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

Libraries

How to use pszemraj/distilgpt2-magicprompt-SD with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pszemraj/distilgpt2-magicprompt-SD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/distilgpt2-magicprompt-SD"
# 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-magicprompt-SD",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/distilgpt2-magicprompt-SD

SGLang

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

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

distilgpt2-magicprompt-SD

Generate/augment your prompt, stable diffusion style.

This model is a fine-tuned version of distilgpt2 on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:

Loss: 1.3089
eval_steps_per_second = 17.201
perplexity = 3.7022

example

Results in (DALL-E, but you get the idea):

this distilgpt2 version is probably small/fast enough to be used locally on CPU!

basic usage

install transformers as needed:

pip install -U transformers

load and query through a pipeline object:

from transformers import pipeline

model_tag = "pszemraj/distilgpt2-magicprompt-SD"
generator = pipeline(
    "text-generation",
    model=model_tag,
)

prompt = "The Answer to Why"
result = generator(
    prompt,
    max_new_tokens=24,
)  # generate, adjust/add kwargs as needed
print(result[0]["generated_text"])

Training and evaluation data

refer to the Gustavosta/Stable-Diffusion-Prompts dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 16
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 256
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.7061	0.99	33	2.5859
2.08	1.99	66	1.9965
1.7623	2.99	99	1.7248
1.5408	3.99	132	1.5449
1.4147	4.99	165	1.4437
1.3593	5.99	198	1.3768
1.2703	6.99	231	1.3362
1.2528	7.99	264	1.3175
1.1981	8.99	297	1.3091
1.2117	9.99	330	1.3089

Framework versions

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

Downloads last month: 16

Safetensors

Model size

88.2M params

Tensor type

F32

Model tree for pszemraj/distilgpt2-magicprompt-SD

Quantizations

2 models

pszemraj
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distilgpt2-magicprompt-SD