Text Generation
Transformers
Safetensors
English
gpt2
boris
nmai
125M
text-generation-inference
File size: 2,474 Bytes
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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- HuggingFaceFW/fineweb-edu
- mlfoundations/dclm-baseline-1.0-parquet
tags:
- boris
- nmai
- gpt2
- 125M
---

![Boris](Boris-125M.png)

# Boris-125M

Boris-125M is a 125 million-parameter language model created by New Millennium
Artificial Intelligence (NMAI).

This is a **base (pretrained) model**. It has not been instruction-tuned and does
not follow instructions or hold a conversation — it continues text. For an
instruction-following version, see
[KSP-NMAI/Boris-125M-Instruct](https://huggingface.co/KSP-NMAI/Boris-125M-Instruct).

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("KSP-NMAI/Boris-125M")
model = AutoModelForCausalLM.from_pretrained("KSP-NMAI/Boris-125M")

ids = tok("The ocean is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))
```

## Details

| | |
|---|---|
| Architecture | GPT-2 (pre-LN, learned positional embeddings, tied embeddings) |
| Layers / heads / d_model | 12 / 12 / 768 |
| Context length | 1024 |
| Vocab | 50304 (GPT-NeoX-20B BPE, padded) |
| Tokenizer | `EleutherAI/gpt-neox-20b` |
| Precision | trained in bf16 autocast with fp32 master weights |

## Base model training

Trained on 2.5B tokens for 33:38:48 on one RTX 3060.

| | |
|---|---|
| Final loss | 3.2998 |
| Final grad norm | 0.281 |
| Final learning rate | 6.00e-05 |

![Benchmarks](benchmark.png)

## Limitations

A base model of this size will produce text that is frequently inaccurate,
inconsistent, or offensive. It has received no alignment or safety tuning and
should not be used for factual reference or deployed without supervision.

## Copyright & License

*Copyright 2026 Joseph Jones*

This project and all associated files (the "Work") are licensed under the Apache
License, Version 2.0 (the "License"); you may not use this project except in
compliance with the License. You may obtain a copy of the License at:

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.