Instructions to use QCRI/Fanar-1-9B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QCRI/Fanar-1-9B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="QCRI/Fanar-1-9B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QCRI/Fanar-1-9B-Instruct")
model = AutoModelForCausalLM.from_pretrained("QCRI/Fanar-1-9B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QCRI/Fanar-1-9B-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QCRI/Fanar-1-9B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QCRI/Fanar-1-9B-Instruct

SGLang

How to use QCRI/Fanar-1-9B-Instruct 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-Instruct" \
    --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": "QCRI/Fanar-1-9B-Instruct",
		"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 "QCRI/Fanar-1-9B-Instruct" \
        --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": "QCRI/Fanar-1-9B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QCRI/Fanar-1-9B-Instruct with Docker Model Runner:
```
docker model run hf.co/QCRI/Fanar-1-9B-Instruct
```

Fanar-1-9B-Instruct

Commit History

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Commit History

upload bfloat16 model 0c4ca69

Update README.md 0385f32

Update README.md a862ad2

Update README.md 84816fe

Update README.md f2c2434

added model files 40b2c10

Update README.md 7263ed7

Update README.md 5a22b70

Update README.md 13375ca

Update README.md cd25d05

Update README.md 0ab4626

Update README.md 4ad5a83

Update README.md f45d1bc

initial commit 9c0d174

upload bfloat16 model

0c4ca69

Update README.md

0385f32

Update README.md

a862ad2

Update README.md

84816fe

Update README.md

f2c2434

added model files

40b2c10

Update README.md

7263ed7

Update README.md

5a22b70

Update README.md

13375ca

Update README.md

cd25d05

Update README.md

0ab4626

Update README.md

4ad5a83

Update README.md

f45d1bc

initial commit

9c0d174