MAMBA_7B: Codestral Mamba 7B Installation, Benchmarks & Architectural Research

This repository contains the complete codebase, benchmark suite, evaluation datasets, research documents, and visual PowerPoint presentations for MAMBA_7B.

Published to namanadep Hugging Face profile using NAMAN_HF_TOKEN.

---

📊 Summary Benchmark Metrics (Codestral Mamba 7B vs. Qwen 2.5 7B)

Metric	Codestral Mamba 7B	Qwen 2.5 7B Instruct	Takeaway
Architecture	Selective State Space Model (SSM S6)	Multi-Head Self-Attention Transformer	Mamba eliminates $O(N^2)$ quadratic KV-cache memory scaling.
Average Latency	4.28s	7.40s	42.2% faster completion for Codestral Mamba.
Generation Speed	194.8 t/s	193.3 t/s	Identical throughput on NVIDIA H200 GPUs.
Memory Footprint	Constant $O(1)$ Memory State	$O(N)$ Growth	Fixed VRAM up to 256k long-context reasoning.

---

📂 Repository Layout & Uploaded Artifacts

• docs/CODESTRAL_MAMBA_7B_VS_QWEN_7B_COMPARISON.md: 71 KB Exhaustive 10-Prompt Benchmark Report.
• docs/CODESTRAL_MAMBA_7B_VS_QWEN_7B_COMPARISON.pptx: 9-Slide Visual Benchmark Comparison Deck.
• docs/MAMBA_7B_INSTALLATION_AND_ARCHITECTURE_GUIDE.pptx: 8-Slide Hands-on Installation & Architecture Journey Deck.
• docs/MAMBA_MODELS_RESEARCH_OLLAMA_HUGGINGFACE.md: State Space Models Architectural Research Document.
• data/mamba_vs_qwen_results.json: Raw Evaluation JSON transcripts across 10 technical categories.
• src/: Complete Python benchmark test harness and slide generation scripts.