Instructions to use pszemraj/distilgpt2-multiprompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/distilgpt2-multiprompt with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/distilgpt2-multiprompt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/distilgpt2-multiprompt")
model = AutoModelForCausalLM.from_pretrained("pszemraj/distilgpt2-multiprompt")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pszemraj/distilgpt2-multiprompt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/distilgpt2-multiprompt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/distilgpt2-multiprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/distilgpt2-multiprompt

SGLang

How to use pszemraj/distilgpt2-multiprompt 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 "pszemraj/distilgpt2-multiprompt" \
    --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": "pszemraj/distilgpt2-multiprompt",
		"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 "pszemraj/distilgpt2-multiprompt" \
        --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": "pszemraj/distilgpt2-multiprompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/distilgpt2-multiprompt with Docker Model Runner:
```
docker model run hf.co/pszemraj/distilgpt2-multiprompt
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

distilgpt2-multiprompt

Generate/augment your prompt with a model trained on a large & diverse prompt dataset.

This model is a fine-tuned version of distilgpt2 on the pszemraj/text2image-prompts-multi dataset. It achieves the following results on the evaluation set:

Loss: 2.0213
perplexity = 7.55

Intended uses & limitations

The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets.

Training and evaluation data

See the pszemraj/text2image-prompts-multi dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface :)

Training procedure

this was trained with several training rounds, 8 epochs in total on the train set.

Training hyperparameters (last training round)

The following hyperparameters were used during training:

learning_rate: 0.0006
train_batch_size: 16
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 256
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.1637	1.0	965	2.0581
2.0885	2.0	1930	2.0213

Framework versions

Transformers 4.25.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.6.1
Tokenizers 0.13.1

Downloads last month: 5

Safetensors

Model size

88.2M params

Tensor type

F32

Model tree for pszemraj/distilgpt2-multiprompt

Quantizations

2 models

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distilgpt2-multiprompt