Instructions to use EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy with Transformers:

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

pipe = pipeline("text-generation", model="EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy

SGLang

How to use EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy 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 "EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy" \
    --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": "EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy",
		"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 "EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy" \
        --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": "EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy with Docker Model Runner:
```
docker model run hf.co/EmmanuelCasarrubias/AI-Transformer-Adaptive-Fuzzy
```

AI-Transformer-Adaptive-Fuzzy

2.91 kB

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1 contributor

History: 2 commits

EmmanuelCasarrubias

Added project features and creator profile. Included integrations with Hugging Face, Pinecone, ComposeDB, Chroma, Google Colab, and Google Cloud. Defined a model index entry for AI-Transformer-Adaptive-Fuzzy. Specified evaluation metrics for contextual text generation. Provided a placeholder for evaluation source.

401a291 verified about 2 years ago