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

Libraries

How to use ibm-granite/granite-20b-functioncalling with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-20b-functioncalling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-20b-functioncalling")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-20b-functioncalling")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ibm-granite/granite-20b-functioncalling 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-functioncalling"
# 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-20b-functioncalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-20b-functioncalling

SGLang

How to use ibm-granite/granite-20b-functioncalling 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-functioncalling" \
    --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-20b-functioncalling",
		"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-20b-functioncalling" \
        --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-20b-functioncalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

rpand002 commited on Jul 10, 2024

Commit

d46c8b0

verified ·

1 Parent(s): f72b3d9

update template

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -1

tokenizer_config.json CHANGED Viewed

@@ -176,7 +176,7 @@
     "<reponame>"
   ],
   "bos_token": "<|endoftext|>",
-  "chat_template": "{% set function_str = messages.get('functions_str', {}) %}\n{% set query = messages['query'] %}\n{% set sys_prompt = 'You are a helpful assistant with access to the following function calls. Your task is to produce a sequence of function calls necessary to generate response to the user utterance. Use the following function calls as required. ' %}\n{% set funcstr = function_str|join('\n') %}\n{{ 'SYSTEM: ' + sys_prompt + '\n<|function_call_library|>\n' + funcstr + '\n\nIf none of the functions are relevant or the given question lacks the parameters required by the function, please output \"<function_call> {\"name\": \"no_function\", \"arguments\": {}}\".\n\nUSER: ' + query}}\n{% if add_generation_prompt %}\n{{ 'ASSISTANT:' }}\n{% endif %}\n",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
   "model_max_length": 8192,

     "<reponame>"
   ],
   "bos_token": "<|endoftext|>",
+  "chat_template": "{% set function_str = messages.get('functions_str', {}) %}\n{% set query = messages['query'] %}\n{% set sys_prompt = 'You are a helpful assistant with access to the following function calls. Your task is to produce a sequence of function calls necessary to generate response to the user utterance. Use the following function calls as required. ' %}\n{% set funcstr = function_str|join('\n') %}\n{{ 'SYSTEM: ' + sys_prompt + '\n<|function_call_library|>\n' + funcstr + '\n\nIf none of the functions are relevant or the given question lacks the parameters required by the function, please output \"<function_call> {\"name\": \"no_function\", \"arguments\": {}}\".\n\nUSER: ' + query}}\n{% if add_generation_prompt %}\n{{ 'ASSISTANT:' }}{% endif %}",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
   "model_max_length": 8192,