Text Generation
Transformers
Safetensors
GGUF
Arabic
English
arabic
edge
small-language-model
sft
dpo
qwen2
Eval Results (legacy)
conversational
Instructions to use RightNowAI/RightNow-Arabic-0.5B-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RightNowAI/RightNow-Arabic-0.5B-Turbo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RightNowAI/RightNow-Arabic-0.5B-Turbo", dtype="auto") - llama-cpp-python
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RightNowAI/RightNow-Arabic-0.5B-Turbo", filename="gguf/RightNow-Arabic-0.5B-Turbo-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RightNowAI/RightNow-Arabic-0.5B-Turbo: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 RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RightNowAI/RightNow-Arabic-0.5B-Turbo: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 RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Use Docker
docker model run hf.co/RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RightNowAI/RightNow-Arabic-0.5B-Turbo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RightNowAI/RightNow-Arabic-0.5B-Turbo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
- SGLang
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo 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 "RightNowAI/RightNow-Arabic-0.5B-Turbo" \ --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": "RightNowAI/RightNow-Arabic-0.5B-Turbo", "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 "RightNowAI/RightNow-Arabic-0.5B-Turbo" \ --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": "RightNowAI/RightNow-Arabic-0.5B-Turbo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Ollama:
ollama run hf.co/RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
- Unsloth Studio new
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo 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 RightNowAI/RightNow-Arabic-0.5B-Turbo 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 RightNowAI/RightNow-Arabic-0.5B-Turbo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RightNowAI/RightNow-Arabic-0.5B-Turbo to start chatting
- Pi new
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Docker Model Runner:
docker model run hf.co/RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
- Lemonade
How to use RightNowAI/RightNow-Arabic-0.5B-Turbo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RightNowAI/RightNow-Arabic-0.5B-Turbo:Q4_K_M
Run and chat with the model
lemonade run user.RightNow-Arabic-0.5B-Turbo-Q4_K_M
List all available models
lemonade list
| { | |
| "current_quality": { | |
| "bs1": { | |
| "bs": 1, | |
| "elapsed_s": 4.636457004991826, | |
| "tokens_generated": 128, | |
| "tok_per_sec": 27.607287172552066 | |
| }, | |
| "bs4": { | |
| "bs": 4, | |
| "elapsed_s": 4.483801496011438, | |
| "tokens_generated": 492, | |
| "tok_per_sec": 109.72831880217227 | |
| }, | |
| "bs8": { | |
| "bs": 8, | |
| "elapsed_s": 4.641314439999405, | |
| "tokens_generated": 992, | |
| "tok_per_sec": 213.73255633163416 | |
| }, | |
| "bs32": { | |
| "bs": 32, | |
| "elapsed_s": 4.989478911011247, | |
| "tokens_generated": 3968, | |
| "tok_per_sec": 795.2734285023328 | |
| } | |
| }, | |
| "clean_greedy_sdpa": { | |
| "bs1": { | |
| "bs": 1, | |
| "elapsed_s": 1.5616609920107294, | |
| "tokens_generated": 128, | |
| "tok_per_sec": 81.96401181487703 | |
| }, | |
| "bs4": { | |
| "bs": 4, | |
| "elapsed_s": 1.525149337016046, | |
| "tokens_generated": 492, | |
| "tok_per_sec": 322.5913607664137 | |
| }, | |
| "bs8": { | |
| "bs": 8, | |
| "elapsed_s": 1.604114955989644, | |
| "tokens_generated": 992, | |
| "tok_per_sec": 618.4095449618166 | |
| }, | |
| "bs32": { | |
| "bs": 32, | |
| "elapsed_s": 1.8422480699955486, | |
| "tokens_generated": 3968, | |
| "tok_per_sec": 2153.8901652965706 | |
| } | |
| }, | |
| "clean_greedy_fa2": { | |
| "bs1": { | |
| "bs": 1, | |
| "elapsed_s": 2.231158509996021, | |
| "tokens_generated": 128, | |
| "tok_per_sec": 57.36929914505638 | |
| }, | |
| "bs4": { | |
| "bs": 4, | |
| "elapsed_s": 1.9051841270120349, | |
| "tokens_generated": 492, | |
| "tok_per_sec": 258.2427561852619 | |
| }, | |
| "bs8": { | |
| "bs": 8, | |
| "elapsed_s": 1.9795321589917876, | |
| "tokens_generated": 984, | |
| "tok_per_sec": 497.08715038060785 | |
| }, | |
| "bs32": { | |
| "bs": 32, | |
| "elapsed_s": 2.203783990989905, | |
| "tokens_generated": 3936, | |
| "tok_per_sec": 1786.0189637878307 | |
| } | |
| }, | |
| "compiled_fa2": { | |
| "bs1": { | |
| "bs": 1, | |
| "elapsed_s": 2.224584960989887, | |
| "tokens_generated": 128, | |
| "tok_per_sec": 57.538822856665846 | |
| }, | |
| "bs4": { | |
| "bs": 4, | |
| "elapsed_s": 1.940974594996078, | |
| "tokens_generated": 492, | |
| "tok_per_sec": 253.480906586 | |
| }, | |
| "bs8": { | |
| "bs": 8, | |
| "elapsed_s": 1.9738178760162555, | |
| "tokens_generated": 984, | |
| "tok_per_sec": 498.5262378847238 | |
| }, | |
| "bs32": { | |
| "bs": 32, | |
| "elapsed_s": 2.400167693005642, | |
| "tokens_generated": 3936, | |
| "tok_per_sec": 1639.8854177855762 | |
| } | |
| } | |
| } |