Instructions to use sujithrex/yutha_bible_tamil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sujithrex/yutha_bible_tamil with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-8b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "sujithrex/yutha_bible_tamil")

Transformers

How to use sujithrex/yutha_bible_tamil with Transformers:

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

pipe = pipeline("text-generation", model="sujithrex/yutha_bible_tamil")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sujithrex/yutha_bible_tamil", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sujithrex/yutha_bible_tamil with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sujithrex/yutha_bible_tamil

SGLang

How to use sujithrex/yutha_bible_tamil 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 "sujithrex/yutha_bible_tamil" \
    --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": "sujithrex/yutha_bible_tamil",
		"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 "sujithrex/yutha_bible_tamil" \
        --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": "sujithrex/yutha_bible_tamil",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use sujithrex/yutha_bible_tamil with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sujithrex/yutha_bible_tamil to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sujithrex/yutha_bible_tamil to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sujithrex/yutha_bible_tamil to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="sujithrex/yutha_bible_tamil",
    max_seq_length=2048,
)

Docker Model Runner
How to use sujithrex/yutha_bible_tamil with Docker Model Runner:
```
docker model run hf.co/sujithrex/yutha_bible_tamil
```

yutha_bible_tamil

LoRA adapter for Tamil-English Bible text generation, fine-tuned on Qwen3-8B.

Model Details

Model Description

This model is a LoRA adapter trained on Tamil-English Bible verses for biblical text generation and cross-lingual Bible study assistance.

Developed by: Sujith S
Model type: LoRA Adapter
Language(s): Tamil, English
License: Apache 2.0
Finetuned from model: unsloth/qwen3-8b-unsloth-bnb-4bit

Model Sources

Repository: https://huggingface.co/sujithrex/yutha_bible_tamil
Website: rexmi.in

Training Details

Training Data

Tamil-English Bible dataset containing all Bible verses.

Training Hyperparameters

Learning Rate: 2e-4
Batch Size: 2 per device (effective: 8)
Gradient Accumulation Steps: 4
Epochs: 3
Max Sequence Length: 2048
Optimizer: AdamW 8bit
Training regime: BF16/FP16 mixed precision

Speeds, Sizes, Times

Training Duration: 3 days
Adapter Size: 174MB

Intended Use

Tamil Bible text generation
Biblical content creation in Tamil
Cross-lingual Bible study assistance

Limitations

Specialized for biblical content
May not perform well on general Tamil text
Requires base model to function

Model Card Authors

Sujith S

Model Card Contact

rexmi.in

Framework versions

PEFT 0.16.0

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