Instructions to use johnsu6616/SD_Prompt_Generator_Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johnsu6616/SD_Prompt_Generator_Test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="johnsu6616/SD_Prompt_Generator_Test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("johnsu6616/SD_Prompt_Generator_Test") model = AutoModelForCausalLM.from_pretrained("johnsu6616/SD_Prompt_Generator_Test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use johnsu6616/SD_Prompt_Generator_Test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "johnsu6616/SD_Prompt_Generator_Test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "johnsu6616/SD_Prompt_Generator_Test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/johnsu6616/SD_Prompt_Generator_Test
- SGLang
How to use johnsu6616/SD_Prompt_Generator_Test 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 "johnsu6616/SD_Prompt_Generator_Test" \ --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": "johnsu6616/SD_Prompt_Generator_Test", "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 "johnsu6616/SD_Prompt_Generator_Test" \ --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": "johnsu6616/SD_Prompt_Generator_Test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use johnsu6616/SD_Prompt_Generator_Test with Docker Model Runner:
docker model run hf.co/johnsu6616/SD_Prompt_Generator_Test
Download config_Bert.json from johnsu6616/SD_Prompt_Generator_Test: direct link, hf CLI and curl.
- Browser
- Download file 568 Bytes
-
https://huggingface.co/johnsu6616/SD_Prompt_Generator_Test/resolve/main/config_Bert.json
- Command line
-
hf download hf://johnsu6616/SD_Prompt_Generator_Test/config_Bert.json
-
curl -L -o config_Bert.json https://huggingface.co/johnsu6616/SD_Prompt_Generator_Test/resolve/main/config_Bert.json
568 Bytes
| { | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.29.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |