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
```

Pavloria commited on Apr 10, 2025

Commit

95cb943

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1 Parent(s): 7c2b43c

Update README.md

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@@ -13,4 +13,38 @@ pipeline_tag: text-generation
 # Mini Language Model
-This is a toy decoder-only model trained on Tiny Shakespeare.

 # 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](https://huggingface.co/datasets/tiny_shakespeare) 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"))