Granite 3.3
Collection
Language models with improved reasoning and instruction-following capabilities. • 9 items • Updated • 47
How to use ibm-granite/granite-3.3-2b-base-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ibm-granite/granite-3.3-2b-base-GGUF") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ibm-granite/granite-3.3-2b-base-GGUF", device_map="auto")How to use ibm-granite/granite-3.3-2b-base-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
docker model run hf.co/ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
How to use ibm-granite/granite-3.3-2b-base-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-3.3-2b-base-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ibm-granite/granite-3.3-2b-base-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
How to use ibm-granite/granite-3.3-2b-base-GGUF with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ibm-granite/granite-3.3-2b-base-GGUF" \
--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": "ibm-granite/granite-3.3-2b-base-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "ibm-granite/granite-3.3-2b-base-GGUF" \
--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": "ibm-granite/granite-3.3-2b-base-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ibm-granite/granite-3.3-2b-base-GGUF with Ollama:
ollama run hf.co/ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
How to use ibm-granite/granite-3.3-2b-base-GGUF with Docker Model Runner:
docker model run hf.co/ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
How to use ibm-granite/granite-3.3-2b-base-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ibm-granite/granite-3.3-2b-base-GGUF:Q4_K_M
lemonade run user.granite-3.3-2b-base-GGUF-Q4_K_M
lemonade list
This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model.
Please reference the base model's full model card here: https://huggingface.co/ibm-granite/granite-3.3-2b-base
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16-bit
Base model
ibm-granite/granite-3.3-2b-base