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
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
- mlfoundations/dclm-baseline-1.0-parquet
language:
- en
tags:
- boris
- nmai
- 125M
Boris-125M
Boris-125M is a Large Language Model created by New Millennium Artificial Intelligence, trained on 2.5B tokens and contains 125 million parameters.
Trained for 33:38:48 on one RTX 3060.
Final Stats:
Loss - 3.2998
Grad Norm - 0.281
Learning Rate - 6.00e-05
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; not instruction-tuned or aligned. Outputs may be inaccurate or offensive.
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.

