| Model Name | Parameters | Class | Ratio | Tokens | Batch Size (Tokens) | Training Loss ↓ |
| --- | --- | --- | --- | --- |  --- |  --- |
| [GerbilLab/GerbilBlender-A-32m](https://hf.co/GerbilLab/GerbilBlender-A-32m) | 32m | A-Class | 20 | 640M | 262K | 4.127 |


"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models include:

```
'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>'

# Example fill in the middle
'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>'

# Example causal language modelling
'<causal> this is an example text for causal language modelling <|endoftext|>'

# Example masked language modelling
'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>'

```
{"license": "apache-2.0"}
transformers pytorch safetensors gptj text-generation license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #gptj #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models include:



TAGS
#transformers #pytorch #safetensors #gptj #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #safetensors #gptj #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
