Instructions to use large-traversaal/Alif-1.0-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use large-traversaal/Alif-1.0-3B-Instruct with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("large-traversaal/Alif-1.0-3B-Instruct", dtype="auto") - llama-cpp-python
How to use large-traversaal/Alif-1.0-3B-Instruct with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="large-traversaal/Alif-1.0-3B-Instruct", filename="Alif-1.0-3B-Instruct-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use large-traversaal/Alif-1.0-3B-Instruct with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M # Run inference directly in the terminal: llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M # Run inference directly in the terminal: llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
Use Docker
docker model run hf.co/large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use large-traversaal/Alif-1.0-3B-Instruct with Ollama:
ollama run hf.co/large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
- Unsloth Studio new
How to use large-traversaal/Alif-1.0-3B-Instruct 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 large-traversaal/Alif-1.0-3B-Instruct 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 large-traversaal/Alif-1.0-3B-Instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for large-traversaal/Alif-1.0-3B-Instruct to start chatting
- Docker Model Runner
How to use large-traversaal/Alif-1.0-3B-Instruct with Docker Model Runner:
docker model run hf.co/large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
- Lemonade
How to use large-traversaal/Alif-1.0-3B-Instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M
Run and chat with the model
lemonade run user.Alif-1.0-3B-Instruct-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_MUse Docker
docker model run hf.co/large-traversaal/Alif-1.0-3B-Instruct:Q4_K_MAlif 1.0 3B Instruct
Alif 1.0 3B Instruct is an open-source instruction-tuned language model developed by traversaal.ai, focused on Urdu and English understanding and reasoning. Fine-tuned with high-quality, culturally aware synthetic data, Alif delivers strong multilingual performance with a particular emphasis on low-resource language alignment and nuanced instruction following.
Key Highlights
- Multilingual: Optimized for Urdu (primary) and English (secondary)
- Instruction-tuned: Trained on synthetic QA, chat, and reasoning tasks
- Lightweight: Only 3 billion parameters — fast, memory-efficient
- 4-bit Quantized Version: Available for on-device inference
- Training Framework: Fine-tuned 2× faster using Unsloth + TRL
Model Metadata
- Developer: large-traversaal
- License: Apache 2.0
- Base Model:
unsloth/Llama-3.2-3B - Model ID:
Alif-1.0-3B-Instruct - Parameters: 3 billion
Use Cases
Alif is ideal for:
- Urdu and English chatbots
- Question answering in low-resource languages
- Translation, summarization, and creative writing
- Running on edge devices with the 4-bit version
Citation
@article{ShafiqueAlif2025,
title = {Alif: Advancing Urdu Large Language Models via Multilingual Synthetic Data Distillation},
author = {Muhammad Ali Shafique and Kanwal Mehreen and Muhammad Arham and Maaz Amjad and Sabur Butt and Hamza Farooq},
journal = {arXiv preprint arXiv:2510.09051},
year = {2025},
url = {https://arxiv.org/abs/2510.09051}
}
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4-bit
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M# Run inference directly in the terminal: llama-cli -hf large-traversaal/Alif-1.0-3B-Instruct:Q4_K_M