| | --- |
| | languages: |
| | - ar |
| | - en |
| | license: mit |
| | tags: |
| | - pytorch |
| | - llama |
| | - text-generation |
| | - conversational |
| | - chatbot |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Chat Model |
| |
|
| | This is a custom chat model fine-tuned for conversational AI. The model is based on LLaMA architecture and is specifically designed for Arabic and English conversations. |
| |
|
| | ## Model Details |
| | - **Architecture**: LLaMA |
| | - **Task**: Text Generation |
| | - **Language**: Arabic/English |
| | - **License**: MIT |
| | - **Model Size**: Large |
| | - **Training Data**: Custom conversational data |
| | - **Optimization**: Quantized (int8) |
| |
|
| | ## Usage |
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | model = AutoModelForCausalLM.from_pretrained("gamer-to/chat-model") |
| | tokenizer = AutoTokenizer.from_pretrained("gamer-to/chat-model") |
| | |
| | # Example input |
| | input_text = "مرحبا كيف حالك؟" |
| | inputs = tokenizer(input_text, return_tensors="pt") |
| | outputs = model.generate( |
| | **inputs, |
| | max_length=256, |
| | temperature=0.7, |
| | do_sample=True, |
| | top_p=0.95 |
| | ) |
| | response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | ``` |
| |
|
| | ## Inference API |
| | This model is compatible with Hugging Face's Inference API. You can use it with the following endpoint: |
| |
|
| | ``` |
| | POST https://api-inference.huggingface.co/models/gamer-to/chat-model |
| | ``` |
| |
|
| | ## Model Performance |
| | - Optimized for conversational tasks |
| | - Supports both Arabic and English |
| | - Fast response times |
| | - High-quality responses |
| |
|
| | ## Requirements |
| | - PyTorch |
| | - Transformers |
| | - CUDA (optional for GPU acceleration) |
| | --- |
| | pipeline_tag: text-generation |
| | --- |
