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README.md

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+---
+license: apache-2.0
+tags:
+  - llama.cpp
+  - gguf
+  - gemma
+  - quantized
+  - cuda
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# llcuda Models
+
+Optimized GGUF models for llcuda - Zero-config CUDA-accelerated LLM inference.
+
+## Models
+
+### google_gemma-3-1b-it-Q4_K_M.gguf
+
+- **Model**: Google Gemma 3 1B Instruct
+- **Quantization**: Q4_K_M (4-bit)
+- **Size**: 769 MB
+- **Use case**: General-purpose chat, Q&A, code assistance
+- **Recommended for**: 1GB+ VRAM GPUs
+
+**Performance:**
+- Tesla T4 (Colab/Kaggle): ~15 tok/s
+- Tesla P100 (Colab): ~18 tok/s
+- GeForce 940M (1GB): ~15 tok/s
+- RTX 30xx/40xx: ~25+ tok/s
+
+## Usage
+
+### With llcuda (Recommended)
+
+```python
+pip install llcuda
+
+import llcuda
+engine = llcuda.InferenceEngine()
+engine.load_model("gemma-3-1b-Q4_K_M")
+result = engine.infer("What is AI?")
+print(result.text)
+```
+
+### With llama.cpp
+
+```bash
+# Download model
+huggingface-cli download waqasm86/llcuda-models google_gemma-3-1b-it-Q4_K_M.gguf --local-dir ./models
+
+# Run with llama.cpp
+./llama-server -m ./models/google_gemma-3-1b-it-Q4_K_M.gguf -ngl 26
+```
+
+## Supported Platforms
+
+- ✅ Google Colab (T4, P100, V100, A100)
+- ✅ Kaggle (Tesla T4)
+- ✅ Local GPUs (GeForce, RTX, Tesla)
+- ✅ All NVIDIA GPUs with compute capability 5.0+
+
+## Links
+
+- **PyPI**: [pypi.org/project/llcuda](https://pypi.org/project/llcuda/)
+- **GitHub**: [github.com/waqasm86/llcuda](https://github.com/waqasm86/llcuda)
+- **Documentation**: [waqasm86.github.io](https://waqasm86.github.io/)
+
+## License
+
+Apache 2.0 - Models are provided as-is for educational and research purposes.
+
+## Credits
+
+- Model: Google Gemma 3 1B
+- Quantization: llama.cpp GGUF format
+- Package: llcuda by Waqas Muhammad