

# pszemraj/gpt-peter-2.7B

- This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked [here](https://colab.research.google.com/gist/pszemraj/a59b43813437b43973c8f8f9a3944565/testing-pszemraj-gpt-peter-2-7b.ipynb) is a notebook for testing in Colab.
    - alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation
    - the telegram bot code and the model training code can be found [in this repository](https://github.com/pszemraj/ai-msgbot)
                      

## 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
my_chatbot = pipeline('text-generation', 
                      'pszemraj/gpt-peter-2.7B',
                      device=0 if torch.cuda.is_available() else -1,
                    )
```

generate text!

```
my_chatbot('Did you ever hear the tragedy of Darth Plagueis The Wise?')
```

_(example above for simplicity, but adding generation parameters such as `no_repeat_ngram_size` are recommended to get better generations)_

## Training procedure


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1

### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

{"tags": ["gpt-neo", "gpt-peter", "chatbot"], "inference": false, "base_model": "EleutherAI/gpt-neo-2.7B"}
transformers pytorch gpt_neo text-generation gpt-neo gpt-peter chatbot base_model:EleutherAI/gpt-neo-2.7B autotrain_compatible region:us
TAGS
#transformers #pytorch #gpt_neo #text-generation #gpt-neo #gpt-peter #chatbot #base_model-EleutherAI/gpt-neo-2.7B #autotrain_compatible #region-us 



# pszemraj/gpt-peter-2.7B

- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked here is a notebook for testing in Colab.
    - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
    - the telegram bot code and the model training code can be found in this repository
                      

## Usage in python 

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


load the model into a 'pipeline' object:



generate text!



_(example above for simplicity, but adding generation parameters such as 'no_repeat_ngram_size' are recommended to get better generations)_

## Training procedure


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1

### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

# pszemraj/gpt-peter-2.7B

- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked here is a notebook for testing in Colab.
    - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
    - the telegram bot code and the model training code can be found in this repository ## Usage in python 

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


load the model into a 'pipeline' object:



generate text!



_(example above for simplicity, but adding generation parameters such as 'no_repeat_ngram_size' are recommended to get better generations)_ ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1 ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #gpt_neo #text-generation #gpt-neo #gpt-peter #chatbot #base_model-EleutherAI/gpt-neo-2.7B #autotrain_compatible #region-us 
 # pszemraj/gpt-peter-2.7B

- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked here is a notebook for testing in Colab.
    - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
    - the telegram bot code and the model training code can be found in this repository ## Usage in python 

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


load the model into a 'pipeline' object:



generate text!



_(example above for simplicity, but adding generation parameters such as 'no_repeat_ngram_size' are recommended to get better generations)_ ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1 ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #gpt_neo #text-generation #gpt-neo #gpt-peter #chatbot #base_model-EleutherAI/gpt-neo-2.7B #autotrain_compatible #region-us 
# pszemraj/gpt-peter-2.7B

- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked here is a notebook for testing in Colab.
    - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
    - the telegram bot code and the model training code can be found in this repository## Usage in python 

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


load the model into a 'pipeline' object:



generate text!



_(example above for simplicity, but adding generation parameters such as 'no_repeat_ngram_size' are recommended to get better generations)_## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6