README

Praveen

I build practical, inspectable tools around open models: adapters, quantization workflows, evaluation surfaces, and small demos that make model behavior easier to understand.

Featured work

PathPack-Q · LFM2.5-2.6B training-free quantization

An architecture-specific post-training quantization experiment for LiquidAI's hybrid convolution/attention LLM. PathPack-Q uses exact gated-path channel permutations to improve which weights share each 4-bit quantization group, without training, text calibration data, extra parameters, or runtime operators.

• 5.30% lower perplexity than byte-matched uniform MLX 4-bit on 8,160 held-out WikiText-2 tokens
• 9.87% lower KL divergence from the BF16 teacher on a fixed prompt suite
• Identical 1,517,616,892-byte checkpoint and 4.501 effective bits/weight
• Includes the search algorithm, complete-path acceptance gate, rejected-layer evidence, checkpoint builder, and machine-readable evaluations

ScopeGuard · Qwen2.5-1.5B LoRA

A locally trained agent decision-layer adapter that turns natural-language requests into strict JSON risk and confirmation decisions before tools execute.

• 93% risk accuracy on a 100-example held-out split, up from 66% for the base model
• 100% exact schema compliance, up from 84%
• 3.957M trainable parameters — only 0.256% of the 1.5B base model
• Includes adapter weights, original dataset, deterministic generator, training config, baseline outputs, and per-example evaluation

→ Explore the ScopeGuard dataset

→ Open the complete benchmark explorer

LoRA Lens

An in-browser audit tool for adapter_config.json files. It surfaces rank, alpha, scaling, target modules, reproducibility gaps, and conservative parameter-efficiency estimates without uploading weights or requiring an API key.

→ Inspect the implementation

Current lab

• LoRA and PEFT adapter design
• Quantization and memory-aware inference
• Reproducible model cards and evaluation tooling
• Human-readable demos for technical work

I prefer falsifiable, transparent experiments with clear limits over opaque claims.