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Updated AI implementation (Using Mistral 7B (AWQ‐4‐bit)
🚀 Refactoring Our Implementation Strategy: Best Approach for Your SaaS AI Model
After encountering multiple issues with model downloads, storage limits, tokenizer mismatches, and quantization errors, we need a clear, optimized roadmap to:
1️⃣ Select the best AI model for your SaaS
2️⃣ Download it efficiently without running into storage issues
3️⃣ Train it on a scalable platform
4️⃣ Deploy it optimally for inference
Based on our previous discussions, your AI should:
✅ Listen to audio (Whisper) and transcribe it
✅ Generate real-time insights & solutions based on discussions
✅ Summarize meetings with key actions & recommendations
✅ Run efficiently on available GPUs (Kaggle, Colab, or Cloud GPUs)
✅ Be scalable for future SaaS deployment
To meet these needs, we need a model that is:
- Optimized for NLP (meeting summarization, action-item extraction, etc.)
- Supports real-time inference
- Lightweight enough to run on available hardware (not requiring 642GB like DeepSeek-R1)
- Easy to fine-tune and deploy for SaaS
| Model | Size | Strengths | Weaknesses |
|---|---|---|---|
| Mistral-7B (AWQ / 4-bit) | ~7B params (Fits in Colab) | ✅ Optimized for summarization & chat | ❌ Needs fine-tuning for meeting-specific use case |
| LLaMA-2 7B AWQ | ~7B params | ✅ Highly optimized, runs efficiently | ❌ May need custom RAG system for better retrieval |
| Gemma-7B (Google) | ~7B params | ✅ Google-optimized, supports summarization | ❌ Requires Google Cloud TPU for best performance |
| DeepSeek-R1 Distill (LLaMA 70B AWQ) | ~70B params | ✅ High performance, multilingual | ❌ Hard to load, tokenizer errors |
✅ Download & load Mistral-7B AWQ (Step 2)
✅ Test the model’s responses
✅ Prepare a dataset for fine-tuning
✅ Train on RunPod.io or Paperspace
✅ Deploy using vLLM for fast SaaS inference