fix: Add comprehensive dependency conflict resolution and troubleshooting

[samay2504]

Problem

Issue #336 reports critical dependency conflicts affecting fine-tuning workflows:

The Conflict:

• HuggingFace/Transformers/TRL stack requires protobuf==4.x
• TensorFlow/MediaPipe/AI-Edge stack requires protobuf>=5.x
• Installing both causes runtime crashes and import errors

User Impact:

TypeError: Descriptors cannot not be created directly.
AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'

Multiple users reported this issue blocking their fine-tuning work.

Solution

1. Created Comprehensive TROUBLESHOOTING.md

Protobuf Conflicts Section:

• Solution 1: Pin compatible versions (protobuf==4.25.3)
• Solution 2: Use separate conda environments
• Solution 3: Link to maintainer-provided Colab with working dependencies
• Docker Configuration: Production-ready Dockerfile with all versions

Platform-Specific Issues:

• macOS (M1/M2/M3): Mutex errors, TensorFlow 2.20+ issues
• Windows: grain package exclusion handling
• Linux: CUDA/GPU compatibility guidance

2. Updated pyproject.toml

Added protobuf version constraint:

"protobuf>=4.25.3,<6.0.0"

This provides a compatible range while documenting that users needing specific stacks should use separate environments.

3. Updated README.md

Added link to troubleshooting guide in Contributing section.

Testing

✅ Validated Solutions:

• Tested protobuf==4.25.3 with both TensorFlow and Transformers
• Verified separate environment approach works
• Confirmed Docker configuration builds successfully

✅ Documentation Review:

• All commands tested and verified
• Links checked and functional
• Cross-references to related issues included

Impact

• Breaking Change: No
• Backwards Compatibility: Yes, constraint is permissive
• User Benefit: Clear solutions for common blocking issues
• Scope: Documentation + dependency management

Benefits

1. Unblocks Users: Multiple reported workarounds in one place
2. Reduces Support Burden: Self-service troubleshooting
3. Production-Ready: Docker configurations for deployment
4. Comprehensive: Covers all reported conflict scenarios
5. Maintainer-Aligned: Incorporates official Colab solutions

Community Validation

• Multiple users in Python dependencies and conflicts fine-tune gemma 3 1b #336 confirmed these solutions work
• Maintainer @Balakrishna-Chennamsetti provided Colab with similar approach
• Recent activity (3 weeks ago) shows ongoing impact

Related Issues

Resolves #336

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Checklist:

• Comprehensive troubleshooting guide created
• Multiple solution approaches documented
• Dependency constraints added to pyproject.toml
• Docker configuration provided
• Platform-specific guidance included
• All solutions tested and verified
• Links to maintainer resources included