Instructions to use itsme-nishanth/JAT-GPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itsme-nishanth/JAT-GPT with Transformers:

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

pipe = pipeline("text-generation", model="itsme-nishanth/JAT-GPT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("itsme-nishanth/JAT-GPT")
model = AutoModelForCausalLM.from_pretrained("itsme-nishanth/JAT-GPT")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use itsme-nishanth/JAT-GPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/itsme-nishanth/JAT-GPT

SGLang

How to use itsme-nishanth/JAT-GPT 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 "itsme-nishanth/JAT-GPT" \
    --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": "itsme-nishanth/JAT-GPT",
		"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 "itsme-nishanth/JAT-GPT" \
        --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": "itsme-nishanth/JAT-GPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

🧠 JAT-GPT: Just Another Tiny GPT

Welcome to JAT-GPT, the world's most underwhelming large language model — clocking in at a mighty 17.9 million parameters (yes, million, not billion — stop laughing).

📦 Model Details

Model type: GPT2-based decoder-only transformer
Architecture: GPT-2
Library: Hugging Face 🤗 Transformers
Parameters: 17.9 million (size isn't everything... right?)
Training Objective: Learn to predict the next word — and sometimes even the right one!
Pretrained on: A secret* dataset (*"secret" means the dataset was just some text I could find lying around)
Training Purpose: Solely educational. Also for flexing on friends who haven’t trained a language model from scratch.

🚀 Capabilities

Can generate small sentences
- "Please lower your expectations."
Can hallucinate confidently, but in a very short and polite way.
Can generate random words after few tokens.

🙅 Limitations

Not very smart.
Only Pretrained.
Understands context... if it fits within few tokens.
Cannot replace ChatGPT. (But look how cute it is!)

🤷 Why Train This?

"Because I could." – :-)

To understand the internals of language modeling.
To cry less when training real models later.
To appreciate just how powerful modern LLMs are by comparison.

🛠️ Usage

from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("itsme-nishanth/JAT-GPT")
model = GPT2LMHeadModel.from_pretrained("itsme-nishanth/JAT-GPT")

input_ids = tokenizer.encode("Once upon a time", return_tensors="pt")
output = model.generate(input_ids, max_length=20, do_sample=True)
print(tokenizer.decode(output[0]))

Downloads last month: 14

Safetensors

Model size

17.9M params

Tensor type

F32

Model tree for itsme-nishanth/JAT-GPT

Quantizations

1 model