Instructions to use jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling with Transformers:

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

pipe = pipeline("text-generation", model="jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling")
model = AutoModelForCausalLM.from_pretrained("jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling

SGLang

How to use jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-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 "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling" \
    --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": "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling",
		"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 "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling" \
        --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": "jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling with Docker Model Runner:
```
docker model run hf.co/jacobbao/openbuddy-zephyr-7b-v14.1-code-orca-function-calling
```

Use function calling prompt as fallows:

<|system|>
You are a helpful assistant with access to the following functions. Use them if required 
- {
    "name": "calculate_discount",
    "description": "Calculate the discounted price based on original price and percentage",
    "parameters": {
      "type": "object",
      "properties": {
        "original_price": {
          "type": "number",
          "description": "The original price of the item"
        },
        "discount_percentage": {
          "type": "number",
          "description": "The percentage of discount to apply"
        }
      },
    "required": ["original_price","discount_percentage"]
    }
  }
<|user|>
Hi, I saw a dress in a store that costs $200. They are offering a 20% discount. Can you tell me how much it will cost after the discount?
<|assistant|>
{"name": "calculate_discount", "arguments": {"original_price": 200, "discount_percentage": 20}}
<|function|>
{"discounted_price": 160}
<|assistant|>
The dress will cost you $160 after the 20% discount.
<|user|>
That's great! Thank you for the help.
<|assistant|>
You're welcome! If you have any other questions, feel free to ask.

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