Timothyemmanuel

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Arakandar

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Hi Timothy,

Decoupling deterministic decision execution (LightGBM signal generation) from linguistic interpretation (3.1B LLM explanation) is an exceptionally disciplined design choice. In financial and market-research workflows, having the gradient-boosted model act as the immutable source of truth for BUY/HOLD/SELL while restricting the causal LM strictly to evidence explanation completely eliminates autonomous trading hallucinations.

Looking at your base topology (36 layers, GQA with 16 Q heads / 2 KV heads, 32k context) and the 10-12 GB deployment target:

1. Numerical coordinate preservation across 32k spans:
   Financial inputs consist of high-entropy, low-frequency tokens: exact decimal values (e.g. RSI 43.7, MACD diff -39.47), ticker formats, and trade boundaries.
   At extended sequence lengths (e.g. ingesting multi-page balance sheets or earning call transcripts up to 32k), standard rotary embeddings (RoPE) can introduce high-frequency coordinate noise as context stretches. This phase rotation can cause attention heads to lose sharp coordinate grounding, leading to digit transpositions or misattributing numeric metrics between competing tickers.

2. VRAM headroom on 10-12 GB consumer GPUs:
   Your base weights in BF16 consume 6.2 GB.
   With GQA compressed to 2 KV heads (head dimension 128 across 36 layers), the FP16 KV-cache requires ~1.2 GB per sequence at the full 32k horizon.
   Factoring in the PyTorch/CUDA runtime overhead (0.8-1.0 GB), LoRA adapter buffers, and intermediate activations during autoregressive rollout, a 32k context generation pushes total allocation past 9.5-10.5 GB. On a standard 12 GB card (RTX 3060/4070), this leaves very thin margin for batching or multi-turn conversational history before hitting CUDA out-of-memory errors.

3. Numerical grounding via NoPE and constant O(1) state memory:
   In an open architecture project called Maba v2 (reference release and scaling specifications: https://huggingface.co/AndrewThompson1233/maba-v2-architecture), we explore 1B-3B efficiency using a 3:1 macro-topology (DGDA linear recurrence paired with sparse latent attention):

• To prevent numerical drift, we run native NoPE: sequence order is tracked through recurrent state transitions rather than rotary phase shifts, preserving exact numeric token representations across long horizons.
• By updating 75% of depth via recurrent associative fast-weights, generation memory remains strictly bounded in O(1) space, keeping the 3B state footprint under 350 MB even at 131k context.

If you are looking to harden long-context earnings transcript retrieval or lower VRAM pressure for local deployments, the architectural trade-offs in the Maba v2 repo might provide some useful structural points for future iterations.

In your LightGBM-to-LLM pipeline, did you use explicit structured serialization (like JSON-LD or Markdown tables) during LoRA SFT to enforce signal adherence, or does the model ingest raw formatted strings directly?

Best,
Andrew