
# pszemraj/opt-peter-2.7B

 <a href="https://colab.research.google.com/gist/pszemraj/4068382a40bbf7aab50638b062bd97a9/opt-peter-2-7b-example-csearch-generation.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) on about 80k WhatsApp/text messages (mine). Please use responsibly :)

Test it out on Google Colab by clicking the button above.

![chatdemo](https://i.imgur.com/1EgQYat.png)

## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters 
- you can create your own digital clone and deploy it leveraging [this repository I am working on](https://github.com/pszemraj/ai-msgbot).

### sharded checkpoint

As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available [here](https://huggingface.co/pszemraj/opt-peter-2.7B-sharded).

The `pszemraj/opt-peter-2.7B-sharded` model can be used as a drop-in replacement for this one for all use cases.

## Intended uses & limitations

> The base model has a custom license that propagates to this one. **Most importantly, it cannot be used commercially**. Read more here: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) 

- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 GB, Colab notebook linked above.
  - alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation
- **any statements or claims made by this model do not reflect actual claims/statements by me.** Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs.

## Training and evaluation data

WhatsApp & iMessage data were parsed using [ai-msgbot](https://github.com/pszemraj/ai-msgbot) and then fed as a text dataset to the HF trainer.

## Training procedure

### Training hyperparameters

**SESSION ONE**

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

**SESSION TWO**

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 4


### Framework versions

- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
{"license": ["other", "apache-2.0"], "library_name": "transformers", "tags": ["generated_from_trainer", "text-generation", "OPT", "non-commercial", "dialogue", "chatbot", "ai-msgbot"], "pipeline_tag": "text-generation", "widget": [{"text": "If you could live anywhere, where would it be? peter szemraj:", "example_title": "live anywhere"}, {"text": "What would you sing at Karaoke night? peter szemraj:", "example_title": "Karaoke"}, {"text": "If you could hire someone to help you, would it be with cleaning, cooking, or yard work? peter szemraj:", "example_title": "help"}, {"text": "What form of public transportation do you prefer? (air, boat, train, bus, car, etc.) peter szemraj:", "example_title": "transportation"}, {"text": "What's your favorite zoo animal? peter szemraj:", "example_title": "animal"}, {"text": "Do you like or dislike surprises? Why or why not? peter szemraj:", "example_title": "surprises"}, {"text": "What celebrity would you like to meet at Starbucks for a cup of coffee? peter szemraj:", "example_title": "celebrity "}], "base_model": "facebook/opt-2.7b"}
transformers pytorch opt text-generation generated_from_trainer OPT non-commercial dialogue chatbot ai-msgbot base_model:facebook/opt-2.7b doi:10.57967/hf/0233 license:other license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #OPT #non-commercial #dialogue #chatbot #ai-msgbot #base_model-facebook/opt-2.7b #doi-10.57967/hf/0233 #license-other #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# pszemraj/opt-peter-2.7B

 <a href="URL
  <img src="URL alt="Open In Colab"/>
</a>

This model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)

Test it out on Google Colab by clicking the button above.

!chatdemo

## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters 
- you can create your own digital clone and deploy it leveraging this repository I am working on.

### sharded checkpoint

As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available here.

The 'pszemraj/opt-peter-2.7B-sharded' model can be used as a drop-in replacement for this one for all use cases.

## Intended uses & limitations

> The base model has a custom license that propagates to this one. Most importantly, it cannot be used commercially. Read more here: facebook/opt-2.7b 

- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 GB, Colab notebook linked above.
  - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
- any statements or claims made by this model do not reflect actual claims/statements by me. Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs.

## Training and evaluation data

WhatsApp & iMessage data were parsed using ai-msgbot and then fed as a text dataset to the HF trainer.

## Training procedure

### Training hyperparameters

SESSION ONE

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

SESSION TWO

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 4


### Framework versions

- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
# pszemraj/opt-peter-2.7B

 <a href="URL
  <img src="URL alt="Open In Colab"/>
</a>

This model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)

Test it out on Google Colab by clicking the button above.

!chatdemo ## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters 
- you can create your own digital clone and deploy it leveraging this repository I am working on. ### sharded checkpoint

As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available here.

The 'pszemraj/opt-peter-2.7B-sharded' model can be used as a drop-in replacement for this one for all use cases. ## Intended uses & limitations

> The base model has a custom license that propagates to this one. Most importantly, it cannot be used commercially. Read more here: facebook/opt-2.7b 

- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 GB, Colab notebook linked above.
  - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
- any statements or claims made by this model do not reflect actual claims/statements by me. Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs. ## Training and evaluation data

WhatsApp & iMessage data were parsed using ai-msgbot and then fed as a text dataset to the HF trainer. ## Training procedure ### Training hyperparameters

SESSION ONE

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

SESSION TWO

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 4 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #OPT #non-commercial #dialogue #chatbot #ai-msgbot #base_model-facebook/opt-2.7b #doi-10.57967/hf/0233 #license-other #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # pszemraj/opt-peter-2.7B

 <a href="URL
  <img src="URL alt="Open In Colab"/>
</a>

This model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)

Test it out on Google Colab by clicking the button above.

!chatdemo ## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters 
- you can create your own digital clone and deploy it leveraging this repository I am working on. ### sharded checkpoint

As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available here.

The 'pszemraj/opt-peter-2.7B-sharded' model can be used as a drop-in replacement for this one for all use cases. ## Intended uses & limitations

> The base model has a custom license that propagates to this one. Most importantly, it cannot be used commercially. Read more here: facebook/opt-2.7b 

- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 GB, Colab notebook linked above.
  - alternatively, you can message a bot on telegram where I test LLMs for dialogue generation
- any statements or claims made by this model do not reflect actual claims/statements by me. Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs. ## Training and evaluation data

WhatsApp & iMessage data were parsed using ai-msgbot and then fed as a text dataset to the HF trainer. ## Training procedure ### Training hyperparameters

SESSION ONE

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

SESSION TWO

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 4 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #OPT #non-commercial #dialogue #chatbot #ai-msgbot #base_model-facebook/opt-2.7b #doi-10.57967/hf/0233 #license-other #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# pszemraj/opt-peter-2.7B

 <a href="URL
  <img src="URL alt="Open In Colab"/>
</a>

This model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)

Test it out on Google Colab by clicking the button above.

!chatdemo## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications
- Seems to do a lot better than GPT-Neo with similar training parameters 
- you can create your own digital clone and deploy it leveraging this repository I am working on.### sharded checkpoint

As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available here.

The 'pszemraj/opt-peter-2.7B-sharded' model can be used as a drop-in replacement for this one for all use cases.