Instructions to use zeronamoni/backtesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use zeronamoni/backtesting 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 zeronamoni/backtesting 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 zeronamoni/backtesting to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for zeronamoni/backtesting to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="zeronamoni/backtesting", max_seq_length=2048, )
File size: 444 Bytes
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language: ko
license: apache-2.0
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- continued-pretraining
- wikipedia
- unsloth
---
# backtesting - v2_baseline
## Evaluation Results
| Metric | Value | v4_pure_KR | v5_baseline |
| --- | --- | --- | --- |
| **Evaluation Loss** | 2.0091 |
| **Perplexity (PPL)** | 7.46 |
| **BPC** | 2.8985 |
## Experiment Note
- Version: v2_baseline
- Target: Korean Wikipedia Continued Pretraining |