Instructions to use svjack/summary-dialogue-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svjack/summary-dialogue-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="svjack/summary-dialogue-eng")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("svjack/summary-dialogue-eng") model = AutoModelForSeq2SeqLM.from_pretrained("svjack/summary-dialogue-eng") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use svjack/summary-dialogue-eng with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "svjack/summary-dialogue-eng" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "svjack/summary-dialogue-eng", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/svjack/summary-dialogue-eng
- SGLang
How to use svjack/summary-dialogue-eng 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 "svjack/summary-dialogue-eng" \ --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": "svjack/summary-dialogue-eng", "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 "svjack/summary-dialogue-eng" \ --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": "svjack/summary-dialogue-eng", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use svjack/summary-dialogue-eng with Docker Model Runner:
docker model run hf.co/svjack/summary-dialogue-eng
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
from transformers import T5ForConditionalGeneration
from transformers import T5TokenizerFast as T5Tokenizer
model = "svjack/summary-dialogue-eng"
device = "cpu"
tokenizer = T5Tokenizer.from_pretrained(model)
model = T5ForConditionalGeneration.from_pretrained(model).to(device).eval()
prompt = "The Wisconsin Territorial Centennial half dollar was designed by David Parsons and Benjamin Hawkins and minted by the United States Bureau of the Mint in 1936. The obverse (pictured) depicts a pick axe and lead ore, referring to the lead mining in early Wisconsin"
prompt = "{}\nCandidates:Tom Jack".format(prompt)
encode = tokenizer(prompt, return_tensors='pt').to(device)
answer = model.generate(encode.input_ids,
max_length = 128,
num_beams=2,
top_p = 0.95,
top_k = 50,
repetition_penalty = 2.5,
length_penalty=1.0,
early_stopping=True,
)[0]
decoded = tokenizer.decode(answer, skip_special_tokens=True)
decoded.replace("Tom:", "\n").replace("Jack:", "\n").split("\n")
['',
' Have you seen the Wisconsin Territorial Centennial half dollar? ',
' Yeah, it was designed by David Parsons and Benjamin Hawkins. ',
' What is it? ',
" It's a half dollar with a pick axe and lead ore. ",
" That's great!"]
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