This repository contains LoRA adapter weights fine-tuned for English grammar correction.
The adapters are trained on top of the LiquidAI/LFM2.5-1.2B-Instruct base model using QLoRA.

The model is designed to:

Correct grammatical errors
Preserve the original meaning
Minimize unnecessary rewrites

This repository does not contain the base model weights, only the LoRA adapters.

⚠️ About Hugging Face Auto-Generated Code Snippets

Hugging Face may display examples such as:

pipeline("text-generation", model="arjunverma2004/LiquidAI-grammarly-lora")

AutoModel.from_pretrained("arjunverma2004/LiquidAI-grammarly-lora")

These examples are automatically generated by the Hub and will not work for this repository. Below I have provided the correct code

Developed by

Independent contributor

Funded by

Not applicable

Shared by

Community contribution

Model type

Causal Language Model (LoRA adapters)

Language(s)

English

License

Apache 2.0 (inherits base model license)

Finetuned from model

LiquidAI/LFM2.5-1.2B-Instruct

Model Sources

Repository: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct
Paper: Not available
Demo: Not available

Uses

Direct Use

English grammar correction
Proofreading short and medium-length texts
Educational and language-learning tools

Downstream Use

Writing assistants
Grammar checking pipelines
Preprocessing text for downstream NLP tasks

Out-of-Scope Use

Content generation beyond grammar correction
Legal, medical, or professional advice
Multilingual grammar correction

Bias, Risks, and Limitations

The model may reflect biases present in the training data.
It may over-correct stylistic choices in creative writing.
It is optimized for grammatical correctness, not factual accuracy.
Performance may degrade on very long or highly technical texts.

Recommendations

Users should:

Review corrections before final use
Avoid relying on the model for high-stakes or sensitive applications
Combine with human review for best results

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "LiquidAI/LFM2.5-1.2B-Instruct"
adapter_name = "arjunverma2004/LiquidAI-grammarly-lora"

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    trust_remote_code=True,
)

# Attach LoRA adapters
model = PeftModel.from_pretrained(
    base_model,
    adapter_name,
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    base_model_name,
    trust_remote_code=True,
)
tokenizer.pad_token = tokenizer.eos_token

python

from transformers import pipeline

# Use our predefined prompt template
sentence = """Write this sentence correctly: Here was no promise of morning except that we looked up through the trees we saw how low the forest had swung .
"""
dic1 = [{'role': "user", 'content': sentence}]

prompt = tokenizer.apply_chat_template(dic1, tokenize=False, add_generation_prompt=True)
# Run our instruction-tuned model
pipe = pipeline(task="text-generation", model=merged_model, tokenizer=tokenizer)
print(pipe(prompt)[0]["generated_text"])

Training Details

Training Data

JFLEG (JHU Fluency-Extended GUG Corpus)
Dataset focused on grammatical error correction with multiple human references

Training Procedure

Preprocessing

Inputs formatted using the base model’s chat template
Each example consists of an erroneous sentence and a corrected version

Training Hyperparameters

Training regime: Supervised Fine-Tuning (SFT)
Method: QLoRA
Precision: 4-bit (NF4)
Max sequence length: 512 tokens
Optimizer: AdamW (via TRL)
PEFT: LoRA

Speeds, Sizes, Times

Training performed on a single GPU
Lightweight adapter-only training

Evaluation

Testing Data

Held-out samples from JFLEG
Custom manually written grammatical error examples

Factors

Error type (tense, agreement, articles, prepositions)
Sentence length
Error density

Metrics

Training loss (cross-entropy)
Qualitative human evaluation
(Optional) GLEU score

Results

Rapid loss convergence
High-quality grammatical corrections
Minimal semantic drift

Summary

Model Examination

The model demonstrates strong grammatical correction capabilities while preserving sentence meaning.
It performs best on common ESL-style grammatical errors.

Environmental Impact

Hardware Type: NVIDIA GPU (single device)
Hours Used: < 5 hours
Cloud Provider: Google Colab
Compute Region: Not specified
Carbon Emitted: Not estimated

Technical Specifications

Model Architecture and Objective

Base architecture: Transformer-based causal language model
Objective: Next-token prediction for grammar-corrected text

Compute Infrastructure

Single-GPU training with quantization

Hardware

NVIDIA GPU (Google Colab environment)

Software

Python
PyTorch
Hugging Face Transformers
TRL
PEFT
bitsandbytes

Citation

BibTeX

bibtex

@misc{liquidai_grammarly_lora,
  title={LiquidAI Grammarly LoRA},
  author={Anonymous},
  year={2026},
  url={https://huggingface.co/USERNAME/LiquidAI-grammarly-lora}
}

APA

LiquidAI Grammarly LoRA. (2026). Hugging Face. https://huggingface.co/USERNAME/LiquidAI-grammarly-lora

Glossary

LoRA: Low-Rank Adaptation

QLoRA: Quantized LoRA

SFT: Supervised Fine-Tuning

JFLEG: Grammar correction dataset