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")

Notebooks
Google Colab
Kaggle
Local Apps

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 Tristan--zero_shot_classification_test config and test split of Tristan/zero_shot_classification_test

40d3f9e over 3 years ago

preview code

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645 Bytes

	---
	language: en
	inference: false
	tags:
	- text-generation
	- opt
	model-index:
	- name: autoevaluate/zero-shot-classification
	results:
	- task:
	type: zero-shot-classification
	name: Zero-Shot Text Classification
	dataset:
	name: Tristan/zero_shot_classification_test
	type: Tristan/zero_shot_classification_test
	config: Tristan--zero_shot_classification_test
	split: test
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.6666666666666666
	verified: true
	- name: Loss
	type: loss
	value: 0.5084401766459147
	verified: true
	---

	Hello. I am a model, to be evaluated.