Update documentation for v1.3.0 release

- Add v1.3.0 release notes with new model support (Qwen2.5-VL, Qwen3 Dense & Sparse, improved MLLama)
- Update both main README.md and ollama37/README.md for consistency
- Add CLAUDE.md for future Claude Code instances
- Enhanced Docker Hub documentation

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

CLAUDE.md

@@ -0,0 +1,92 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a laboratory for running Ollama (local LLM runner) on NVIDIA K80 GPUs with custom Docker builds optimized for CUDA 11.4 compatibility. The project focuses on LLM-powered workflow automation for software quality assurance, integrating with tools like Dify, VS Code Continue plugin, N8N, and auto-webui.
+
+## Docker Commands
+
+### Running Ollama
+```bash
+# Pull and run the custom K80-optimized Ollama image
+docker pull dogkeeper886/ollama37
+docker run --runtime=nvidia --gpus all -p 11434:11434 dogkeeper886/ollama37
+
+# Using docker-compose (recommended for persistent data)
+cd ollama37/
+docker-compose up -d
+
+# Stop the service
+docker-compose down
+```
+
+### Building Custom Images
+```bash
+# Build the builder image (contains CUDA 11.4, GCC 10, CMake, Go)
+cd ollama37-builder/
+docker build -t dogkeeper886/ollama37-builder .
+
+# Build the runtime image
+cd ollama37/
+docker build -t dogkeeper886/ollama37 .
+```
+
+## Architecture
+
+### Core Components
+
+1. **ollama37-builder/**: Multi-stage Docker build environment
+   - Rocky Linux 8 base with NVIDIA drivers 470
+   - CUDA 11.4 toolkit for K80 GPU compatibility
+   - Custom-compiled GCC 10, CMake 4.0, Go 1.24.2
+   - Environment setup scripts for proper library paths
+
+2. **ollama37/**: Runtime Docker image
+   - Compiled Ollama binary optimized for K80
+   - Minimal runtime environment with required CUDA libraries
+   - Exposes Ollama API on port 11434
+   - Persistent volume support for model storage
+
+3. **dify/**: Workflow automation configurations
+   - YAML workflow definitions for LLM-powered QA tasks
+   - Python utilities for Atlassian/Jira integration (`format_jira_ticket.py`)
+   - Workflow templates: BugBlitz, QualityQuest, ER2Test, etc.
+   - Knowledge base with PDF documentation for various systems
+
+4. **mcp-servers/**: Model Context Protocol integrations
+   - Web browser MCP server for enhanced LLM capabilities
+
+### Key Environment Variables
+- `OLLAMA_HOST=0.0.0.0:11434` - API endpoint
+- `LD_LIBRARY_PATH="/usr/local/lib64:/usr/local/cuda-11.4/lib64"` - CUDA libraries
+- `NVIDIA_DRIVER_CAPABILITIES=compute,utility` - GPU capabilities
+- `NVIDIA_VISIBLE_DEVICES=all` - GPU visibility
+
+### Hardware Requirements
+- NVIDIA K80 GPU
+- NVIDIA Tesla K80 driver installed
+- NVIDIA Container Runtime for Docker
+- Sufficient storage for model downloads (models stored in `./volume/` when using docker-compose)
+
+## Development Workflow
+
+### Model Testing
+The project supports running various LLM models optimized for K80:
+- Qwen2.5-VL (multi-modal vision-language model)
+- Qwen3 Dense & Sparse variants
+- Improved MLLama models
+- Gemma 3 12B  
+- Phi-4 Reasoning 14B
+- DeepSeek-R1:32B
+
+### Quality Assurance Integration
+The Dify workflows enable automated processing of:
+- Jira tickets to Markdown conversion
+- Requirements analysis and test generation
+- Documentation refinement
+- Bug report processing
+
+### Persistent Data
+When using docker-compose, model data persists in `./volume/` directory, mapped to `/root/.ollama` inside the container.

README.md

@@ -20,6 +20,19 @@ This repository includes a customized version of Ollama, specifically optimized
 
 ### 📦 Version History
 
+#### v1.3.0 (2025-07-01)
+
+This release expands model support while maintaining full Tesla K80 compatibility:
+
+**New Model Support:**
+- **Qwen2.5-VL**: Multi-modal vision-language model for image understanding
+- **Qwen3 Dense & Sparse**: Enhanced Qwen3 model variants
+- **Improved MLLama**: Better support for Meta's LLaMA models
+
+**Documentation Updates:**
+- Updated installation guides for Tesla K80 compatibility
+- Enhanced Docker Hub documentation with latest model information
+
 #### v1.2.0 (2025-05-06)
 
 This release introduces support for Qwen3 models, marking a significant step in our commitment to staying Tesla K80 with leading open-source language models. Testing includes successful execution of Gemma 3 12B, Phi-4 Reasoning 14B, and Qwen3 14B, ensuring compatibility with models expected to be widely used in May 2025.

ollama37/README.md

@@ -14,8 +14,11 @@ This setup ensures that users can start experimenting with AI models without the
 ## Features
 
 - **GPU Acceleration**: Fully supports NVIDIA K80 GPUs to accelerate model computations.
+- **Multi-Modal AI**: Supports vision-language models like Qwen2.5-VL for image understanding.
+- **Advanced Reasoning**: Built-in thinking support for enhanced AI reasoning capabilities.
 - **Pre-built Binary**: Contains the compiled Ollama binary for immediate use.
 - **CUDA Libraries**: Includes necessary CUDA libraries and drivers for GPU operations.
+- **Enhanced Tool Support**: Improved tool calling and WebP image input support.
 - **Environment Variables**: Configured to facilitate seamless interaction with the GPU and network settings.
 
 ## Usage
@@ -99,6 +102,19 @@ This will stop and remove the container, but the data stored in the `.ollama` di
 
 ## 📦 Version History
 
+### v1.3.0 (2025-07-01)
+
+This release expands model support while maintaining full Tesla K80 compatibility:
+
+**New Model Support:**
+- **Qwen2.5-VL**: Multi-modal vision-language model for image understanding
+- **Qwen3 Dense & Sparse**: Enhanced Qwen3 model variants
+- **Improved MLLama**: Better support for Meta's LLaMA models
+
+**Documentation Updates:**
+- Updated installation guides for Tesla K80 compatibility
+- Enhanced Docker Hub documentation with latest model information
+
 ### v1.2.0 (2025-05-06)
 
 This release introduces support for Qwen3 models, marking a significant step in our commitment to staying Tesla K80 with leading open-source language models. Testing includes successful execution of Gemma 3 12B, Phi-4 Reasoning 14B, and Qwen3 14B, ensuring compatibility with models expected to be widely used in May 2025.