Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant	Model Size	lm_head
2.2	2208 MB	6
2.5	2438 MB	6
3.0	2854 MB	6
3.5	3269 MB	6
3.75	3477 MB	6
4.0	3685 MB	6
4.25	3892 MB	6
5.0	4515 MB	6
6.0	5358 MB	8
6.5	5789 MB	8
8.0	6655 MB	8

Model Card for Cymist2-v0.3-SFT

Model Description

Cymist2-v0.3 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.

Developed by: Cypien AI Team
Model type: Language Model for Text-Generation
Language(s) (NLP): Turkish, English
License: Apache-2.0
Finetuned from model: mistralai/Mistral-7B-v0.3

Direct Use

This model is designed for direct use in general applications requiring natural 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.

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.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "cypienai/cymist2-v03-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 regarding that 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)

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.

Environmental Impact

The training of Cymist2-v0.3-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.

0.9 kg of CO2eq on 12 hours H100 utilization

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

[email protected]

CypienAI team

Zoyd
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cypienai_cymist-2-v03-SFT-2_5bpw_exl2