Instructions to use thu-coai/blenderbot-400M-esconv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thu-coai/blenderbot-400M-esconv with Transformers:

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

pipe = pipeline("text-generation", model="thu-coai/blenderbot-400M-esconv")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thu-coai/blenderbot-400M-esconv")
model = AutoModelForSeq2SeqLM.from_pretrained("thu-coai/blenderbot-400M-esconv")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use thu-coai/blenderbot-400M-esconv with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thu-coai/blenderbot-400M-esconv"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thu-coai/blenderbot-400M-esconv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thu-coai/blenderbot-400M-esconv

SGLang

How to use thu-coai/blenderbot-400M-esconv 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 "thu-coai/blenderbot-400M-esconv" \
    --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": "thu-coai/blenderbot-400M-esconv",
		"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 "thu-coai/blenderbot-400M-esconv" \
        --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": "thu-coai/blenderbot-400M-esconv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thu-coai/blenderbot-400M-esconv with Docker Model Runner:
```
docker model run hf.co/thu-coai/blenderbot-400M-esconv
```

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

blenderbot-400M-distill fine-tuned on the ESConv dataset. Usage example:

import torch
from transformers import AutoTokenizer
from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration

def _norm(x):
    return ' '.join(x.strip().split())

tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-400M-esconv')
model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-400M-esconv')
model.eval()

utterances = [
    "I am having a lot of anxiety about quitting my current job. It is too stressful but pays well",
    "What makes your job stressful for you?",
    "I have to deal with many people in hard financial situations and it is upsetting",
    "Do you help your clients to make it to a better financial situation?",
    "I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted",
]
input_sequence = '  '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces
input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:]
input_ids = torch.LongTensor([input_ids])

model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False)
generation = tokenizer.batch_decode(model_output, skip_special_tokens=True)
generation = [_norm(e) for e in generation]
print(generation)

utterances.append(generation[0]) # for future loop

Please kindly cite the original paper if you use this model:

@inproceedings{liu-etal-2021-towards,
  title={Towards Emotional Support Dialog Systems},
  author={Liu, Siyang  and 
    Zheng, Chujie  and 
    Demasi, Orianna  and 
    Sabour, Sahand  and 
    Li, Yu  and 
    Yu, Zhou  and 
    Jiang, Yong  and 
    Huang, Minlie},
  booktitle={Proceedings of the 59th annual meeting of the Association for Computational Linguistics},
  year={2021}
}

Downloads last month: 2,276

Safetensors

Model size

0.4B params

Tensor type

F32

Paper for thu-coai/blenderbot-400M-esconv

Towards Emotional Support Dialog Systems

Paper • 2106.01144 • Published Jun 2, 2021