Instructions to use autoevaluate/zero-shot-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autoevaluate/zero-shot-classification with Transformers:

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

pipe = pipeline("text-generation", model="autoevaluate/zero-shot-classification")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("autoevaluate/zero-shot-classification")
model = AutoModelForCausalLM.from_pretrained("autoevaluate/zero-shot-classification", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use autoevaluate/zero-shot-classification with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/autoevaluate/zero-shot-classification

SGLang

How to use autoevaluate/zero-shot-classification 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 "autoevaluate/zero-shot-classification" \
    --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": "autoevaluate/zero-shot-classification",
		"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 "autoevaluate/zero-shot-classification" \
        --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": "autoevaluate/zero-shot-classification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use autoevaluate/zero-shot-classification with Docker Model Runner:
```
docker model run hf.co/autoevaluate/zero-shot-classification
```

zero-shot-classification / README.md

lewtun HF Staff

Add evaluation results on the autoevaluate--zero-shot-classification-sample config and test split of autoevaluate/zero-shot-classification-sample

049bb19 over 3 years ago

1.19 kB

language: en
tags:
  - text-generation
  - opt
inference: false
model-index:
  - name: autoevaluate/zero-shot-classification
    results:
      - task:
          type: zero-shot-classification
          name: Zero-Shot Text Classification
        dataset:
          name: autoevaluate/zero-shot-classification-sample
          type: autoevaluate/zero-shot-classification-sample
          config: autoevaluate--zero-shot-classification-sample
          split: test
        metrics:
          - type: accuracy
            value: 0.6666666666666666
            name: Accuracy
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Q4YjIxMjdlYTJlNzE1NTE2MTgzM2RmMzU2Y2EzMDI0YWI0MWFkMzdhNGU4MzM5M2FmZGE2YWEzY2E5ZTEzNyIsInZlcnNpb24iOjF9.jBlcu153kaqz6iRGtOxG8Q9ynayhjZnIRwDp30-3gShW1UfQqXBsfv5dK-jxh03NT_uEhizWBPUt5Whc_s6XBw
          - type: loss
            value: 0.5084398140509924
            name: Loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMThjZjBkMWEwYTM0OTIzZjRjZmVmYzYyZjk2ZjYzZTRjOGIzM2U0MTE0MjljNGFlODk1ZDYwMjM3NTBmZWZmOCIsInZlcnNpb24iOjF9.BqM9rOmxo4xWUgyYr1g7c2gNZ5Q2N_F-QzLMVk2-OgE3zQNLhO_4vWEHRz-uPZFG_JW0i2JvheZV2zTXLcnQBA

Hello. I am a model, to be evaluated.