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arjunverma2004
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LiquidAI-grammarly-lora

Text Generation
PEFT
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lora
qlora
grammar-correction
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license: apache-2.0 language: - en datasets: - jhu-clsp/jfleg

Model Card for LiquidAI Grammarly (LoRA)

Model Details

Model Description

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

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

or

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

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 

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

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 

@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

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Model tree for arjunverma2004/LiquidAI-grammarly-lora

Base model

LiquidAI/LFM2.5-1.2B-Base
Finetuned
LiquidAI/LFM2.5-1.2B-Instruct
Adapter
(12)
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