Instructions to use RedHatAI/granite-3.1-2b-base-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/granite-3.1-2b-base-FP8-dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/granite-3.1-2b-base-FP8-dynamic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/granite-3.1-2b-base-FP8-dynamic") model = AutoModelForCausalLM.from_pretrained("RedHatAI/granite-3.1-2b-base-FP8-dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RedHatAI/granite-3.1-2b-base-FP8-dynamic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/granite-3.1-2b-base-FP8-dynamic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/granite-3.1-2b-base-FP8-dynamic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RedHatAI/granite-3.1-2b-base-FP8-dynamic
- SGLang
How to use RedHatAI/granite-3.1-2b-base-FP8-dynamic 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 "RedHatAI/granite-3.1-2b-base-FP8-dynamic" \ --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": "RedHatAI/granite-3.1-2b-base-FP8-dynamic", "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 "RedHatAI/granite-3.1-2b-base-FP8-dynamic" \ --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": "RedHatAI/granite-3.1-2b-base-FP8-dynamic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RedHatAI/granite-3.1-2b-base-FP8-dynamic with Docker Model Runner:
docker model run hf.co/RedHatAI/granite-3.1-2b-base-FP8-dynamic
Download recipe.yaml from RedHatAI/granite-3.1-2b-base-FP8-dynamic: direct link, hf CLI and curl.
- Browser
- Download file 384 Bytes
-
https://huggingface.co/RedHatAI/granite-3.1-2b-base-FP8-dynamic/resolve/main/recipe.yaml
- Command line
-
hf download hf://RedHatAI/granite-3.1-2b-base-FP8-dynamic/recipe.yaml
-
curl -L -o recipe.yaml https://huggingface.co/RedHatAI/granite-3.1-2b-base-FP8-dynamic/resolve/main/recipe.yaml
384 Bytes
| quant_stage: | |
| quant_modifiers: | |
| QuantizationModifier: | |
| ignore: [lm_head] | |
| config_groups: | |
| group_0: | |
| targets: [Linear] | |
| weights: {num_bits: 8, type: float, symmetric: true, strategy: channel, observer: mse} | |
| input_activations: {num_bits: 8, type: float, symmetric: true, strategy: token, | |
| dynamic: true, observer: memoryless} | |