SYSTEM_WEIGHT_ANALYSIS.md

Aglimate System Weight Analysis & CPU Optimization Guide

Current System Weight

Model Sizes (Approximate)

1. Qwen1.5-1.8B (~1.8B parameters) ✅ OPTIMIZED

   • Size: ~3.6-7 GB (FP32) / ~3.6 GB (FP16) / ~1.8 GB (INT8 quantized)
   • RAM Usage: 4-8 GB at runtime
   • Status: ✅ CPU-OPTIMIZED - Much lighter than 4B model

2. NLLB Translation Model (drrobot9/nllb-ig-yo-ha-finetuned)

   • Size: 600M-1.3B parameters (2-5 GB)
   • RAM Usage: 4-10 GB
   • Status: ⚠️ Heavy but manageable

3. SentenceTransformer Embedding (paraphrase-multilingual-MiniLM-L12-v2)

   • Size: ~420 MB
   • RAM Usage: ~1-2 GB
   • Status: ✅ Acceptable

4. FastText Language ID

   • Size: ~130 MB
   • RAM Usage: ~200 MB
   • Status: ✅ Lightweight

5. Intent Classifier (joblib)

   • Size: ~10-50 MB
   • RAM Usage: ~100 MB
   • Status: ✅ Lightweight

Total Estimated Weight

• Disk Space: ~10-15 GB (models + dependencies) ✅ REDUCED
• RAM at Startup: ~500 MB (lazy loading) / ~4-8 GB (when loaded)
• CPU Load: Moderate (1.8B model much faster on CPU than 4B)

Dependencies Weight

• torch (full): ~1.5 GB
• transformers: ~500 MB
• sentence-transformers: ~200 MB
• Other deps: ~500 MB
• Total: ~2.7 GB

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Why this matters for Aglimate

Keeping the Aglimate backend lean is essential so that smallholder farmers can access climate-resilient advice on affordable CPU-only infrastructure, without requiring expensive GPUs or large-cloud deployments.

Critical Issues for CPU Deployment

1. Eager Model Loading ✅ FIXED

All models load at import time in crew_pipeline.py:

• ✅ FIXED: Models now load lazily on-demand
• ✅ Qwen 1.8B loads only when /ask endpoint is called
• ✅ Translation model loads only when needed
• ✅ Startup time reduced to <5 seconds
• ✅ Initial RAM usage ~500 MB

2. Wrong PyTorch Version

• Using torch instead of torch-cpu (saves ~500 MB)
• torch.float16 on CPU is inefficient (should use float32 or quantized)

3. No Quantization

• Models run in FP32/FP16 (full precision)
• INT8 quantization could reduce size by 4x and speed by 2-3x

4. No Lazy Loading

• Models should load on-demand, not at startup
• Only load when endpoint is called

5. Device Map Issues

• device_map="auto" may try GPU even on CPU
• Should explicitly set CPU device

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Optimization Recommendations

Priority 1: Lazy Loading (CRITICAL)

Move model loading from import time to function calls.

Priority 2: Use CPU-Optimized PyTorch

Replace torch with torch-cpu in requirements.

Priority 3: Model Quantization

Use INT8 quantized models for CPU inference.

Priority 4: Smaller Models ✅ COMPLETED

✅ DONE: Switched to Qwen 1.5-1.8B (much lighter for CPU)

• ✅ Replaced Qwen 4B with Qwen 1.8B
• ✅ Reduced model size by ~55% (from 4B to 1.8B parameters)
• ✅ Reduced RAM usage by ~75% (from 16-32GB to 4-8GB)

Priority 5: Optimize Dockerfile

Remove model pre-downloading (let HuggingFace Spaces handle it).

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Best Practices for Hugging Face CPU Spaces

1. Memory Limits: HF Spaces CPU has ~16-32 GB RAM
2. Startup Time: Keep under 60 seconds
3. Cold Start: Models should load lazily
4. Disk Space: Limited to ~50 GB
5. Concurrency: Single worker recommended for CPU