Instructions to use nvidia/Minitron-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Minitron-8B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Minitron-8B-Base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/Minitron-8B-Base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nvidia/Minitron-8B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Minitron-8B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Minitron-8B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nvidia/Minitron-8B-Base
- SGLang
How to use nvidia/Minitron-8B-Base 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 "nvidia/Minitron-8B-Base" \ --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": "nvidia/Minitron-8B-Base", "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 "nvidia/Minitron-8B-Base" \ --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": "nvidia/Minitron-8B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nvidia/Minitron-8B-Base with Docker Model Runner:
docker model run hf.co/nvidia/Minitron-8B-Base
Models Settings :
this model has better setting closer to the accetable setting:
32 layers / 4096 hidden size and the other nubers seem to have fallen closely into place in the PRime BInary markers:
1 2 4 8 16 32 64 128 :
there is a odd number ( 48 ) but it is still good as it is 1 /16 and 1/ 32 so its still good !
its imprtant to get the settings correxct , the pretraining is just data and can always be improved as well as the rrained methods !
but the settings are the most important for training and the associated mathmatics in the addition and subtraction of tensors , so because the number are binary aligned then the calculations will be faster ! so normalizing and finetuning and loss reduiction will also be faster ! hence ocnvergance is Faster: and retrieval is faster !
hopefull for thuis model !