---
library_name: transformers
pipeline_tag: text-generation
tags: [gpt-bert, babylm, remote-code]
license: other
---
# haznitrama/babybabellm-multi_gpu-gpt_bert-eng-main-causal

GPT-BERT style BabyBabyLLM model for language **eng**.

This repository may include both *main* and *EMA* variants.

**Default variant exposed to generic loaders:** `main`

## Variants Available
main

## Files
- model.safetensors (alias of default variant)

## 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
}
```
Tokenizer file: `tokenizer_eng.json`

## Quick Usage
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
model_id = 'haznitrama/babybabellm-multi_gpu-gpt_bert-eng-main-causal'
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)
out = model(**tok('Hello world', return_tensors='pt'))
```

### Causal LM Wrapper
This repo includes a lightweight GPTBertForCausalLM wrapper.
Generation example:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
mid='haznitrama/babybabellm-multi_gpu-gpt_bert-eng-main-causal'
tok=AutoTokenizer.from_pretrained(mid)
model=AutoModelForCausalLM.from_pretrained(mid, trust_remote_code=True)
print(tok.decode(model.generate(**tok('Hello', return_tensors='pt'), max_new_tokens=20)[0], skip_special_tokens=True))
```

## Notes
- Converted on 2025-09-27T15:21:53.977598+00:00
- Weights are the exact trained parameters; no new layers were initialized.
- Requires `trust_remote_code=True` due to custom architecture.