Instructions to use TheBloke/StableBeluga2-70B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/StableBeluga2-70B-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/StableBeluga2-70B-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/StableBeluga2-70B-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/StableBeluga2-70B-GPTQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/StableBeluga2-70B-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/StableBeluga2-70B-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/StableBeluga2-70B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/StableBeluga2-70B-GPTQ
- SGLang
How to use TheBloke/StableBeluga2-70B-GPTQ 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 "TheBloke/StableBeluga2-70B-GPTQ" \ --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": "TheBloke/StableBeluga2-70B-GPTQ", "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 "TheBloke/StableBeluga2-70B-GPTQ" \ --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": "TheBloke/StableBeluga2-70B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/StableBeluga2-70B-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/StableBeluga2-70B-GPTQ
Why so few 8 bit capable models?
Just genuine curiosity, it seems like things are either full on 32bit, fp16 or 4bit/3bit quantized for the most part. Is there something special about 8bit quantized that makes it undesirable? For instance I can easily fit the large models at 4bit but an fp16 of them stretches beyond my vram. 8bit would fit and use up in most cases a larger amount of my VRAM. Is it a performance thing that there isn't much difference between 8 and 4 or is it more that there aren't many people that could do 8 and not do 16 so there just isn't a demand? It just seemed really odd to me that 8bit just isn't very prevalent at all in the community.
Just genuine curiosity, it seems like things are either full on 32bit, fp16 or 4bit/3bit quantized for the most part. Is there something special about 8bit quantized that makes it undesirable? For instance I can easily fit the large models at 4bit but an fp16 of them stretches beyond my vram. 8bit would fit and use up in most cases a larger amount of my VRAM. Is it a performance thing that there isn't much difference between 8 and 4 or is it more that there aren't many people that could do 8 and not do 16 so there just isn't a demand? It just seemed really odd to me that 8bit just isn't very prevalent at all in the community.
Very simple. 8-bit is slower than 4-bit because of memory bandwidth. There's just more gigabytes to copy around.
On CPU however (ggml llama.cpp), TheBloke does often provide an 8-bit option.
I agree that 4-bit quantization is no good. 5-bit should be a minimum.