Instructions to use Morpheus-Function-Calling/Morph-Caller with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Morpheus-Function-Calling/Morph-Caller with Transformers:

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

pipe = pipeline("text-generation", model="Morpheus-Function-Calling/Morph-Caller")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Morpheus-Function-Calling/Morph-Caller")
model = AutoModelForCausalLM.from_pretrained("Morpheus-Function-Calling/Morph-Caller")
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 Morpheus-Function-Calling/Morph-Caller with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Morpheus-Function-Calling/Morph-Caller

SGLang

How to use Morpheus-Function-Calling/Morph-Caller 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 "Morpheus-Function-Calling/Morph-Caller" \
    --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": "Morpheus-Function-Calling/Morph-Caller",
		"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 "Morpheus-Function-Calling/Morph-Caller" \
        --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": "Morpheus-Function-Calling/Morph-Caller",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Morpheus-Function-Calling/Morph-Caller with Docker Model Runner:
```
docker model run hf.co/Morpheus-Function-Calling/Morph-Caller
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Morph-Caller Model Card

Model Description

Morph-Caller is a state-of-the-art language model designed to perform function calling using a structured schema. It leverages a sophisticated system to parse and execute function calls, providing users with the ability to interact with the model in a more dynamic and utilitarian manner. The model differentiates itself by both excelling at function calling and also being unrestrained from model censorship.

Capabilities

Morph-Caller excels in understanding and generating structured outputs based on function calling schemas. It can interpret user queries that involve function calls and respond with accurate and relevant information. The model adheres to a predefined JSON schema for function calls, which is detailed in the Hermes-Function-Calling GitHub repository.

Usage

To interact with Morph-Caller, users should format their prompts according to the function calling schema provided in the repository. The model can process these prompts and return structured data, making it an invaluable tool for developers and researchers who require programmatic access to language model capabilities.

Installation and Usage

Please refer to the Hermes-Function-Calling GitHub repository for detailed instructions on installation and usage.

Contributions

Morph-Caller is built on the principles of open collaboration. We encourage contributions that improve the model's performance and extend its capabilities. If you are interested in contributing, please follow the contribution guidelines in the repository.

License

This model is available under the MIT license, which allows for a wide range of uses with few restrictions.

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Model size

8B params

Tensor type

BF16

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