Transformers
TensorBoard
Safetensors
PEFT
Arabic
arabic
egyptian-arabic
dialectal-arabic
colloquial
chat
chatbot
lora
qwen2.5
Instructions to use MenemAI/smoke-arabic-chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MenemAI/smoke-arabic-chatbot with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MenemAI/smoke-arabic-chatbot", dtype="auto") - PEFT
How to use MenemAI/smoke-arabic-chatbot with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,756 Bytes
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base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: smoke-arabic-chatbot
tags:
- arabic
- egyptian-arabic
- dialectal-arabic
- colloquial
- chat
- chatbot
- lora
- peft
- qwen2.5
licence: license
datasets:
- kokojake/oasst2_egyptian_arabic_convs
- kokojake/lmsys_egyptian_arabic_convs
- Omar-youssef/islamic-qa-egyptian-arabic
- miscovery/General_Facts_in_English_Arabic_Egyptian_Arabic
- Elfsong/Qwen3_4B_Arabic_200-responses-Egyptian
language:
- ar
---
# Model Card for smoke-arabic-chatbot
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MenemAI/smoke-arabic-chatbot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 1.3.0
- Transformers: 5.8.0
- Pytorch: 2.11.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2
## Citations
Cite TRL as:
```bibtex
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
``` |