README.md

---
license: apache-2.0
pipeline_tag: text-generation
tags:
- multilingual
- PyTorch
- Transformers
- gpt3
- gpt2
- Deepspeed
- Megatron
datasets:
- mc4
- Wikipedia

widget:
- text: "Ich weiß, dass du müde bist, aber können wir heute Abend noch einen Spaziergang machen? peter szemraj: ich" 
  example_title: "walk - Deutsch"
- text: "peter szemraj: 我喜欢穿很酷的衣服"
  example_title: "fashion - Chinese"
- text: "Wat zei je over mijn moeder? peter szemraj: Ik"
  example_title: "🚎 - Dutch"
- text: "Zagadka: Najpierw mnie zjadasz, a potem sam zostajesz zjedzony. Czym ja jestem? peter szemraj: Czy to"
  example_title: "brain teaser - Polish"
- text: "Minha amiga diz que conhece todas as línguas, mas não fala nenhuma delas... o que há de errado com ela? peter szemraj: Eu" 
  example_title: "language - Portuguese"
- text: "se potesse vivere ovunque, dove sarebbe? peter szemraj: Io" 
  example_title: "dream living place - Italian"
- text: "Can you take me for dinner somewhere nice this time?\npeter szemraj:\n\n"
  example_title: "dinner"
- text: "What really makes you angry?\npeter szemraj:\n\n" 
  example_title: "pet peeve"
- text: "Jak nazwać aligatora, który właśnie przeszedł operację usunięcia lewego ramienia?peter szemraj: Ja" 
  example_title: "alligator - Polish"
- text: "Warum sind Transformers für die Sprachmodellierung wichtig?  peter szemraj: Es ist"
  example_title: "Transformers - German"
- text: "как написать хорошие подсказки для языковых моделей? peter szemraj: сначала вам нужно"
  example_title: "prompt tutorial - Russian"
- text: "Pewien mężczyzna wpycha swój samochód do hotelu i mówi właścicielowi, że jest bankrutem. Dlaczego? peter szemraj: może"
  example_title: "brain teaser - Polish 2"
- text: "Zagadka: Mówię bez ust i słyszę bez uszu. Nie mam ciała, ale ożywiam się wraz z wiatrem. Czym jestem? peter szemraj: Czy to"
  example_title: "brain teaser - Polish 3"
  
inference:
  parameters:
    min_length: 2
    max_length: 64
    no_repeat_ngram_size: 3
    do_sample: True
    top_p: 0.95
    top_k: 50
    temperature: 0.65
    repetition_penalty: 3.5
    
---


# mGPT: fine-tune on message data - 2E

- This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. This builds on the minimum-working-example checkpoint [here](https://huggingface.co/pszemraj/mGPT-Peter-mwe).
- 2E = 2 epochs

## Model description

- testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitly 

**Interesting findings thus far:**

- Passing a generic word after the `<name-identifier>` that is in a non-English language helps ensure the model responds in the question language (see: any example).
- Model generations (in general) remain semantically consistent, even if the generations switch from  `<language>`to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding"

### Usage in python

Install the transformers library if you don't have it:

```
pip install -U transformers
```

load the model into a pipeline object:


```
from transformers import pipeline
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
my_chatbot = pipeline('text-generation', 
                      'pszemraj/mGPT-Peter-2E',
                      device=0 if device == 'cuda' else -1,
                    )
```




## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1 (in addition to all training on prior checkpoints)

### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1