Instructions to use DarwinAnim8or/GPT-Greentext-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarwinAnim8or/GPT-Greentext-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="DarwinAnim8or/GPT-Greentext-1.5b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("DarwinAnim8or/GPT-Greentext-1.5b")
model = AutoModelForMultimodalLM.from_pretrained("DarwinAnim8or/GPT-Greentext-1.5b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DarwinAnim8or/GPT-Greentext-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarwinAnim8or/GPT-Greentext-1.5b"
# 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-Greentext-1.5b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DarwinAnim8or/GPT-Greentext-1.5b

SGLang

How to use DarwinAnim8or/GPT-Greentext-1.5b 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-Greentext-1.5b" \
    --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-Greentext-1.5b",
		"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-Greentext-1.5b" \
        --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-Greentext-1.5b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DarwinAnim8or/GPT-Greentext-1.5b with Docker Model Runner:
```
docker model run hf.co/DarwinAnim8or/GPT-Greentext-1.5b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GPT-Greentext-1.5b

A finetuned version of GPT2-XL on the 'greentext' dataset. A demo is available here The demo playground is recommended over the inference box on the right.

This is the largest release of the "GPT-Greentext" model series. The other models can be found here:

Training Procedure

This was trained on the 'greentext' dataset, on Google Colab. This model was trained for 1 epoch with learning rate 1e-2. Notably this uses the "prompt" and "completion" style jsonl file, rather than the plain text file found in the greentext dataset. This nets somewhat better, mostly more consistent results.

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 greentext dataset. It should be noted that offensive or not PG-output is definitely possible and likely will happen.

Intended Use

This model is meant for fun, nothing else.

Noteworthy differences between this model and the others

This model tends to like no_repeat_ngram_size values of 1 or 2; whereas the other models in this series tend to prefer 3.

Sample Use

#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-Greentext-1.5b")

#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=1, do_sample=True, top_k=80, temperature=0.8, max_length=150, early_stopping=False)

#Generate a response:
result = happy_gen.generate_text(""">be me
>""", args=args_top_k)

print(result)
print(result.text)

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Safetensors

Model size

2B params

Tensor type

F16

BOOL

Model tree for DarwinAnim8or/GPT-Greentext-1.5b

Quantizations

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DarwinAnim8or
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GPT-Greentext-1.5b