Instructions to use InterSync/Gemma-7B-Instruct-Function-Calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InterSync/Gemma-7B-Instruct-Function-Calling with Transformers:

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

pipe = pipeline("text-generation", model="InterSync/Gemma-7B-Instruct-Function-Calling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("InterSync/Gemma-7B-Instruct-Function-Calling")
model = AutoModelForCausalLM.from_pretrained("InterSync/Gemma-7B-Instruct-Function-Calling")
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 Settings

vLLM

How to use InterSync/Gemma-7B-Instruct-Function-Calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InterSync/Gemma-7B-Instruct-Function-Calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InterSync/Gemma-7B-Instruct-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/InterSync/Gemma-7B-Instruct-Function-Calling

SGLang

How to use InterSync/Gemma-7B-Instruct-Function-Calling 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 "InterSync/Gemma-7B-Instruct-Function-Calling" \
    --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": "InterSync/Gemma-7B-Instruct-Function-Calling",
		"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 "InterSync/Gemma-7B-Instruct-Function-Calling" \
        --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": "InterSync/Gemma-7B-Instruct-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use InterSync/Gemma-7B-Instruct-Function-Calling with Docker Model Runner:
```
docker model run hf.co/InterSync/Gemma-7B-Instruct-Function-Calling
```

kinqsradio commited on Mar 4, 2024

Commit

31cd13d

verified ·

1 Parent(s): 966a3eb

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -0

README.md CHANGED Viewed

@@ -1,3 +1,14 @@
 ---
 license: cc
 ---

 ---
 license: cc
 ---
+# Fine-tuned Gemma with OpenAI Function Call Support
+Finetuned version of [Gemma 7B Instruct ](https://huggingface.co/google/gemma-7b-it) to support direct function calling. This new capability aligns with the functionality seen in OpenAI's models, enabling Gemma to interact with external data sources and perform more complex tasks, such as fetching real-time information or integrating with custom databases for enriched AI-powered applications.
+## Features
+- **Direct Function Calls**: Gemma now supports structured function calls, allowing for the integration of external APIs and databases directly into the conversational flow. This makes it possible to execute custom searches, retrieve data from the web or specific databases, and even summarize or explain content in depth.
+## Quantization Models
+- Updating