Instructions to use substratusai/weaviate-gorilla-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use substratusai/weaviate-gorilla-v4 with Transformers:

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

pipe = pipeline("text-generation", model="substratusai/weaviate-gorilla-v4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("substratusai/weaviate-gorilla-v4")
model = AutoModelForCausalLM.from_pretrained("substratusai/weaviate-gorilla-v4")

Inference
Local Apps Settings

vLLM

How to use substratusai/weaviate-gorilla-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "substratusai/weaviate-gorilla-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "substratusai/weaviate-gorilla-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/substratusai/weaviate-gorilla-v4

SGLang

How to use substratusai/weaviate-gorilla-v4 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 "substratusai/weaviate-gorilla-v4" \
    --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": "substratusai/weaviate-gorilla-v4",
		"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 "substratusai/weaviate-gorilla-v4" \
        --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": "substratusai/weaviate-gorilla-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use substratusai/weaviate-gorilla-v4 with Docker Model Runner:
```
docker model run hf.co/substratusai/weaviate-gorilla-v4
```

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Check out the documentation for more information.

Example usage

https://colab.research.google.com/drive/1e4Gw459nXUfW__fm13bTlC5QXziXrixM?usp=sharing

Prompt template

## Instruction
Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.

Only use the API reference to understand the syntax of the request.

## Natural Language Query
{nlcommand}

## Schema
{schema}

## API reference
{apiRef}

## Answer
{output}

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Example usage

Prompt template

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