| # TD Merge Pipeline - Complete Python Dependency List |
| # Python 3.11-3.12 (3.12 preferred) |
| # CUDA 12.4 (RTX 4090 compatible) |
| # Updated: February 2026 |
|
|
| # ============================================================================ |
| # CORE ML FRAMEWORKS |
| # ============================================================================ |
|
|
| # PyTorch 2.4+ with CUDA 12.4 support (RTX 4090 compatible) |
| torch==2.4.1 |
| torchvision==0.19.1 |
| torchaudio==2.4.1 |
|
|
| # NVIDIA CUDA Toolkit support (already installed on system) |
| # CUDA 12.4 for RTX 4090 compatibility |
| # Note: Install via: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 |
|
|
| # ============================================================================ |
| # TRANSFORMERS & MODEL LOADING |
| # ============================================================================ |
|
|
| # Transformers library - must support Qwen3 (requires 4.51.0+) |
| transformers==4.51.0 |
|
|
| # Safetensors for efficient model serialization |
| safetensors==0.4.5 |
|
|
| # Accelerate for distributed training & multi-GPU support |
| accelerate==1.2.1 |
|
|
| # ============================================================================ |
| # PARAMETER EFFICIENT FINE-TUNING (PEFT/QLoRA) |
| # ============================================================================ |
|
|
| # PEFT (Parameter-Efficient Fine-Tuning) - supports QLoRA |
| # Must be >= 0.14.0 for 8-bit weight merging |
| peft==0.14.0 |
|
|
| # BitsAndBytes for 4-bit quantization (QLoRA) |
| # Works with PyTorch 2.4, stable with >= 0.42 |
| bitsandbytes==0.44.0 |
|
|
| # ============================================================================ |
| # OPTIMAL TRANSPORT & MODEL MERGING |
| # ============================================================================ |
|
|
| # POT (Python Optimal Transport) - for Transport and Merge algorithm |
| # Used for activation-aligned cross-architecture weight alignment |
| POT==0.9.6 |
|
|
| # SciPy for optimization & linear algebra (OrthoMerge, LARV) |
| scipy==1.14.1 |
|
|
| # NumPy for numerical operations |
| numpy==1.26.4 |
|
|
| # Lark parser for td_lang DSL |
| lark>=1.1.0 |
|
|
| # Unsloth for fast fine-tuning with 7B models |
| # Includes pre-quantized Qwen3-8B support, VLLM Standby Mode for concurrent training+inference |
| unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@main |
|
|
| # ============================================================================ |
| # REINFORCEMENT LEARNING (RL TRAINING) |
| # ============================================================================ |
|
|
| # TRL (Transformers Reinforcement Learning) |
| # Provides GRPO (Group Relative Policy Optimization) trainer |
| # v0.27.2 stable, tested with transformers 4.40+ |
| trl==0.27.2 |
|
|
| # ============================================================================ |
| # EVALUATION & BENCHMARKING |
| # ============================================================================ |
|
|
| # LM-Eval (EleutherAI evaluation harness) for benchmarking |
| # Explicitly install HF backend for transformers support |
| lm-eval[hf]==0.4.10 |
|
|
| # MathEval utilities |
| math-eval==0.0.3 |
|
|
| # ============================================================================ |
| # DATA HANDLING & DATASETS |
| # ============================================================================ |
|
|
| # HuggingFace Datasets library (HF Hub integration) |
| datasets==4.5.1 |
|
|
| # PyArrow for efficient data processing |
| pyarrow==17.0.0 |
|
|
| # Pandas for data manipulation |
| pandas==2.2.3 |
|
|
| # ============================================================================ |
| # OPTIONAL: MERGING & FUSION (if not building Transport & Merge from scratch) |
| # ============================================================================ |
|
|
| # MergeKit - alternative model merging tool (supports TIES/DARE-TIES) |
| # Note: Limited to same-architecture merges, but useful for fallback strategy |
| mergekit==0.0.7 |
|
|
| # ============================================================================ |
| # WEB & KNOWLEDGE RETRIEVAL (for ALAS - Autonomous Learning Agent System) |
| # ============================================================================ |
|
|
| # Requests for HTTP operations |
| requests==2.31.0 |
|
|
