Instructions to use ibm-granite/granite-3.0-1b-a400m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-3.0-1b-a400m-base with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-3.0-1b-a400m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.0-1b-a400m-base")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3.0-1b-a400m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-3.0-1b-a400m-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-3.0-1b-a400m-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-3.0-1b-a400m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-3.0-1b-a400m-base

SGLang

How to use ibm-granite/granite-3.0-1b-a400m-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-3.0-1b-a400m-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-3.0-1b-a400m-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-3.0-1b-a400m-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-3.0-1b-a400m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ibm-granite/granite-3.0-1b-a400m-base with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-3.0-1b-a400m-base
```

amezasor commited on Oct 20, 2024

Commit

7234fc1

verified ·

1 Parent(s): 8cc2ce0

typo fix

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README.md +2 -2

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@@ -255,8 +255,8 @@ output = tokenizer.batch_decode(output)
 print(output)
 ```
-**Model Architeture:**
-Granite-3.0-1B-A400M-Base is based on a decoder-only sparse Mixture of Experts(MoE) transformer architecture. Core components of this architecture are: Fine-grained Experts, Dropless Token Routing, and Load Balancing Loss.
 | Model                        | 2B Dense | 8B Dense | 1B MoE       | 3B MoE   |
 | :--------                    | :--------| :--------| :--------    | :--------|

 print(output)
 ```
+**Model Architecture:**
+Granite-3.0-1B-A400M-Base is based on a decoder-only sparse Mixture of Experts (MoE) transformer architecture. Core components of this architecture are: Fine-grained Experts, Dropless Token Routing, and Load Balancing Loss.
 | Model                        | 2B Dense | 8B Dense | 1B MoE       | 3B MoE   |
 | :--------                    | :--------| :--------| :--------    | :--------|