---
title: Praveen · Open Model Builder
emoji: 🧪
colorFrom: orange
colorTo: yellow
license: apache-2.0
---

# 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](https://huggingface.co/praveenkumarpranjal/LFM2.5-2.6B-4bit-PathPack-Q)

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](https://huggingface.co/praveenkumarpranjal/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](https://huggingface.co/datasets/praveenkumarpranjal/scopeguard-decisions)

→ [Open the complete benchmark explorer](https://huggingface.co/spaces/praveenkumarpranjal/scopeguard-benchmark)

### [LoRA Lens](https://huggingface.co/spaces/praveenkumarpranjal/lora-lens-demo)

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](https://huggingface.co/praveenkumarpranjal/lora-lens)

## 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.