Instructions to use ibm-granite/granite-3b-code-instruct-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-3b-code-instruct-128k with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-3b-code-instruct-128k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3b-code-instruct-128k")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3b-code-instruct-128k", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-3b-code-instruct-128k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-3b-code-instruct-128k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3b-code-instruct-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-3b-code-instruct-128k

SGLang

How to use ibm-granite/granite-3b-code-instruct-128k 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-3b-code-instruct-128k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3b-code-instruct-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-3b-code-instruct-128k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3b-code-instruct-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ibm-granite/granite-3b-code-instruct-128k with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-3b-code-instruct-128k
```

rpand002 commited on Jul 16, 2024

Commit

8e873f4

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1 Parent(s): 7a884a9

Update README.md

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@@ -129,7 +129,7 @@ model-index:
 ## Usage
 ### Intended use
-The model is designed to respond to coding related instructions over long-conext input and can be used to build coding assistants.
 <!-- TO DO: Check starcoder2 instruct code example that includes the template https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 -->
@@ -167,7 +167,7 @@ for i in output:
 <!-- TO DO: Check this part -->
 ## Training Data
 Granite Code Instruct models are trained on a mix of short and long context data as follows.
-* Short-Context Instruction Data: [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft), [BigCode-SC2-Instruct](bigcode/self-oss-instruct-sc2-exec-filter-50k), [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [Glaive-Code-Assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [Glaive-Function-Calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [NL2SQL11](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction), [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer), [OpenPlatypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) including a synthetically generated dataset for API calling and multi-turn code interaction with execution feedback. We also include a collection of hardcoded prompts to ensure our model generates correct outputs given inquiries about its name or developers.
 * Long-Context Instruction Data: A synthetically-generated dataset by bootstrapping the repository-level file-packed documents through Granite-8b-Code-Instruct to improve long-context capability of the model.
 ## Infrastructure

 ## Usage
 ### Intended use
+The model is designed to respond to coding related instructions over long-conext input up to 128K length and can be used to build coding assistants.
 <!-- TO DO: Check starcoder2 instruct code example that includes the template https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 -->
 <!-- TO DO: Check this part -->
 ## Training Data
 Granite Code Instruct models are trained on a mix of short and long context data as follows.
+* Short-Context Instruction Data: [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft), [BigCode-SC2-Instruct](bigcode/self-oss-instruct-sc2-exec-filter-50k), [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [Glaive-Code-Assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [Glaive-Function-Calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [NL2SQL11](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction), [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer), [OpenPlatypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) including a synthetically generated dataset for API calling and multi-turn code interactions with execution feedback. We also include a collection of hardcoded prompts to ensure our model generates correct outputs given inquiries about its name or developers.
 * Long-Context Instruction Data: A synthetically-generated dataset by bootstrapping the repository-level file-packed documents through Granite-8b-Code-Instruct to improve long-context capability of the model.
 ## Infrastructure