Instructions to use lm-spell/llama-3.1-8b-it-ft-ssc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lm-spell/llama-3.1-8b-it-ft-ssc with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "lm-spell/llama-3.1-8b-it-ft-ssc")

Transformers

How to use lm-spell/llama-3.1-8b-it-ft-ssc with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lm-spell/llama-3.1-8b-it-ft-ssc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lm-spell/llama-3.1-8b-it-ft-ssc", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lm-spell/llama-3.1-8b-it-ft-ssc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lm-spell/llama-3.1-8b-it-ft-ssc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lm-spell/llama-3.1-8b-it-ft-ssc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lm-spell/llama-3.1-8b-it-ft-ssc

SGLang

How to use lm-spell/llama-3.1-8b-it-ft-ssc with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lm-spell/llama-3.1-8b-it-ft-ssc" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lm-spell/llama-3.1-8b-it-ft-ssc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lm-spell/llama-3.1-8b-it-ft-ssc" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lm-spell/llama-3.1-8b-it-ft-ssc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use lm-spell/llama-3.1-8b-it-ft-ssc with Docker Model Runner:
```
docker model run hf.co/lm-spell/llama-3.1-8b-it-ft-ssc
```

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Llama 3.1 8B Instruct Fine-tuned for Sinhala Spell Correction

A Llama 3.1 8B Instruct model fine-tuned for Sinhala spell correction using LoRA as part of the LMSpell project.

Model Details

Base model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
Model type: LoRA adapter
Language: Sinhala (si)
Task: Sinhala Spell Correction (SSC)
Training method: Supervised Fine-Tuning (SFT) with LoRA
Dataset: Sinhala Spell Correction Dataset
License: CC BY 4.0
Project: LMSpell

Uses

Direct Use

The model can be used to correct spelling errors in Sinhala text.

Out-of-Scope Use

The model is not intended for languages other than Sinhala or for tasks unrelated to spell correction.

Limitations

Performance may vary across different domains and text styles.
The model may occasionally modify text that is already correctly spelled.
Performance may be lower on informal, code-mixed, or domain-specific text.

How to Get Started

### 1. Model Loading

#### Using Unsloth

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="lm-spell/llama-3.1-8b-it-ft-ssc"
)

tokenizer.pad_token = tokenizer.eos_token

FastLanguageModel.for_inference(model)

Using Transformers and PEFT

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

adapter_name = "lm-spell/llama-3.1-8b-it-ft-ssc"
base_model =  "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit"

tokenizer = AutoTokenizer.from_pretrained(adapter_name)

model = AutoModelForCausalLM.from_pretrained(
    base_model
)

model = PeftModel.from_pretrained(
    model,
    adapter_name
)

tokenizer.pad_token = tokenizer.eos_token

2. Inference

import re

def clean_text(text):
    match = re.search(r'### Output:\n(.*)', text, re.DOTALL)
    if match:
        return match.group(1).replace("Text:\n", "").replace("Output:\n", "").replace("###", "").strip()
    return text.strip()

alpaca_prompt = f"""You are an Expert Sinhala Spell Corrector. Below is a sentence in Sinhala Language. It may or may not have a spelling mistake. Give the corrected output in Sinhala.

### Text:
{{}}

### Output:
{{}}"""

text = "යින් හැඟෙන්නේ පවත්නා අධ්‍යාපන ක්‍රමයේ පවතින උසස මට්ටමය ."

inputs = tokenizer(
    [alpaca_prompt.format("", text)],
    return_tensors="pt"
) # .to("cuda") if using unsloth

outputs = model.generate(**inputs)

pred_text = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(clean_text(pred_text))

Training Details

Training Data

The model was fine-tuned using the Sinhala Spell Correction Dataset.

Training Procedure

The model was fine-tuned using Supervised Fine-Tuning (SFT) with LoRA using Unsloth.

Evaluation

The model was evaluated for Sinhala spell correction using the evaluation datasets and metrics described in the LMSpell paper.

For detailed training methodology, dataset preparation, evaluation results, and comparisons with other models, please refer to the paper.

Research

LMSpell: Spell Correction with Pre-Trained Language Models

A. Gunathilake, N. Karunarathna, T. Bandaranayake, S. Ranathunga, N. de Silva and N. Jayatilleke, "LMSpell: Spell Correction with Pre-Trained Language Models," 2026 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2026, pp. 503-508.

Paper: https://doi.org/10.1109/MERCon71835.2026.11691371
Extended Preprint: https://arxiv.org/html/2512.05414v3

Citation

@INPROCEEDINGS{11691371,
  author={Gunathilake, Akesh and Karunarathna, Nadil and Bandaranayake, Tharusha and Ranathunga, Surangika and de Silva, Nisansa and Jayatilleke, Nevidu},
  booktitle={2026 Moratuwa Engineering Research Conference (MERCon)},
  title={LMSpell: Spell Correction with Pre-Trained Language Models},
  year={2026},
  pages={503-508},
  doi={10.1109/MERCon71835.2026.11691371}
}

Framework Versions

PEFT: 0.17.1

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