Instructions to use knowledgator/flan-t5-large-for-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use knowledgator/flan-t5-large-for-classification with Transformers:

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

pipe = pipeline("text-generation", model="knowledgator/flan-t5-large-for-classification")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("knowledgator/flan-t5-large-for-classification")
model = AutoModelForSeq2SeqLM.from_pretrained("knowledgator/flan-t5-large-for-classification")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use knowledgator/flan-t5-large-for-classification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "knowledgator/flan-t5-large-for-classification"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "knowledgator/flan-t5-large-for-classification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/knowledgator/flan-t5-large-for-classification

SGLang

How to use knowledgator/flan-t5-large-for-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 "knowledgator/flan-t5-large-for-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": "knowledgator/flan-t5-large-for-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 "knowledgator/flan-t5-large-for-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": "knowledgator/flan-t5-large-for-classification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use knowledgator/flan-t5-large-for-classification with Docker Model Runner:
```
docker model run hf.co/knowledgator/flan-t5-large-for-classification
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

flan-t5-small-for-classification

This is an additional fine-tuned flan-t5-large model on many classification datasets.

The model supports prompt-tuned classification and is suitable for complex classification settings such as resumes classification by criteria.

You can use the model simply generating the text class name or using our unlimited-classifier.

The library allows to set constraints on generation and classify text into millions of classes.

How to use:

To use it with transformers library take a look into the following code snippet:

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("knowledgator/flan-t5-large-for-classification")
model = T5ForConditionalGeneration.from_pretrained("knowledgator/flan-t5-large-for-classification", device_map="auto")

input_text = "Define sentiment of the following text: I love to travel and someday I will see the world."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Using unlimited-classifier

# pip install unlimited-classifier

from unlimited_classifier import TextClassifier

classifier = TextClassifier(
    labels=[
        'positive',
        'negative',
        'neutral'    
    ],
    model='knowledgator/flan-t5-large-for-classification',
    tokenizer='knowledgator/flan-t5-large-for-classification',
)
output = classifier.invoke(input_text)
print(output)

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Collection of auto-regressive models tuned for text classification • 3 items • Updated Jan 29