Instructions to use jihyoung/rebot-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jihyoung/rebot-generation with Transformers:

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

pipe = pipeline("text-generation", model="jihyoung/rebot-generation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("jihyoung/rebot-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("jihyoung/rebot-generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jihyoung/rebot-generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jihyoung/rebot-generation

SGLang

How to use jihyoung/rebot-generation 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 "jihyoung/rebot-generation" \
    --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": "jihyoung/rebot-generation",
		"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 "jihyoung/rebot-generation" \
        --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": "jihyoung/rebot-generation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jihyoung/rebot-generation with Docker Model Runner:
```
docker model run hf.co/jihyoung/rebot-generation
```

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

👫ReBot - Generation Module⏰

ReBot is a novel multi-session dialgoue model which can generate dialogue with chronological dynamics! ReBot consists two modules: (1) chronological summarization module; (2) dialogue generation module.

This repoistory for dialogue generation module. You can check summarization module on this repoistory.

🚨 Please be cautious when testing our model with the Hosted Inference API. Our model takes sequences as input, so you should provide sequences as input through the API as well.

Model description

Paper: Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
Dataset : Conversation Chronicles
Generation Module of Model : this repoistory
Summarization Module of Model : chronological summarization module

Load with Transformers

To load our dataset with Hugging Face Transformers, please use the following code:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("jihyoung/rebot-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("jihyoung/rebot-generation")

Citation Information

@inproceedings{jang-etal-2023-conversation,
    title = "Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations",
    author = "Jang, Jihyoung  and
      Boo, Minseong  and
      Kim, Hyounghun",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.838",
    doi = "10.18653/v1/2023.emnlp-main.838",
    pages = "13584--13606",
}

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Dataset used to train jihyoung/rebot-generation

Paper for jihyoung/rebot-generation

Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations

Paper • 2310.13420 • Published Oct 20, 2023 • 2