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---
library_name: transformers
pipeline_tag: fill-mask
tags: [gpt-bert, babylm, remote-code]
license: other
---
# jumelet/gptbert-pol-250steps-base
GPT-BERT style BabyBabyLLM model for language **pol**.
This repository may include both *main* and *EMA* variants.
**Default variant exposed to generic loaders:** `ema`
## Variants Available
ema, main
## Files
- model.safetensors (alias of default variant)
- model_ema.safetensors
- pytorch_model.bin (legacy PyTorch format)
- pol-2gpu-250steps.bin (raw training checkpoint)
- pol-2gpu-250steps_ema.bin (raw training checkpoint)
## Configuration
```json
{
"attention_probs_dropout_prob": 0.1,
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"intermediate_size": 2560,
"max_position_embeddings": 512,
"position_bucket_size": 32,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"vocab_size": 16384,
"layer_norm_eps": 1e-05,
"force_causal_mask": true,
"classifier_dropout": 0.1,
"classifier_layer_norm_eps": 1e-05,
"num_labels": 2
}
```
Tokenizer file: `tokenizer_pol_vs16384.json`
## Quick Usage
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
model_id = 'jumelet/gptbert-pol-250steps-base'
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)
out = model(**tok('Hello world', return_tensors='pt'))
```
### Forced Causal Attention
Causal attention is enforced during inference by applying a triangular future mask inside the remote code.
This prevents the hybrid GPT-BERT layers from attending to future tokens even when a bidirectional mask is provided.
### Sequence Classification
`GPTBertForSequenceClassification` mirrors the original GLUE classifier head for downstream fine-tuning.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = 'jumelet/gptbert-pol-250steps-base'
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id, trust_remote_code=True)
outputs = model(**tok('This movie was great!', return_tensors='pt'))
print(outputs.logits)
```
## Notes
- Converted on 2025-10-07T01:14:48.240581+00:00
- Weights are the exact trained parameters; no new layers were initialized.
- Requires `trust_remote_code=True` due to custom architecture.
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