Instructions to use ModelsLab/Flux-Prompt-Enhance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelsLab/Flux-Prompt-Enhance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ModelsLab/Flux-Prompt-Enhance")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ModelsLab/Flux-Prompt-Enhance") model = AutoModelForSeq2SeqLM.from_pretrained("ModelsLab/Flux-Prompt-Enhance") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ModelsLab/Flux-Prompt-Enhance with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ModelsLab/Flux-Prompt-Enhance" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ModelsLab/Flux-Prompt-Enhance", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ModelsLab/Flux-Prompt-Enhance
- SGLang
How to use ModelsLab/Flux-Prompt-Enhance 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 "ModelsLab/Flux-Prompt-Enhance" \ --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": "ModelsLab/Flux-Prompt-Enhance", "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 "ModelsLab/Flux-Prompt-Enhance" \ --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": "ModelsLab/Flux-Prompt-Enhance", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ModelsLab/Flux-Prompt-Enhance with Docker Model Runner:
docker model run hf.co/ModelsLab/Flux-Prompt-Enhance
File size: 1,504 Bytes
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"_name_or_path": "t5-base",
"architectures": [
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"d_ff": 3072,
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"d_model": 768,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
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"eos_token_id": 1,
"feed_forward_proj": "relu",
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"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 12,
"num_heads": 12,
"num_layers": 12,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"task_specific_params": {
"summarization": {
"early_stopping": true,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
},
"torch_dtype": "float32",
"transformers_version": "4.42.4",
"use_cache": true,
"vocab_size": 32128
}
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