
<h1 style="font-size: 42px">Instructions ruGPT Small v0.1a<h1/>



# Model Summary

> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода


# Quick Start

```python
from transformers import pipeline
pipe = pipeline(model='AlexWortega/instruct_rugptSmall')
pipe('''Как собрать питон код?''')
```
or
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AlexWortega/instruct_rugptSmall")
model = AutoModelForCausalLM.from_pretrained("AlexWortega/instruct_rugptSmall")
```

# License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.



## Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.



# References

#Metrics

SOON

## BibTeX entry and citation info

```bibtex
@article{
  title={GPT2xl is underrated task solver},
  author={Nickolich Aleksandr, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
  year={2023}
}
```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finance", "code"], "datasets": ["IlyaGusev/habr", "Den4ikAI/russian_instructions", "wiki_qa"], "inference": {"parameters": {"max_new_tokens": 32, "temperature": 1, "top_k": 50, "top_p": 0.7, "do_sample": true}}, "pipeline_tag": "text-generation", "widget": [{"text": "\u0427\u0435\u043c \u043e\u0442\u043b\u0438\u0447\u0430\u0435\u0442\u0441\u044f \u043b\u043e\u0441\u044c \u043e\u0442 \u0435\u0436\u0430?", "example_title": "Question Answering"}, {"text": "\u041a\u0430\u043a \u0432\u044b\u043f\u0440\u043e\u0441\u0438\u0442\u044c \u043f\u043e\u0432\u044b\u0448\u0435\u043d\u0438\u0435?", "example_title": "Logical reasoning"}, {"text": "\u041a\u0430\u043a\u0430\u044f \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u0430 \u0437\u0430\u043a\u0438\u043f\u0430\u043d\u0438\u044f \u0430\u0437\u043e\u0442\u0430?", "example_title": "Scientific knowledge"}]}
transformers pytorch gpt2 text-generation finance code en dataset:IlyaGusev/habr dataset:Den4ikAI/russian_instructions dataset:wiki_qa license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #finance #code #en #dataset-IlyaGusev/habr #dataset-Den4ikAI/russian_instructions #dataset-wiki_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


<h1 style="font-size: 42px">Instructions ruGPT Small v0.1a<h1/>



# Model Summary

> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода


# Quick Start


or


# License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.



## Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.



# References

#Metrics

SOON

## BibTeX entry and citation info


# Model Summary

> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода # Quick Start


or # License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License. ## Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency. # References ## BibTeX entry and citation info
TAGS
#transformers #pytorch #gpt2 #text-generation #finance #code #en #dataset-IlyaGusev/habr #dataset-Den4ikAI/russian_instructions #dataset-wiki_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Model Summary

> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода # Quick Start


or # License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License. ## Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency. # References ## BibTeX entry and citation info
TAGS
#transformers #pytorch #gpt2 #text-generation #finance #code #en #dataset-IlyaGusev/habr #dataset-Den4ikAI/russian_instructions #dataset-wiki_qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Model Summary

> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода# Quick Start


or# License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.## Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.# References## BibTeX entry and citation info