App action	Training tag	Purpose
`grammar`	`TASK_GRAMMAR`	Fix grammar, spelling, punctuation, and agreement errors with minimal edits while preserving meaning and style.
`simplify`	`TASK_SIMPLIFY`	Make the text simpler and easier to understand while preserving meaning.
`clarity`	`TASK_CLARITY`	Make the text clearer and easier to understand while preserving meaning.
`coherence`	`TASK_COHERENCE`	Improve flow, structure, and coherence while preserving meaning.

There is no separate TASK_PHRASING tag. If you want a general phrasing rewrite, use:

TASK_CLARITY for sentence-level rewording
TASK_COHERENCE for flow and structure improvements

This repository contains the adapter weights and tokenizer assets. It is not a standalone base model.

Intended Use

This adapter is intended for:

local grammar correction
plain-English simplification
clarity-focused rewrites
coherence and flow edits
controlled rewriting in offline or local-first writing tools

It is not intended as a general-purpose open-ended chat assistant.

Prompt Format

The adapter expects the task to be stated explicitly in the user message.

During fine-tuning, each example used:

system prompt: You are a precise local writing engine. Follow the requested task exactly. Preserve the user's meaning.
user prompt format:

text

LANG_EN | TASK_...
Instruction: ...
Input: ...

Example for grammar correction:

text

LANG_EN | TASK_GRAMMAR
Instruction: Fix grammar, spelling, punctuation, and agreement errors with minimal edits. Preserve meaning and style.
Input: She go to school yesterday.

Quick Start

Load this adapter on top of the base model with PEFT.

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit"
adapter_id = "johan237/localgrammar-qwen25-3b-lora-v1"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    trust_remote_code=True,
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

messages = [
    {
        "role": "system",
        "content": (
            "You are a precise local writing engine. "
            "Follow the requested task exactly. Preserve the user's meaning."
        ),
    },
    {
        "role": "user",
        "content": (
            "LANG_EN | TASK_GRAMMAR\n"
            "Instruction: Fix grammar, spelling, punctuation, and agreement "
            "errors with minimal edits. Preserve meaning and style.\n"
            "Input: She go to school yesterday."
        ),
    },
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=64,
        do_sample=False,
        temperature=0.0,
    )

generated_ids = output_ids[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(generated_ids, skip_special_tokens=True))

Expected output:

She went to school yesterday.

How It Was Fine-Tuned

This adapter was fine-tuned with supervised fine-tuning on a chat-formatted English rewrite dataset assembled for local writing assistance.

Training data construction

The training set was built from:

grammarly/coedit
jhu-clsp/jfleg
facebook/asset
chaojiang06/wiki_auto

The dataset compiler:

mapped source tasks into the four task tags above
removed duplicate source/target pairs within each task
stripped instruction-like prefixes from some source examples
filtered obviously broken rows
filtered very short examples
filtered examples with large source/target length mismatches

Training set summary

total rows: 42,247
train rows: 42,035
validation rows: 2,212

Task distribution:

TASK_GRAMMAR: 20,000
TASK_SIMPLIFY: 15,000
TASK_COHERENCE: 8,000
TASK_CLARITY: 1,247

Dataset usage:

CoEdIT provided the main multi-task supervision
JFLEG augmented grammar correction
ASSET and WikiAuto augmented simplification

LoRA configuration

From adapter_config.json:

LoRA rank r=16
lora_alpha=32
lora_dropout=0
target modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj

Recorded training state

From the saved trainer state:

supervised fine-tuning
1 epoch
1855 total training steps
evaluation every 250 steps
save every 250 steps
recorded final validation loss: 0.4007
recorded train batch size: 2

Framework Versions

The exported training metadata reported:

PEFT 0.19.1
TRL 0.24.0
Transformers 5.5.0
PyTorch 2.5.1+cu121
Datasets 4.3.0
Tokenizers 0.22.2

Limitations

English only
optimized for short-to-medium rewrite requests rather than open-ended chat
output quality depends strongly on using the correct task tag
some training examples come from public rewrite datasets and may retain occasional dataset artifacts
TASK_CLARITY has much less training data than grammar or simplification

License

The base model Qwen/Qwen2.5-3B-Instruct is published on Hugging Face under the qwen-research license. This adapter should be used and redistributed in compliance with the upstream base-model license and the terms of the source datasets.

Sources

Base model family: Qwen/Qwen2.5-3B-Instruct
Quantized training base: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
Datasets:
- grammarly/coedit
- jhu-clsp/jfleg
- facebook/asset
- chaojiang06/wiki_auto

Citation

If you use this adapter, cite the base model, the source datasets, and your own project/repository in addition to standard library citations.

TRL citation:

bibtex

@misc{vonwerra2022trl,
  title        = {TRL: Transformer Reinforcement Learning},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = {2020},
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}