Basic Project Blueprint: Efficient Agent - Ready LLM with Bamba + Ternary Quantization + 1 - shot RLVR + H - BitLinear ( A “ Bit Bamba ” Model) Overview This project aims to develop an ultra - efficient, agentic - capable open - source LLM stack that can run on consumer - grade hardware. It combines: • Bamba architecture (Transformer + interleaved SSM from IBM) • Ternary quantization from pretraining (1.58 - bit weights as per Spectra/Britney) • 4 - bit activation quantization using H - BitLinear (BitNet v2) • 1 - shot Reinforcement Learning with Verifiable Reward (RLVR) for reasoning bootstrapping Architecture Stack Model Backbone: Quantized Bamba - 21B • Transformer blocks interleaved with diagonal SSM layers • Gating mechanisms in SSMs • Long - context capability (trained on 4k, scales to 128K+) Quantization: • Ternary weights (1.58 - bit) from pretraining • 4 - bit activations with online Hadamard transform (H - BitLinear) • Full quantization baked in from the start — no post - compression Training Regimen: • Pretrain on 36T+ tokens • Loss: Causal LM + quant - aware regularizers • Optimizer: ternary - aware LAMB or Adam Fine - Tuning with 1 - shot RLVR Target Stage: Post - pretraining, fully quantized model Inputs: • One or two high - leverage math prompts (e.g. π1, π13) • Verifiable binary reward Loss Terms: • Policy Gradient Loss (REINFORCE/GRPO) • Entropy Loss ( α = - 0.001 to - 0.003) to enable post - saturation generalization Setup: • Batch: 128 • Rollouts: 8 per prompt • RL Steps: 2k • Eval: MATH500, AIME, ARC, Minerva Projected Reasoning Accuracy Improvement Deployment and Inference Target Runtime: • 6 - 12 GB GDDR6X VRAM + 32 - 64GB DDR4 system RAM • 32 K - 128K context, ~ 50 - 15 tok/s throughput (inference) Framework: vLLM • Ternary + 4b kernels (custom if needed) • Activation paging to RAM if >32K context • Efficient SSM state management Projected Throughput Improvements vs. Context Length Agentic Capabilities • Retain full multi - turn context (>100K tokens) • Support scratchpad and tool - trace reasoning • Enable local planner + reflection loop without server Evaluation Benchmarks • Reasoning: MATH500, ARC, Minerva, GSM8K • Long - seq: LRA, Books3, contract QA • Real - world agents: Reflexion, AutoGPT, CAMEL Future Extensions • Hierarchical summarization for >1M context • Routing - aware SSM MoEs • Local memory bank persistence (LLM - as - OS) • Ternary + LoRA for domain - specific agents Projected Inference Through put Improvements Deployment Considerations • Entire 21B model < 6GB in VRAM (weights + activations) • Remainder fits in RAM w/ paging • Consumer - grade GPU - ready, no server farms required Project Model Weight Size Differences