Instructions to use ibm-granite/granite-20b-code-base-r1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-20b-code-base-r1.1 with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-20b-code-base-r1.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-20b-code-base-r1.1")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-20b-code-base-r1.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-20b-code-base-r1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-20b-code-base-r1.1"
# 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-20b-code-base-r1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-20b-code-base-r1.1

SGLang

How to use ibm-granite/granite-20b-code-base-r1.1 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-20b-code-base-r1.1" \
    --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-20b-code-base-r1.1",
		"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-20b-code-base-r1.1" \
        --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-20b-code-base-r1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ibm-granite/granite-20b-code-base-r1.1 with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-20b-code-base-r1.1
```

rpand002 commited on Jul 16, 2024

Commit

bfa91e1

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@@ -211,7 +211,14 @@ model-index:
 - **Release Date**: July 18th, 2024
 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
 ## Usage
 ### Intended use
 Prominent enterprise use cases of LLMs in software engineering productivity include code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation, and more. All Granite Code Base models, including the **20B parameter model**, are able to handle these tasks as they were trained on a large amount of code data from 116 programming languages.

 - **Release Date**: July 18th, 2024
 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+## Results on HumanEvalPack
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/648a7542ac1fbb8a1a654645/opFpDd55Jwd9dc4mVAT3r.jpeg" width="70%"/>
+</center>
 ## Usage
 ### Intended use
 Prominent enterprise use cases of LLMs in software engineering productivity include code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation, and more. All Granite Code Base models, including the **20B parameter model**, are able to handle these tasks as they were trained on a large amount of code data from 116 programming languages.