Instructions to use Redgerd/llama3-roman-urdu-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Redgerd/llama3-roman-urdu-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Redgerd/llama3-roman-urdu-finetuned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Redgerd/llama3-roman-urdu-finetuned", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Redgerd/llama3-roman-urdu-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Redgerd/llama3-roman-urdu-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Redgerd/llama3-roman-urdu-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Redgerd/llama3-roman-urdu-finetuned

SGLang

How to use Redgerd/llama3-roman-urdu-finetuned 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 "Redgerd/llama3-roman-urdu-finetuned" \
    --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": "Redgerd/llama3-roman-urdu-finetuned",
		"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 "Redgerd/llama3-roman-urdu-finetuned" \
        --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": "Redgerd/llama3-roman-urdu-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Redgerd/llama3-roman-urdu-finetuned 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 Redgerd/llama3-roman-urdu-finetuned 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 Redgerd/llama3-roman-urdu-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Redgerd/llama3-roman-urdu-finetuned to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Redgerd/llama3-roman-urdu-finetuned",
    max_seq_length=2048,
)

Docker Model Runner
How to use Redgerd/llama3-roman-urdu-finetuned with Docker Model Runner:
```
docker model run hf.co/Redgerd/llama3-roman-urdu-finetuned
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Redgerd/llama3-roman-urdu-finetuned

Redgerd/llama3-roman-urdu-finetuned is a multilingual instruction-tuned LLaMA 3 model fine-tuned on a combination of Roman Urdu QA pairs and English examples from the Stanford Alpaca dataset.

This model is designed to enhance performance in low-resource, multilingual, and instruction-following tasks, especially involving Roman Urdu.

Model Details

Base Model: Meta LLaMA 3
Architecture: Decoder-only transformer
Fine-tuned On: Custom Alpaca-style dataset
Languages: Roman Urdu 🇵🇰 & English 🇬🇧
Format: Instruction-tuning (compatible with Alpaca style)

Dataset Overview

A custom dataset with ~500 instruction-based examples in Roman Urdu and ~500 from Stanford Alpaca at from Redgerd/roman-urdu-alpaca-qa-mix

Training Setup

Frameworks: transformers, unsloth
Format: Instruction-based fine-tuning (instruction, input, output)
Environment: A100 GPU with bfloat16 precision
Checkpointing: Supported

Credits

Developed by Muhammad Salaar using:

Stanford Alpaca Dataset
GPT-4 for Roman Urdu generation
LLaMA 3.1 as the base model

For feedback or collaboration, visit: github.com/Redgerd

Downloads last month: -; Downloads are not tracked for this model. How to track

Collection including Redgerd/llama3-roman-urdu-finetuned

Roman Urdu Transliteration

Collection

A collection of models and datasets designed to standardize inconsistent Roman Urdu spellings. • 4 items • Updated Feb 10