When multiple Vulkan-compatible GPUs are present (e.g., NVIDIA + AMD),
llama.cpp automatically distributes layers across all GPUs for performance.
This can cause unwanted VRAM allocation on the NVIDIA GPU when the user
wants to use only the AMD GPU.

The new --vulkan-single-gpu flag uses tensor_split to force all model
layers onto a single specified GPU device, preventing distribution.

- Added --vulkan-single-gpu argument
- Added count_vulkan_devices() method to detect GPU count
- Modified load_model to build tensor_split array when single_gpu=True
- Updated README with documentation for the new flag

Example usage:
  python coderai --model model.gguf --backend vulkan --vulkan-device 1 --vulkan-single-gpu

- Add --upgrade flag to pip install for llama-cpp-python in build.sh
  This ensures the latest version is installed, supporting newer models
  like Qwen3.5 that may not be supported in older versions
- Add note in README about updating llama-cpp-python for newer models
- Make coderai script executable

- Add --vulkan-device argument to select specific GPU (for multi-GPU systems)
- Add --vulkan-list-devices to list available Vulkan GPUs
- Update VulkanBackend to use main_gpu parameter for device selection
- Add list_vulkan_devices() method to show available devices
- Update README with new command-line options and examples

Useful when you have both NVIDIA and AMD GPUs and want to ensure
Vulkan uses the AMD GPU specifically.

- Comment out procname in requirements-nvidia.txt
- Comment out procname in requirements-vulkan.txt
- Add note about requiring libproc2-dev for procname

- build.sh: Update package list to include glslc, glslang-tools, glslang-dev
- README.md: Update installation instructions with correct package names
- Add better guidance for finding glslc in non-standard locations

- Update build.sh to check for glslc before attempting build
- Update README with correct package names (glslang-tools/glslang)
- Add troubleshooting for missing glslc error

- Add build.sh script with nvidia/vulkan arguments (default: nvidia)
- Create backend abstraction: ModelBackend base class
- Implement NvidiaBackend using HuggingFace Transformers
- Implement VulkanBackend using llama-cpp-python with GGUF models
- Add separate requirements files for nvidia and vulkan backends
- Add --backend argument with auto/nvidia/vulkan options
- Add Vulkan-specific options: --n-gpu-layers, --n-ctx
- Make procname import optional
- Update README with comprehensive Vulkan usage instructions
- Add Vulkan troubleshooting section
- Add GGUF model recommendations

The application now supports:
- NVIDIA GPUs via PyTorch/Transformers (HuggingFace models)
- AMD GPUs via llama-cpp-python/Vulkan (GGUF models)

- Add InvalidLogitsProcessor to replace NaN and Inf values with finite numbers
- Add _validate_generation_params() to clamp temperature and top_p to valid ranges
- Add try-except blocks with fallback to greedy decoding on numerical errors
- Add error handling in streaming responses to prevent crashes
- Fix temperature=0 handling to use greedy decoding instead of sampling

- Add main server script with FastAPI and memory-aware model loading
- Add requirements.txt with dependencies and platform-specific PyTorch options
- Add comprehensive README.md with installation, usage, and troubleshooting
- Add LICENSE.md with GPLv3 license