metadata
license: apache-2.0
tags:
- unsloth
language:
- en
base_model:
- unsloth/Meta-Llama-3.1-8B
pipeline_tag: text-generation
datasets:
- Varadrajan/informal_formal_tweet
Uploaded adapter
- Developed by: Varadrajan
- License: apache-2.0
- Finetuned from model : unsloth/Meta-Llama-3.1-8B
This llama adapter was trained 2x faster with Unsloth and Huggingface's TRL library.
📖 Overview / Purpose
This adapter enables transforming informal / casual English sentences into formal / polished prose.
It’s ideal for tone standardization, content polishing, and elevating everyday speech into refined writing.
🚀 How to Use
You have two main options:
- On-the-fly adapter usage: Load base model + adapter during inference (keeps highest fidelity).
- Merged model inference: Merge the adapter into a model (16-bit or 4-bit) so inference needs only one model artifact (simpler deployment).
🧪 Merging Options & Tradeoffs
- 16-bit merge — preferred for better output quality while simplifying inference
- 4-bit merge (experimental) — smaller footprint but may degrade quality due to quantization / rounding errors
Use the method that fits your resource constraints and quality requirements.
📌 Intended Applications & Use Cases
- Customer support / chat interfaces, rewriting user text more professionally
- Content polishing tools, style editors
- Internal documentation, standardization of tone across teams
- Writers / non-native English users seeking more formal expression
⚠️ Limitations & Risks
- The adapter might over-formalize or introduce vocabulary changes
- In edge cases (slang, idioms, sarcasm), output may misinterpret meaning
- Merged low-bit models can degrade quality
- Always review output in sensitive or public contexts