Instructions to use DarwinAnim8or/GPT-NoSleep-355m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarwinAnim8or/GPT-NoSleep-355m with Transformers:

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

pipe = pipeline("text-generation", model="DarwinAnim8or/GPT-NoSleep-355m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DarwinAnim8or/GPT-NoSleep-355m")
model = AutoModelForCausalLM.from_pretrained("DarwinAnim8or/GPT-NoSleep-355m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DarwinAnim8or/GPT-NoSleep-355m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarwinAnim8or/GPT-NoSleep-355m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarwinAnim8or/GPT-NoSleep-355m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DarwinAnim8or/GPT-NoSleep-355m

SGLang

How to use DarwinAnim8or/GPT-NoSleep-355m 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 "DarwinAnim8or/GPT-NoSleep-355m" \
    --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": "DarwinAnim8or/GPT-NoSleep-355m",
		"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 "DarwinAnim8or/GPT-NoSleep-355m" \
        --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": "DarwinAnim8or/GPT-NoSleep-355m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DarwinAnim8or/GPT-NoSleep-355m with Docker Model Runner:
```
docker model run hf.co/DarwinAnim8or/GPT-NoSleep-355m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GPT-NoSleep-355m

A finetuned version of GPT2-Medium on the 'reddit-nosleep-posts' dataset. (Linked above)

TIP You can find a larger, more capable version of the model here: GPT-NoSleep-1.5b

Training Procedure

This was trained on the 'reddt-nosleep-posts' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for X epochs with learning rate 1e-2.

Biases & Limitations

This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the dataset. It likely will generate offensive output.

Intended Use

This model is meant for fun, nothing else.

Sample code

#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-NoSleep-355m")

#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=3, do_sample=True, top_k=80, temperature=0.8, max_length=150, early_stopping=False)

#Generate a response:
result = happy_gen.generate_text("[WP] We don't go to the forest at night [RESPONSE] ", args=args_top_k)

print(result)
print(result.text)

Downloads last month: 6

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for DarwinAnim8or/GPT-NoSleep-355m

Quantizations

2 models

DarwinAnim8or
/

GPT-NoSleep-355m