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

Libraries

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

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

pipe = pipeline("text-generation", model="Justus-Jonas/Imaginary-Embeddings-SpeakerTokens-STP")
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-SpeakerTokens-STP")
model = AutoModel.from_pretrained("Justus-Jonas/Imaginary-Embeddings-SpeakerTokens-STP")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Justus-Jonas/Imaginary-Embeddings-SpeakerTokens-STP 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-SpeakerTokens-STP"
# 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-SpeakerTokens-STP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use Justus-Jonas/Imaginary-Embeddings-SpeakerTokens-STP 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-SpeakerTokens-STP" \
    --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-SpeakerTokens-STP",
		"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-SpeakerTokens-STP" \
        --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-SpeakerTokens-STP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Justus-Jonas commited on Feb 22, 2024

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@@ -4,6 +4,9 @@ pipeline_tag: conversational
 datasets:
 - daily_dialog
 ---
 Imaginary Embeddings utilize Curved Contrastive Learning (see paper [Imagination Is All You Need!](https://arxiv.org/pdf/2211.07591.pdf) (ACL 2023)) on [Sentence Transformers](https://sbert.net/) for long-short term dialogue planning and efficient abstract sequence modeling.

 datasets:
 - daily_dialog
 ---
+⚠️ **This model is deprecated. Please don't use it as it produces embeddings of low quality.
+We recommend using [triple-encoders](https://huggingface.co/UKPLab/triple-encoders-dailydialog) 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!](https://arxiv.org/pdf/2211.07591.pdf) (ACL 2023)) on [Sentence Transformers](https://sbert.net/) for long-short term dialogue planning and efficient abstract sequence modeling.