Instructions to use assix-research/lebanese-llama-3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use assix-research/lebanese-llama-3.1-8b with Transformers:

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

pipe = pipeline("text-generation", model="assix-research/lebanese-llama-3.1-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("assix-research/lebanese-llama-3.1-8b")
model = AutoModelForCausalLM.from_pretrained("assix-research/lebanese-llama-3.1-8b", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use assix-research/lebanese-llama-3.1-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "assix-research/lebanese-llama-3.1-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "assix-research/lebanese-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/assix-research/lebanese-llama-3.1-8b

SGLang

How to use assix-research/lebanese-llama-3.1-8b 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 "assix-research/lebanese-llama-3.1-8b" \
    --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": "assix-research/lebanese-llama-3.1-8b",
		"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 "assix-research/lebanese-llama-3.1-8b" \
        --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": "assix-research/lebanese-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use assix-research/lebanese-llama-3.1-8b with Docker Model Runner:
```
docker model run hf.co/assix-research/lebanese-llama-3.1-8b
```

Anthony Assi commited on Jan 23

Commit

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@@ -31,7 +31,17 @@ This model was trained and validated on the **NVIDIA DGX Spark**, the world’s
 Try the model instantly in your browser without any setup:
 👉 **[Lebanese Llama Chat Demo](https://huggingface.co/spaces/esix117/lebanese-llama-demo)**
 ## 🚀 Model Features
 * **Dialectal Authenticity:** Fine-tuned to recognize and generate Lebanese syntax, specifically the "B" prefix for verbs and regional idioms (e.g., *mni7*, *shou fi ma fi*).

 Try the model instantly in your browser without any setup:
 👉 **[Lebanese Llama Chat Demo](https://huggingface.co/spaces/esix117/lebanese-llama-demo)**
+## 🎭 The Lebanese Persona
+To get the most authentic "Ammiya" experience, use this system prompt. It activates the model's specialized cultural knowledge and linguistic patterns.
+**System Prompt:**
+> "You are an authentic Lebanese AI assistant. Speak strictly in the Lebanese dialect (Ammiya). Use expressions like 'Yalla', 'Walaw', 'Mni7', and 'Ya zalame'. Respond in Arabizi (using numbers like 3, 7, 2) or Arabic script as requested."
+### Example Comparison
+* **User:** "Kifak? Khabbirni kif l wade3 bi Lebnen l yom bi kel sra7a."
+* **Lebanese-Llama:** "Ya zalame, l wade3 de7ek mtl kel marra. Kelshi mni7, hamdellah."
+---
 ## 🚀 Model Features
 * **Dialectal Authenticity:** Fine-tuned to recognize and generate Lebanese syntax, specifically the "B" prefix for verbs and regional idioms (e.g., *mni7*, *shou fi ma fi*).