---
base_model: inceptionai/jais-adapted-7b-chat
language:
- ar
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
datasets:
- Wajdi1976/Tunisian_Derja_Dataset
library_name: transformers
---
## Model Overview

Labess is a family of open models instruction-tuned for Tunisian Derja, it's a continual pre-training version of jais-adapted-7b-chat with 1tunisian_Derja_Dataset
# Uploaded  model

- **Developed by:** Wajdi1976
- **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:

```sh
pip install unsloth
```
### First, Load the Model
```python
from unsloth import FastLanguageModel
import torch
max_seq_length = 600 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Wajdi1976/Labess",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,    
)
```

### Second, Try the model 
```python
prompt_ar=" يمكنك الإجابة باللهجة التونسية فقط.\n\nأكمل المحادثة أدناه بين [|Human|] و [|AI|]:\n### Input: [|Human|] {Question}\n### Response: [|AI|]"
device = "cuda" if torch.cuda.is_available() else "cpu"
FastLanguageModel.for_inference(model)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
def get_response(text, tokenizer=tokenizer, model=model):
    tokenized = tokenizer(text, return_tensors="pt")
    input_ids, attention_mask = tokenized['input_ids'].to(device), tokenized['attention_mask'].to(device)
    input_len = input_ids.shape[-1]
    generate_ids = model.generate(
        input_ids,
        attention_mask=attention_mask,
        top_p=1,
        temperature=0.3,
        max_length=600,
        min_length=input_len + 4,
        repetition_penalty=1.2,
        do_sample=True,
        pad_token_id=tokenizer.pad_token_id
    )
    response = tokenizer.batch_decode(
        generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )[0]
    response = response.split("### Response :")[-1].lstrip()
    return response

ques = "  احكي معايا باللهجة التونسية"
text = prompt_ar.format_map({'Question': ques})
print(get_response(text))
```
- Response:  بالطبع نجم نجاوب على سؤالك واللهجه التونسية هي نوع من اللهجات اللي تتكلم في بلاد اسمها تونس كيفما فما برشا لهجات مختلفة كيما الإنجليزية أو الإسبانية الناس في العالم يتكلموا لغات متنوعة أما اللهجة التونسى هيا الطريقة الخاصة بالكلام للناس في البلاد هذيك يعني كان تسألني سؤال بالهججه تونسي نحب نعاونك باش نفهموه 

## Citations
When using the **Tunisian Derja Dataset** dataset, please cite:

```bibtex
@model{linagora2025LLM-tn,
  author = {Wajdi Ghezaiel and Jean-Pierre Lorré},
  title = {Labess:Tunisian Derja LLM},
  year = {2025},
  month = {January},  
  url = {https://huggingface.co/datasets/Wajdi1976/Labess}
}

```
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)