---
language: en
license: apache-2.0
datasets:
- bexgboost/openai-agents-python-qa-firecrawl
tags:
- firecrawl
- gemma
- web-scraping
- question-answering
- fine-tuned
- unsloth
---

# Gemma-3-12B Firecrawl Expert

This is a fine-tuned version of Gemma-3-12B specialized in answering questions about Firecrawl web scraping.

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

## Training Data
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
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
)
```