Instructions to use rudrashah/RLM-spell-checker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rudrashah/RLM-spell-checker with Transformers:

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

pipe = pipeline("text-generation", model="rudrashah/RLM-spell-checker")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-spell-checker")
model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-spell-checker")

Keras

How to use rudrashah/RLM-spell-checker with Keras:

# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras

model = keras.saving.load_model("hf://rudrashah/RLM-spell-checker")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rudrashah/RLM-spell-checker with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rudrashah/RLM-spell-checker"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudrashah/RLM-spell-checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rudrashah/RLM-spell-checker

SGLang

How to use rudrashah/RLM-spell-checker 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 "rudrashah/RLM-spell-checker" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudrashah/RLM-spell-checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rudrashah/RLM-spell-checker" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rudrashah/RLM-spell-checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rudrashah/RLM-spell-checker with Docker Model Runner:
```
docker model run hf.co/rudrashah/RLM-spell-checker
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Description

The RLM-spell-checker is a fine-tuned version of gemma-2b-V3, enhanced using LoRA (Low-Rank Adaptation) to specialize in spelling correction. LoRA fine-tunes models efficiently by adjusting only a few parameters, allowing the RLM-spell-checker to retain the robust language understanding of gemma-2b-V3 while focusing on identifying and correcting spelling errors. This fine-tuning enables the model to provide context-aware suggestions for corrections, making it a powerful tool for real-time applications like automated writing assistance, chatbots, and word processors. By improving spelling accuracy without interrupting the natural flow of text, the RLM-spell-checker enhances text quality and user experience in various tasks.

Author: Rudra Shah

Running Model

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-spell-checker")
model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-spell-checker")

sent = "Whaat iss the mision?"
template = "Sentence:\n{org}\n\nCorrect_Grammar:\n{new}"
input_text = template.format(org=sent, new="")

input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))

Downloads last month: 9

Safetensors

Model size

3B params

Tensor type

F32

Model tree for rudrashah/RLM-spell-checker

Base model

google/gemma-2-2b

Finetuned

(544)

this model

Quantizations

2 models