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Ladder + TTFT LLM Finetuning

Project description

Ladder + TTFT

Custom Ladder implementation on any Complex problem for LLM. a reimplementation of the paper “LADDER: SELF-IMPROVING LLMS THROUGH RECURSIVE PROBLEM DECOMPOSITION” https://arxiv.org/pdf/2503.00735

workflow

Finetuned Qwen2-0.5B with Ladder (Model Response)

Ladder-Finetuned

setup

install from source using PDM

git clone git@github.com:AbdelrahmanAbounida/ladder.git
cd ladder
pdm install

Run

our main usecase (Graph problem)

python src/main.py

TODO

Dataset Generation

  • LLM Intelligence ratio Equation
  • Custom Verification Method if required (for our Graph Usecase)
  • DatasetGenerator > Generate subproblems according to the model intelligence ratio (step3)
  • Difficulty Engine should decide the level of difficulty to be generated and what transformations to be applied
  • Verification engine should use the small llm to be tuned not the Larger one
  • LLM Engine (temperature cycling and persona based prompts for different operations like variant generation)

Ladder

  • Ladder Finetuning Process
  • GRPO Implementation
  • reward functions

TTRL

  • TTRL Implementation
  • Data Generation in a loop

Others

  • General Configurations for all Constants and Hyper Parameters
  • implement different interfaced for different models to be used (HF, Ollama, VLLM, deepspeed, LiteLLM,..)
  • LLMS Benchmarking
  • Metrics and other evaluation methods
  • implement more usecases if required for diverse benchmarking
  • use accelerate / PEFT / deepspeed and vllm to speed up training process

Production

  • Documentation
  • packaging
  • CICD
  • Testing

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