Text Generation
Transformers
Safetensors
t5
text2text-generation
datadreamer
datadreamer-0.28.0
Synthetic
gpt-4
text-generation-inference
Instructions to use CCB/abstracts_to_tweet_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CCB/abstracts_to_tweet_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CCB/abstracts_to_tweet_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("CCB/abstracts_to_tweet_model") model = AutoModelForMultimodalLM.from_pretrained("CCB/abstracts_to_tweet_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CCB/abstracts_to_tweet_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CCB/abstracts_to_tweet_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CCB/abstracts_to_tweet_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CCB/abstracts_to_tweet_model
- SGLang
How to use CCB/abstracts_to_tweet_model 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 "CCB/abstracts_to_tweet_model" \ --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": "CCB/abstracts_to_tweet_model", "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 "CCB/abstracts_to_tweet_model" \ --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": "CCB/abstracts_to_tweet_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CCB/abstracts_to_tweet_model with Docker Model Runner:
docker model run hf.co/CCB/abstracts_to_tweet_model
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"model_card": {
"Date & Time": "2024-06-22T12:12:32.445548",
"Model Card": [
"https://huggingface.co/google/t5-v1_1-base"
],
"License Information": [
"apache-2.0"
],
"Citation Information": [
"\n@inproceedings{Wolf_Transformers_State-of-the-Art_Natural_2020,\n author = {Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien",
"\n@Misc{peft,\n title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},\n author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes"
]
},
"data_card": {
"Generate Research Paper Abstracts": {
"Date & Time": "2024-06-22T10:58:16.666642",
"Model Name": [
"gpt-4"
],
"Model Card": [
"https://cdn.openai.com/papers/gpt-4-system-card.pdf"
],
"License Information": [
"https://openai.com/policies"
],
"Citation Information": [
"@article{OpenAI2023GPT4TR,\n title={GPT-4 Technical Report},\n author={OpenAI},\n journal={ArXiv},\n year={2023},\n volume={abs/2303.08774},\n url={https://api.semanticscholar.org/CorpusID:257532815}\n}",
"@article{ouyang2022training,\n title={Training language models to follow instructions with human feedback},\n author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},\n journal={Advances in Neural Information Processing Systems},\n volume={35},\n pages={27730--27744},\n year={2022}\n}"
]
},
"Generate Tweets from Abstracts": {
"Date & Time": "2024-06-22T10:58:55.064072",
"Model Name": [
"gpt-4"
],
"Model Card": [
"https://cdn.openai.com/papers/gpt-4-system-card.pdf"
],
"License Information": [
"https://openai.com/policies"
],
"Citation Information": [
"@article{OpenAI2023GPT4TR,\n title={GPT-4 Technical Report},\n author={OpenAI},\n journal={ArXiv},\n year={2023},\n volume={abs/2303.08774},\n url={https://api.semanticscholar.org/CorpusID:257532815}\n}",
"@article{ouyang2022training,\n title={Training language models to follow instructions with human feedback},\n author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},\n journal={Advances in Neural Information Processing Systems},\n volume={35},\n pages={27730--27744},\n year={2022}\n}"
]
},
"Generate Tweets from Abstracts (train split)": {
"Date & Time": "2024-06-22T10:58:55.091788"
}
},
"__version__": "0.28.0",
"datetime": "2024-06-22T10:58:56.752118",
"type": "TrainHFFineTune",
"name": "Train an Abstract => Tweet Model",
"version": 1.0,
"fingerprint": "7ac09ad132f2a9e6",
"req_versions": {
"dill": "0.3.8",
"sqlitedict": "2.1.0",
"torch": "2.3.1",
"numpy": "1.26.4",
"transformers": "4.41.2",
"datasets": "2.20.0",
"huggingface_hub": "0.23.4",
"accelerate": "0.31.0",
"peft": "0.11.1",
"tiktoken": "0.7.0",
"tokenizers": "0.19.1",
"openai": "1.35.3",
"ctransformers": "0.2.27",
"optimum": "1.20.0",
"bitsandbytes": "0.42.0",
"litellm": "1.31.14",
"trl": "0.8.1",
"setfit": "1.0.3"
},
"interpreter": "3.10.14 (main, Mar 19 2024, 21:46:16) [Clang 15.0.0 (clang-1500.3.9.4)]"
} |