| # Beautiful Soup for web scraping |
| beautifulsoup4==4.12.3 |
|
|
| # ============================================================================ |
| # AGENT ORCHESTRATION & UTILITIES |
| # ============================================================================ |
|
|
| # LangGraph for multi-agent coordination (SYMPHONY) |
| langgraph==0.2.7 |
|
|
| # LangChain for prompt management & chains |
| langchain==0.3.9 |
|
|
| # Pydantic for data validation |
| pydantic==2.8.2 |
|
|
| # ============================================================================ |
| # VISION AGENT (Fara-7B integration) |
| # ============================================================================ |
|
|
| # Pillow for image processing |
| Pillow==11.2.0 |
|
|
| # OpenCV for computer vision tasks |
| opencv-python==4.10.1.26 |
|
|
| # ============================================================================ |
| # INFERENCE & SERVING |
| # ============================================================================ |
|
|
| # vLLM for fast LLM inference serving |
| vllm==0.6.4 |
|
|
| # ============================================================================ |
| # UTILITIES & LOGGING |
| # ============================================================================ |
|
|
| # PyYAML for config files |
| PyYAML==6.0.2 |
|
|
| # Python-dotenv for environment variable management |
| python-dotenv==1.0.1 |
|
|
| # Tqdm for progress bars |
| tqdm==4.67.1 |
|
|
| # Rich for beautiful terminal output |
| rich==13.8.1 |
|
|
| # ============================================================================ |
| # DEVELOPMENT & TESTING (OPTIONAL) |
| # ============================================================================ |
|
|
| # Pytest for testing |
| pytest==8.3.2 |
|
|
| # IPython for interactive development |
| ipython==8.20.0 |
|
|
| # Jupyter for notebooks |
| jupyter==1.0.0 |
|
|
| # ============================================================================ |
| # VERSION NOTES & COMPATIBILITY MATRIX |
| # ============================================================================ |
| # |
| # COMPATIBILITY VERIFIED: |
| # ✓ PyTorch 2.4.1 + CUDA 12.4 + RTX 4090 (full support) |
| # ✓ Transformers 4.51.0 + Qwen3-8B (latest, required for Qwen3) |
| # ✓ Unsloth 2026.2.x + Qwen3 + QLoRA (fast fine-tuning) |
| # ✓ BitsAndBytes 0.44.0 + PyTorch 2.4 (4-bit quantization) |
| # ✓ PEFT 0.14.0 + BitsAndBytes (8-bit weight merging) |
| # ✓ TRL 0.27.2 + GRPO (RL training with group advantage) |
| # ✓ POT 0.9.6 + SciPy 1.14.1 (optimal transport) |
| # ✓ LM-Eval 0.4.10[hf] + Transformers 4.51.0 (benchmarking) |
| # |
| # KNOWN ISSUES & WORKAROUNDS: |
| # - Flash-Attention-2: Works with Qwen3 but may produce incorrect outputs |
| # → Use attn_implementation="sdpa" (default) instead |
| # → DO NOT set attn_implementation="flash_attention_2" |
| # |
| # - BitsAndBytes + XFormers: Avoid mixing with older PyTorch versions |
| # → Use Unsloth bundled installer which pre-handles this |
| # |
| # - Thinking Mode Survival: Qwen3's thinking tokens (151668) may be scrambled |
| # → Freeze thinking token embeddings during Transport & Merge |
| # → Apply Contrastive Gradient Identification (ReasonAny) to protect reasoning params |
| # → Post-merge fine-tune on 500-1000 thinking examples |
| # |
| # CUDA 12.4 NOTES: |
| # - RTX 4090 full support (Ada architecture, compute capability 8.9) |
| # - All libraries compiled for CUDA 12.4 compatibility |
| # - No need to install system CUDA separately if PyTorch wheels handle it |
| # |
| # HARDWARE CHECKLIST: |
| # ✓ Dual RTX 4090 (48GB VRAM total) - adequate for full pipeline |
| # ✓ 64GB+ system RAM (128GB comfortable) |
| # ✓ 1500W+ PSU (handles 1.2kW sustained load) |
| # ✓ Gen4+ NVMe SSD (3000+ MB/s write, 2TB minimum) |
| # |
| # INSTALLATION: |
| # 1. Create venv: python3.12 -m venv venv && source venv/bin/activate |
| # 2. Install PyTorch with CUDA 12.4: |
| # pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 |
| # 3. Install this requirements file: |
| # pip install -r requirements.txt |
| # 4. Optional - install Unsloth's bundled version (handles all conflicts): |
| # pip install unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@main |
| # |
| # ESTIMATED INSTALLATION TIME: |
| # - PyTorch (download): 5-10 min |
| # - Other packages: 2-5 min |
| # - Total: 10-15 minutes |
| # |
|
|
