Instructions to use KSP-NMAI/Boris-250M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KSP-NMAI/Boris-250M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KSP-NMAI/Boris-250M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KSP-NMAI/Boris-250M") model = AutoModelForCausalLM.from_pretrained("KSP-NMAI/Boris-250M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KSP-NMAI/Boris-250M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KSP-NMAI/Boris-250M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KSP-NMAI/Boris-250M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KSP-NMAI/Boris-250M
- SGLang
How to use KSP-NMAI/Boris-250M with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "KSP-NMAI/Boris-250M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KSP-NMAI/Boris-250M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "KSP-NMAI/Boris-250M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KSP-NMAI/Boris-250M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KSP-NMAI/Boris-250M with Docker Model Runner:
docker model run hf.co/KSP-NMAI/Boris-250M
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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
- 250M
---

# Boris-250M
Boris-250M is a 250 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-250M-Instruct](https://huggingface.co/KSP-NMAI/Boris-250M-Instruct).
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("KSP-NMAI/Boris-250M")
model = AutoModelForCausalLM.from_pretrained("KSP-NMAI/Boris-250M")
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 / 18 / 1152 |
| 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 5.01B tokens for ~105:56:17 on one RTX 3060.
| | |
|---|---|
| Final loss | 3.0693 |
| Final grad norm | 0.225 |
| Final learning rate | 6.00e-05 |

## 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.
|