saifamdouni/TunCHAT-V0.2

Model Card: TunChat-V0.2

Model Overview:

• Model Name: TunChat-V0.2
• Model Size: 2B parameters
• Instruction-Tuned: Yes
• Language: Tunisian Dialect
• Use Case Focus: Conversational exchanges, translation, summarization, content generation, and cultural research.

Model Description: TunChat-V0.2 is a 2-billion parameter language model specifically instruction-tuned for the Tunisian dialect. It is designed to handle tasks such as conversational exchanges, informal text summarization, and culturally-aware content generation. The model is optimized to understand and generate text in Tunisian Dialect, enabling enhanced performance for applications targeting Tunisian users.

Intended Use:

• Conversational agents and chatbots operating in Tunisian Dialect.
• Translation, summarization, and content generation in informal Tunisian dialect.
• Supporting cultural research related to Tunisian language and heritage.

How to Use:

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="saifamdouni/TunChat-V0.2",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda" # replace with "mps" to run on a Mac device
)

messages = [
    {"role": "user", "content": 'شكون صنعك'},
]

outputs = pipe(messages,
              max_new_tokens=2048,
              do_sample=True,
              top_p=0.95,
              temperature=0.7,
              top_k=50)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)

صنعوني جماعة من المهندسين والمطورين التوانسة. يحبوا يطوّروا الذكاء الاصطناعي في تونس و يسهلوا استخدامه باللهجة متاعنا.

Quantized Versions:

• GGUF quantized versions will be released later.

Training Dataset:

• Tun-SFT dataset (to be released later):
  • A mix between organically collected and synthetically generated data

Limitations and Ethical Considerations:

• The model may occasionally produce incorrect or biased responses.
• The model may occasionally produce culturally inappropriate responses.
• It may not perform optimally on formal Tunisian Arabic texts.

Future Plans:

• Release of GGUF quantized versions.
• Open-source availability of the Tun-SFT dataset.

Author: Saif Eddine Amdouni