library_name: transformers
tags:
- turkish
- general tasks
- RAG
- SFT
license: apache-2.0
language:
- tr
- en
pipeline_tag: text2text-generation
base_model: mistralai/Mistral-7B-v0.3
model-index:
- name: cymist-2-v03-SFT
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 59.12
name: normalized accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 82.56
name: normalized accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.12
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 36.61
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.43
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.65
name: accuracy
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
CypienAI team