Instructions to use linagora/Labess-7b-chat-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use linagora/Labess-7b-chat-16bit with Transformers:

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

pipe = pipeline("text-generation", model="linagora/Labess-7b-chat-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("linagora/Labess-7b-chat-16bit")
model = AutoModelForCausalLM.from_pretrained("linagora/Labess-7b-chat-16bit")
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

vLLM

How to use linagora/Labess-7b-chat-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "linagora/Labess-7b-chat-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "linagora/Labess-7b-chat-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/linagora/Labess-7b-chat-16bit

SGLang

How to use linagora/Labess-7b-chat-16bit 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 "linagora/Labess-7b-chat-16bit" \
    --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": "linagora/Labess-7b-chat-16bit",
		"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 "linagora/Labess-7b-chat-16bit" \
        --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": "linagora/Labess-7b-chat-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use linagora/Labess-7b-chat-16bit 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 linagora/Labess-7b-chat-16bit 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 linagora/Labess-7b-chat-16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for linagora/Labess-7b-chat-16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="linagora/Labess-7b-chat-16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use linagora/Labess-7b-chat-16bit with Docker Model Runner:
```
docker model run hf.co/linagora/Labess-7b-chat-16bit
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview

Labess-7b-chat is an open model instruction-tuned for Tunisian Derja, it's a continual pre-training version of jais-adapted-7b-chat with tunisian_Derja_Dataset

Uploaded model

Developed by: Linagora
License: apache-2.0
Finetuned from model : inceptionai/jais-adapted-7b-chat

Usage

Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:

pip install transformers

Usage

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="linagora/Labess-7b-chat-16bit",
    model_kwargs={"torch_dtype": torch.bfloat16},    
    device="cuda" # replace with "mps" to run on a Mac device
)

messages = [
    {"role": "user", "content": 'وين تجي تونس؟'},
]

outputs = pipe(messages, max_new_tokens=64, do_sample=True, temperature=0.2)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)

- Response:تونس هي بلاد في شمال إفريقيا هي بلاد جميلة برشة ومعروفة في العالم الكل هي بلاد فيها مناظر طبيعية

Citations

When using this model Labess-7b-chat, please cite:

@model{linagora2025LLM-tn,
  author = {Wajdi Ghezaiel and Jean-Pierre Lorré},
  title = {Labess-7b-chat:Tunisian Derja LLM},
  year = {2025},
  month = {January},  
  url = {https://huggingface.co/datasets/linagora/Labess-7b-chat-16bit}
}

Acknowledgements

Training of Labess-7b-chat was made possible by computing AI and storage resources by GENCI at IDRIS thanks to the grant 2024-AD011014561 on the supercomputer Jean Zay’s A100 partition.