frameai_-_ChatFrame-Instruct-Persian-Small-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

ChatFrame-Instruct-Persian-Small - GGUF

• Model creator: https://huggingface.co/frameai/
• Original model: https://huggingface.co/frameai/ChatFrame-Instruct-Persian-Small/

Original model description:

license: mit language:

• en
• fa library_name: transformers pipeline_tag: text-generation

---

ChatFrame-Persian Model Card

Model Description

ChatFrame-Persian is an instruction-following AI model developed by AIFRAME INC. It is a large language model (LLM) designed for English and Persian language support. This model can be used for a variety of commercial applications and fine-tuning. ChatFrame-Persian demonstrates superior performance when compared to similar models such as Llama-70B, GPT-3.5, and GPT-4.

Intended Uses & Limitations

ChatFrame-Persian is intended for a wide range of language-based tasks, including but not limited to:

• Text generation and language translation
• Question answering and conversational AI
• Language understanding and analysis

The model has been trained on a diverse dataset, but it is important to note that its performance may vary across different cultural, demographic, and phenotypic groups.

Model Architecture

ChatFrame-Persian utilizes a transformer-based architecture with a large number of parameters, allowing it to capture complex linguistic patterns and generate human-like responses.

Training Data and Methodology

The model was trained on a vast dataset containing text data from various sources, including books, websites, and social media. The training process involved advanced techniques such as large-batch training and fine-tuning to optimize performance.

Performance Metrics

ChatFrame-Persian excels in several key performance metrics, including accuracy, precision, and recall. It has been benchmarked against other leading models and consistently demonstrates superior results, especially in language generation and understanding tasks.

Potential Biases and Limitations

While ChatFrame-Persian has been trained to minimize biases, it is important to recognize that all language models have limitations. Users should be aware of potential biases related to the training data and the possibility of generating inappropriate or offensive responses. Additionally, the model may struggle with highly specialized or technical language.

Responsible AI Considerations

ChatFrame-Persian has been developed with a focus on responsible AI practices. The model has been trained to respect user privacy and adhere to ethical guidelines. It is important for users to monitor the model's output and provide feedback to continuously improve its performance and address any potential issues.

Using pipeline to run model(A100 GPU tested):

from transformers import pipeline
import torch

device = 0 if torch.cuda.is_available() else -1

def generate_response(user_input):
    pipe = pipeline("text-generation", model="frameai/ChatFrame-Instruct-Persian-Small", device = device)
    messages = [
        {"role": "user", "content": user_input},
    ]
    response = pipe(messages, max_length=512)
    return response[0]['generated_text'][1]["content"]

ai = generate_response(user_input = "ایران رو برام توصیف کن")
print(ai)

Authors

Trained by Experts: The brainchild of Mohammadmoein Pisoude (CEO) and Alex Romniof (Manager), ChatFrame-Persian is the result of the dedication and expertise of the entire AIFRAME INC team. Their combined efforts in coding and project management have led to the creation of this innovative model.