Instructions to use Pavloria/mini-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pavloria/mini-language-model with Transformers:

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

pipe = pipeline("text-generation", model="Pavloria/mini-language-model")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Pavloria/mini-language-model")
model = AutoModel.from_pretrained("Pavloria/mini-language-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pavloria/mini-language-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Pavloria/mini-language-model

SGLang

How to use Pavloria/mini-language-model 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 "Pavloria/mini-language-model" \
    --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": "Pavloria/mini-language-model",
		"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 "Pavloria/mini-language-model" \
        --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": "Pavloria/mini-language-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pavloria/mini-language-model with Docker Model Runner:
```
docker model run hf.co/Pavloria/mini-language-model
```

Mini Language Model

🧠 Model Description

This is a toy decoder-only language model based on a TransformerDecoder architecture. It was trained from scratch on the Tiny Shakespeare dataset using PyTorch.

The goal was to explore autoregressive language modeling using minimal resources and libraries like torch.nn and transformers.

🏋️ Training Details

Architecture: TransformerDecoder
Tokenizer: GPT2Tokenizer from Hugging Face
Vocabulary Size: 50257 (from GPT-2)
Sequence Length: 64 tokens
Batch Size: 8
Epochs: 5
Learning Rate: 1e-3
Number of Parameters: ~900k
Hardware: Trained on CPU (Google Colab)

📊 Evaluation

The model was evaluated on a 10% validation split. It shows consistent training and validation loss decrease, though it is not expected to produce coherent long text due to the small training size.

📂 Intended Use

This model is intended for educational purposes only. It is not suitable for production use.

🚫 Limitations

Only trained on a tiny dataset
Small architecture, limited capacity
Limited ability to generalize or generate meaningful long text

💬 Example Usage (Python)

python from transformers import GPT2Tokenizer from model import MiniDecoderModel # Assuming you restore the class

tokenizer = GPT2Tokenizer.from_pretrained("Pavloria/mini-language-model") model = MiniDecoderModel(...) # Load your config model.load_state_dict(torch.load("pytorch_model.bin"))

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