Instructions to use ibm-granite/granite-3.1-3b-a800m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-3.1-3b-a800m-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ibm-granite/granite-3.1-3b-a800m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.1-3b-a800m-base")
model = AutoModelForMultimodalLM.from_pretrained("ibm-granite/granite-3.1-3b-a800m-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-3.1-3b-a800m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-3.1-3b-a800m-base"
# 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-3.1-3b-a800m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-3.1-3b-a800m-base

SGLang

How to use ibm-granite/granite-3.1-3b-a800m-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 "ibm-granite/granite-3.1-3b-a800m-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": "ibm-granite/granite-3.1-3b-a800m-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 "ibm-granite/granite-3.1-3b-a800m-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": "ibm-granite/granite-3.1-3b-a800m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ibm-granite/granite-3.1-3b-a800m-base with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-3.1-3b-a800m-base
```

EvalEvalBot commited on 14 days ago

Commit

083a73c

verified ·

1 Parent(s): c55c915

Add EvalEval community eval results

Browse files

Adds EvalEval Community Evals YAML entries with source backlinks to EEE aggregate records.

Contributor: evaleval

Files changed (1) hide show

.eval_results/mmlu-pro.yaml +1 -1

.eval_results/mmlu-pro.yaml CHANGED Viewed

@@ -1,6 +1,6 @@
 - dataset:
     id: TIGER-Lab/MMLU-Pro
-    task_id: mmlu-pro
   source:
     name: EvalEval
     url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/a9/31/a931868f-9ae1-4cd5-a2a7-8ea402101bbe.json

 - dataset:
     id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro
   source:
     name: EvalEval
     url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/a9/31/a931868f-9ae1-4cd5-a2a7-8ea402101bbe.json