

# pszemraj/opt-peter-1.3B

This model is a fine-tuned version of [pszemraj/opt-peter-1.3B-1E](https://huggingface.co/pszemraj/opt-peter-1.3B-1E) on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235

## 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 

## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- **any statements or claims made by this model do not reflect actual claims/statements by me**


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.01
- num_epochs: 2

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer", "text-generation", "opt", "non-commercial", "dialogue", "chatbot"], "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 "}], "inference": {"parameters": {"min_length": 2, "max_length": 64, "length_penalty": 0.7, "temperature": 0.3, "no_repeat_ngram_size": 2, "top_k": 20, "do_sample": true, "repetition_penalty": 4.5}}}
transformers pytorch tensorboard opt text-generation generated_from_trainer non-commercial dialogue chatbot license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #non-commercial #dialogue #chatbot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# pszemraj/opt-peter-1.3B

This model is a fine-tuned version of pszemraj/opt-peter-1.3B-1E on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235

## 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 

## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- any statements or claims made by this model do not reflect actual claims/statements by me


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.01
- num_epochs: 2

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

# pszemraj/opt-peter-1.3B

This model is a fine-tuned version of pszemraj/opt-peter-1.3B-1E on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235 ## 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 ## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- any statements or claims made by this model do not reflect actual claims/statements by me ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.01
- num_epochs: 2 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #non-commercial #dialogue #chatbot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # pszemraj/opt-peter-1.3B

This model is a fine-tuned version of pszemraj/opt-peter-1.3B-1E on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235 ## 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 ## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- any statements or claims made by this model do not reflect actual claims/statements by me ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.01
- num_epochs: 2 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #non-commercial #dialogue #chatbot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# pszemraj/opt-peter-1.3B

This model is a fine-tuned version of pszemraj/opt-peter-1.3B-1E on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235## 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## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- any statements or claims made by this model do not reflect actual claims/statements by me## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.01
- num_epochs: 2### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1