Instructions to use ivanasp/Llama-3.2-1B-JSON-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivanasp/Llama-3.2-1B-JSON-Extractor with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ivanasp/Llama-3.2-1B-JSON-Extractor", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use ivanasp/Llama-3.2-1B-JSON-Extractor with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ivanasp/Llama-3.2-1B-JSON-Extractor to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ivanasp/Llama-3.2-1B-JSON-Extractor to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ivanasp/Llama-3.2-1B-JSON-Extractor to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ivanasp/Llama-3.2-1B-JSON-Extractor", max_seq_length=2048, )
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license: apache-2.0
base_model: unsloth/Llama-3.2-1B-instruct-bnb-4bit
tags:
- text-to-json
- unsloth
- llama-3
- fine-tuned
- extraction
language:
- en
- es
colorFrom: blue
colorTo: indigo
library_name: transformers
---
# 🚀 Llama-3.2-1B-JSON-Extractor

This model is a professional fine-tuned version of **Llama 3.2 1B Instruct**, specialized in converting **Natural Language into structured JSON objects**.
## 🎯 Project Goal
The system interprets human intent and extracts key entities into a machine-readable format. It is designed to be the bridge between human communication and database systems.
### Extracted Fields:
- `product`: Name or description of the item.
- `price`: Numerical value (currency independent).
- `category`: Market segment or classification.
## 🛠️ Technical Specifications
- **Architecture**: Llama 3.2 1B
- **Optimization**: QLoRA (4-bit)
- **Rank (r)**: 16
- **Alpha**: 32
- **Learning Rate**: 2e-4
- **Final Training Loss**: ~0.013 (High precision)
## 💡 Usage Example
**Input:**
> "I want to sell a Sony PlayStation 5 for 500 dollars in the gaming category."
**Output:**
```json
{
"product": "Sony PlayStation 5",
"price": 500,
"category": "gaming"
}
```
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
Developed by ivanasp using Unsloth. |