Cymist2 V01 SFT

🚀 Model Card for Cymist2-v0.1-SFT

Cymist2-v0.1-SFT is a state - of - the - art language model developed by the Cypien AI Team. It is optimized for text - generation tasks, leveraging the transformers library and available under the Apache - 2.0 license.

🚀 Quick Start

New Version Available

Check out the new version cymist-2-v02-SFT.

Model Setup

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "cypienai/cymist2-v01-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token_id = tokenizer.eos_token_id

Use Flash - Attention 2 to further speed - up generation

First, make sure to install flash - attn. Refer to the original repository of Flash Attention for package installation. Simply change the snippet above with:

model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.bfloat16,
        attn_implementation="flash_attention_2"
        )

Example usage

Here's the prompt template for this model:

question="Yenilenebilir gıdalar nelerdir ?"
prompt= f"[INST] {question} [/INST]"

with torch.inference_mode():
  input_ids = tokenizer(prompt, return_tensors="pt").to(device)
  output = model.generate(**input_ids, max_new_tokens=8096)
  decoded_output = tokenizer.decode(output[0], skip_special_tokens=False)
  print(decoded_output)

✨ Features

• Multilingual Support: Supports both Turkish and English languages, enabling a wide range of applications.
• Optimized for Text - Generation: Ideal for general text - generation tasks, RAG, and Turkish language understanding.
• Flash - Attention 2 Compatibility: Allows for faster generation speeds.

📦 Installation

The model can be installed using the transformers library. You can use the following code to load the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "cypienai/cymist2-v01-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

💻 Usage Examples

Basic Usage

The basic usage is shown in the example above, where you can input a question and get a generated response.

Advanced Usage

You can use Flash - Attention 2 to speed up the generation process as shown in the code snippet above.

📚 Documentation

Model Description

Cymist2 - v0.1 is a cutting - edge language model developed by the Cypien AI Team, optimized for text - generation tasks. The model leverages the transformers library and is available under the Apache - 2.0 license.

Direct Use

This model is designed for direct use in general applications requiring Turkish language understanding, RAG, and text - generation capabilities. It can be integrated into chatbots, virtual assistants, and other AI systems where understanding and generating human - like responses are essential.

Out - of - Scope Use

The model is not intended for use in critical systems where incorrect answers could lead to harm or in contexts that require domain - specific knowledge beyond the scope of general text - generation.

Bias, Risks, and Limitations

The model, like all AI models, may inherit biases from its training data. Users should be aware of these potential biases and consider them when integrating the model into applications.

Training Details

Training Data

The model was trained on a diverse set of Turkish & English language sources, encompassing a wide range of topics to ensure comprehensive language understanding.

Training Procedure

Preprocessing

The training data underwent standard NLP preprocessing steps, including tokenization, normalization, and possibly data augmentation to enhance the model's robustness.

Training Hyperparameters

• Learning Rate: 2e - 4

Environmental Impact

The training of Cymist2 - v0.1 - SFT was conducted with a focus on minimizing carbon emissions. Detailed carbon emission statistics will be provided based on the Machine Learning Impact calculator, considering hardware type, usage hours, cloud provider, compute region, and total emissions. 0.93 kg of CO2eq. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Technical Specifications

More detailed technical specifications, including model architecture, compute infrastructure, hardware, and software, will be provided to offer insights into the model's operational context.

Citation

When citing this model in your research, please refer to this model card for information about the model's development and capabilities.

Glossary

A glossary section can be added to define specific terms and calculations related to the model, ensuring clarity for all potential users.

More Information [optional]

For more information or inquiries about the model, please contact the Cypien AI Team.

Model Card Contact

info@cypien.ai CypienAI team