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Kronu/
gemma-2-2b-lean-expert-optimized

Name: gemma-2-2b-lean-expert-optimized
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gemma-2-2b-lean-expert-optimized

Optimized Gemma Model for 94%+ Success Rate

This repository contains the training configuration for an optimized Gemma-2-2B model targeting 94%+ success rate on Lean trading algorithm optimization tasks.

Training Configuration

Base Model: google/gemma-2-2b
Dataset: Kronu/lean-expert-optimized-2000
Target Success Rate: 94%+
Expected Performance: 96% (94-98% range)

Key Optimizations

JSON Parsing Focus: 1,333 examples (0% → 95% success target)
Enhanced LoRA: rank=64, alpha=128
Optimized Training: 12 epochs, 2e-4 learning rate
Advanced Configuration: Gradient checkpointing, FP16

Training Instructions

To train this model using HuggingFace Jobs:

Set up your HuggingFace token as environment variable
Run the training script: python train.py
Monitor training progress in the HuggingFace dashboard

Expected Results

Training Time: 25-35 minutes
Cost: $3-5
Final Model: Kronu/gemma-2-2b-lean-expert-optimized
Success Rate: 96% (94-98% range)

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load the trained model
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b")
model = PeftModel.from_pretrained(base_model, "Kronu/gemma-2-2b-lean-expert-optimized")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")