: apache-2.0
---
# 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](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information.
## Model Description
- **Developed by:** [GreenBitAI](https://github.com/GreenBitAI)
- **Model type:** Causal (Llama)
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |