
<h1 style="font-size: 42px">Instructions ruGPT large v0.11_25к_a<h1/>



# Model Summary

> Это ruGPTlarge дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке


# Quick Start


```python
from transformers import GPT2TokenizerFast,GPT2LMHeadModel
tokenizer = GPT2TokenizerFast.from_pretrained("AlexWortega/instruct_rugptlarge")
special_tokens_dict = {'additional_special_tokens': ['<code>', '</code>', '<instructionS>', '<instructionE>', '<next>']}

tokenizer.add_special_tokens(special_tokens_dict)
device = 'cuda'
model = GPT2LMHeadModel.from_pretrained("AlexWortega/instruct_rugptlarge")
model.to(device)

model.resize_token_embeddings(len(tokenizer))

def generate_seqs(q,model, k=2):
    gen_kwargs = {
        "min_length": 20,
        "max_new_tokens": 100,
        "top_k": 50,
        "top_p": 0.7,
        "do_sample": True,  
        "early_stopping": True,
        "no_repeat_ngram_size": 2,
        "eos_token_id": tokenizer.eos_token_id,
        "pad_token_id": tokenizer.eos_token_id,
        "use_cache": True,
        "repetition_penalty": 1.5,  
        "length_penalty": 1.2,  
        "num_beams": 4,
        "num_return_sequences": k
    }
    q = q + '<instructionS>'
    t = tokenizer.encode(q, return_tensors='pt').to(device)
    g = model.generate(t, **gen_kwargs)
    generated_sequences = tokenizer.batch_decode(g, skip_special_tokens=True)
    
    return  generated_sequences

```
обратите внимание, что лучшие параметры для генерации 
```
gen_kwargs = {
        "min_length": 20,
        "max_new_tokens": 100,
        "top_k": 50,
        "top_p": 0.9,
        "do_sample": True,  
        "early_stopping": True,
        "no_repeat_ngram_size": 2,
        "eos_token_id": tokenizer.eos_token_id,
        "pad_token_id": tokenizer.eos_token_id,
        "use_cache": True,
        "repetition_penalty": 1.5,  
        "length_penalty": 0.8,  
        "num_beams": 4,
        "num_return_sequences": k
    }
```  
# 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

ван дей пипл, ван дееей

## BibTeX entry and citation info

```bibtex
@article{
  title={GPT2xl is underrated task solver},
  author={Nickolich Aleksandr, 5Q, datascience, Ilya Gusev, Alex Kukushkin, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
  year={2023}
}
```
{"language": ["ru"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finance", "code"], "datasets": ["IlyaGusev/ru_turbo_alpaca"], "inference": {"parameters": {"min_length": 20, "max_new_tokens": 250, "top_k": 50, "top_p": 0.9, "early_stopping": true, "no_repeat_ngram_size": 2, "use_cache": true, "repetition_penalty": 1.5, "length_penalty": 0.8, "num_beams": 2}}, "pipeline_tag": "text-generation", "widget": [{"text": "\u041c\u043e\u0436\u0435\u0442 \u043b\u0438 \u0432\u0441\u0442\u0440\u0435\u0442\u0438\u0442\u044c\u0441\u044f \u043f\u0438\u043d\u0433\u0432\u0438\u043d \u0438 \u0431\u0435\u043b\u044b\u0439 \u043c\u0435\u0434\u0432\u0435\u0434\u044c?", "example_title": "Question Answering"}, {"text": "\u041a\u0430\u043a \u0437\u0430\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u043c\u043d\u043e\u0433\u043e \u0434\u0435\u043d\u0435\u0433 \u043e\u0431\u0443\u0447\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u0438? <instructionS>", "example_title": "Open domain Knoweledge"}, {"text": "\u041d\u0430\u043f\u0438\u0448\u0438 \u043d\u0430 python \u043a\u043e\u0434 \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b\u0432\u0435\u0434\u0435\u0442 \u043f\u0440\u0438\u0432\u0435\u0442 \u043c\u0438\u0440 <code>", "example_title": "Code writing"}, {"text": "\u041f\u0435\u0440\u0435\u0432\u0435\u0434\u0438 \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u0438\u0439 \u0438 \u0443\u043a\u0430\u0436\u0438 \u044f\u0437\u044b\u043a \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b\u0430: My name is Arthur.", "example_title": "Zero shor translate"}, {"text": "\u041a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u044b\u0439 \u043a\u043e\u0440\u0435\u043d\u044c \u0438\u0437 x \u0440\u0430\u0432\u0435\u043d \u043a\u0443\u0431\u0438\u0447\u0435\u0441\u043a\u043e\u043c\u0443 \u043a\u043e\u0440\u043d\u044e \u0438\u0437 y. \u0427\u0435\u043c\u0443 \u0440\u0430\u0432\u043d\u043e y \u0432 \u0441\u0442\u0435\u043f\u0435\u043d\u0438 2, \u0435\u0441\u043b\u0438 x = 4?", "example_title": "Math example"}]}
transformers pytorch safetensors gpt2 text-generation finance code ru dataset:IlyaGusev/ru_turbo_alpaca license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #finance #code #ru #dataset-IlyaGusev/ru_turbo_alpaca #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


<h1 style="font-size: 42px">Instructions ruGPT large v0.11_25к_a<h1/>



# Model Summary

> Это ruGPTlarge дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке


# Quick Start



обратите внимание, что лучшие параметры для генерации 
  
# 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

ван дей пипл, ван дееей

## BibTeX entry and citation info


# Model Summary

> Это ruGPTlarge дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке # Quick Start



обратите внимание, что лучшие параметры для генерации # 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 #safetensors #gpt2 #text-generation #finance #code #ru #dataset-IlyaGusev/ru_turbo_alpaca #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Model Summary

> Это ruGPTlarge дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке # Quick Start



обратите внимание, что лучшие параметры для генерации # 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 #safetensors #gpt2 #text-generation #finance #code #ru #dataset-IlyaGusev/ru_turbo_alpaca #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Model Summary

> Это ruGPTlarge дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке# Quick Start



обратите внимание, что лучшие параметры для генерации# 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