Instructions to use recoilme/insomnia_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use recoilme/insomnia_v1 with Transformers:

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

pipe = pipeline("text-generation", model="recoilme/insomnia_v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("recoilme/insomnia_v1")
model = AutoModelForCausalLM.from_pretrained("recoilme/insomnia_v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use recoilme/insomnia_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "recoilme/insomnia_v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "recoilme/insomnia_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/recoilme/insomnia_v1

SGLang

How to use recoilme/insomnia_v1 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 "recoilme/insomnia_v1" \
    --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": "recoilme/insomnia_v1",
		"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 "recoilme/insomnia_v1" \
        --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": "recoilme/insomnia_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use recoilme/insomnia_v1 with Docker Model Runner:
```
docker model run hf.co/recoilme/insomnia_v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

v2 is out! https://huggingface.co/recoilme/insomnia_v2

Project by https://aiartlab.org/
A GPT2 model to generate prompts for SDXL or similar models.
Trained from the GPT2 small model. Attach a Style to affect the render further.
Trained on Syntetic prompts generated with Mistral7b.
Dataset: https://huggingface.co/datasets/recoilme/SyntheticPrompts

from transformers import pipeline, GPT2Tokenizer,GPT2LMHeadModel

checkpoint_path = "recoilme/insomnia_v2"
model = GPT2LMHeadModel.from_pretrained(checkpoint_path)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)

texts = [
    "Frog in a Harry Potter costume",
    "Cat, art by ",
    "a photo of woman underwater",
    "thunderstorms on the alien planet, very shocking",
    "Standing on the land of a new planet, the Female astronaut dances",
    "The face of the cat woman, a face beautiful, young. The head is adorned with the Egyptian crown of Bastet.",
]

for text in texts:
    print(f"Input: {text}:")
    out = text_generator(text, max_length=150, num_return_sequences=2,temperature=1.0,)
    print(f"Output 1: {out[0]['generated_text']}\n\n")
    print(f"Output 2: {out[1]['generated_text']}")
    print("\n")

Input: Frog in a Harry Potter costume:

Output 1: Frog in a Harry Potter costume, detailed with a touch of magical realism, highlight bulging eyes, slick skin, webbed feet, add atmospheric detail  misty breath, dawns first light at lilycovered pond, end with a nod to Gabriel Garca Mrquezs wizarding world.
Output 2: Frog in a Harry Potter costume, detailed and exact, persona reminiscent of a dragon or wizard duel, setting for a graveyard, atmosphere charged with suspense and anticipation, mystical creatures looming, cinematic style emphasizing amphibious grace.```

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for recoilme/insomnia_v1

Quantizations

1 model

recoilme
/

insomnia_v1

Model tree for recoilme/insomnia_v1

Dataset used to train recoilme/insomnia_v1