
# **UPDATE (2023-09-23):**
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. [See here](https://huggingface.co/Crataco/ggml-ai-dungeon-2-classic).
***
# AID-Neo-125M

## Model description
This model was inspired by -- and finetuned on the same dataset of -- [KoboldAI's GPT-Neo-125M-AID (Mia) model](https://huggingface.co/KoboldAI/GPT-Neo-125M-AID): the AI Dungeon dataset (`text_adventures.txt`). This was to fix a possible oversight in the original model, which was trained with [an unfortunate bug](https://github.com/EricFillion/happy-transformer/issues/283). You could technically consider it a "retraining" of the same model using different software.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
{"language": "en", "license": "mit", "pipeline_tag": "text-generation"}
transformers pytorch safetensors gpt_neo text-generation en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# UPDATE (2023-09-23):
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.
*
# AID-Neo-125M

## Model description
This model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model: the AI Dungeon dataset ('text_adventures.txt'). This was to fix a possible oversight in the original model, which was trained with an unfortunate bug. You could technically consider it a "retraining" of the same model using different software.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
# UPDATE (2023-09-23):
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.
* # AID-Neo-125M ## Model description
This model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model: the AI Dungeon dataset ('text_adventures.txt'). This was to fix a possible oversight in the original model, which was trained with an unfortunate bug. You could technically consider it a "retraining" of the same model using different software. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # UPDATE (2023-09-23):
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.
* # AID-Neo-125M ## Model description
This model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model: the AI Dungeon dataset ('text_adventures.txt'). This was to fix a possible oversight in the original model, which was trained with an unfortunate bug. You could technically consider it a "retraining" of the same model using different software. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
passage: TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# UPDATE (2023-09-23):
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.
*# AID-Neo-125M## Model description
This model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model: the AI Dungeon dataset ('text_adventures.txt'). This was to fix a possible oversight in the original model, which was trained with an unfortunate bug. You could technically consider it a "retraining" of the same model using different software.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0