The model was fine-tuned on the bexgboost/openai-agents-python-qa-firecrawl dataset, which contains question-answer pairs about Firecrawl and web scraping techniques.

Use Cases

Base Model: unsloth/gemma-3-12b-it
Fine-tuning Method: LoRA (Low-Rank Adaptation) using Unsloth
Training Library: Unsloth
Fine-tuned: April 24, 2025

This model is specialized in:

Answering questions about Firecrawl web scraping
Providing guidance on web scraping techniques
Helping with Firecrawl implementation

Training Parameters

LoRA Rank: 8
LoRA Alpha: 8
Learning Rate: 2e-4
Epochs: 1
Quantization: 4-bit

Usage with Unsloth

python

from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name = "Laksh99/Gemma_finetuned_april_24_2025",
    max_seq_length = 2048,
    load_in_4bit = True
)