Instructions to use Justus-Jonas/Imaginary-Embeddings-Classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Justus-Jonas/Imaginary-Embeddings-Classic with Transformers:

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

pipe = pipeline("text-generation", model="Justus-Jonas/Imaginary-Embeddings-Classic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Justus-Jonas/Imaginary-Embeddings-Classic")
model = AutoModel.from_pretrained("Justus-Jonas/Imaginary-Embeddings-Classic")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Justus-Jonas/Imaginary-Embeddings-Classic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Justus-Jonas/Imaginary-Embeddings-Classic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Justus-Jonas/Imaginary-Embeddings-Classic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Justus-Jonas/Imaginary-Embeddings-Classic

SGLang

How to use Justus-Jonas/Imaginary-Embeddings-Classic 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 "Justus-Jonas/Imaginary-Embeddings-Classic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Justus-Jonas/Imaginary-Embeddings-Classic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Justus-Jonas/Imaginary-Embeddings-Classic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Justus-Jonas/Imaginary-Embeddings-Classic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Justus-Jonas/Imaginary-Embeddings-Classic with Docker Model Runner:
```
docker model run hf.co/Justus-Jonas/Imaginary-Embeddings-Classic
```

YAML Metadata Warning:The pipeline tag "conversational" 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

⚠️ This model is deprecated. Please don't use it as it produces embeddings of low quality. We recommend using triple-encoders instead, also if you want to use them as a classic bi-encoder.

Imaginary Embeddings utilize Curved Contrastive Learning (see paper Imagination Is All You Need! (ACL 2023)) on Sentence Transformers for long-short term dialogue planning and efficient abstract sequence modeling.

This model does not use speaker tokens and was evaluated in the Long-Term planning and sequence modeling experiments.

setup

python -m pip install imaginaryNLP

Usage Sequence Modeling:

from imaginaryNLP.ImaginaryEmbeddingsForSequenceModeling import EvalImaginaryEmbeddingsForSequenceModeling

# Load the model
seq = EvalImaginaryEmbeddingsForSequenceModeling('Justus-Jonas/Imaginary-Embeddings-Classic', speaker_token=False)

# add candidates and context
seq.load_candidates_from_strings(["I'm fine, thanks. How are you?", "Where did you go?", "ACL is an interesting conference"])

# create context, pre-compute and keep 80% of utterances
seq.create_context(["Hi!",'Hey, how are you?'], precompute_top_p=0.8)

seq.sequence_modeling_with_precompute("I am doing good. Today I went for a walk. ")

Long-Term-Planning

from imaginaryNLP.ImaginaryEmbeddingsForLTP import ImaginaryEmbeddingsForLTP

ltp = ImaginaryEmbeddingsForLTP('Justus-Jonas/Imaginary-Embeddings-Classic', speaker_token=False)

# add a contex
ltp.create_context([' Hello', 'Hi , great to meet you ! '])

# add goals
ltp.add_goal(" great to hear that ! ")
ltp.add_goal(" Want to go for a walk ? ")
ltp.add_goal(" Bye !")

# greedy curving
ltp.greedy_curving()

# imaginary embedding chains
ltp.imaginary_embedding_chains()

# imaginary embedding chains with curving
ltp.imaginary_embedding_chains_with_curving()

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Safetensors

Model size

0.1B params

Tensor type

I64

F32

Dataset used to train Justus-Jonas/Imaginary-Embeddings-Classic

Paper for Justus-Jonas/Imaginary-Embeddings-Classic

Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning

Paper • 2211.07591 • Published Nov 14, 2022