Instructions to use QCRI/Fanar-1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/Fanar-1-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QCRI/Fanar-1-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QCRI/Fanar-1-9B") model = AutoModelForCausalLM.from_pretrained("QCRI/Fanar-1-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QCRI/Fanar-1-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QCRI/Fanar-1-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QCRI/Fanar-1-9B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QCRI/Fanar-1-9B
- SGLang
How to use QCRI/Fanar-1-9B 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 "QCRI/Fanar-1-9B" \ --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": "QCRI/Fanar-1-9B", "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 "QCRI/Fanar-1-9B" \ --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": "QCRI/Fanar-1-9B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QCRI/Fanar-1-9B with Docker Model Runner:
docker model run hf.co/QCRI/Fanar-1-9B
Download tokenizer.model from QCRI/Fanar-1-9B: direct link, hf CLI and curl.
- Browser
- Download file 2.12 MB
-
https://huggingface.co/QCRI/Fanar-1-9B/resolve/main/tokenizer.model
- Command line
-
hf download hf://QCRI/Fanar-1-9B/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/QCRI/Fanar-1-9B/resolve/main/tokenizer.model
2.12 MB
- Xet hash:
- df736c08b3aebc0abed19bf0026ce4661a89f9f75074d4b1ca1b7e24d379d7d1
- Size of remote file:
- 2.12 MB
- SHA256:
- c689fef08039792f515e5bcd67759b3bd73f845d8e7f0fda75471c6a103aad41
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