Instructions to use N8Programs/BestTerm-440M-Checkpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N8Programs/BestTerm-440M-Checkpts with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="N8Programs/BestTerm-440M-Checkpts")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("N8Programs/BestTerm-440M-Checkpts")
model = AutoModelForCausalLM.from_pretrained("N8Programs/BestTerm-440M-Checkpts", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use N8Programs/BestTerm-440M-Checkpts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N8Programs/BestTerm-440M-Checkpts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N8Programs/BestTerm-440M-Checkpts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/N8Programs/BestTerm-440M-Checkpts

SGLang

How to use N8Programs/BestTerm-440M-Checkpts 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 "N8Programs/BestTerm-440M-Checkpts" \
    --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": "N8Programs/BestTerm-440M-Checkpts",
		"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 "N8Programs/BestTerm-440M-Checkpts" \
        --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": "N8Programs/BestTerm-440M-Checkpts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use N8Programs/BestTerm-440M-Checkpts with Docker Model Runner:
```
docker model run hf.co/N8Programs/BestTerm-440M-Checkpts
```

BestTerm-440M-Checkpts

File size: 1,060 Bytes

38d3937

---
library_name: transformers
base_model: N8Programs/NextTerm-440M
tags:
- qwen3
- oeis
- sequence-modeling
- slerp
---

# BestTerm-440M Checkpoint

Tentative checkpoint for `BestTerm-440M`.

This is a global parameter-vector SLERP between the released `N8Programs/NextTerm-440M` base model and the hot b-file-only continued-pretraining checkpoint at 500M tokens, with interpolation `t=0.80`.

## Quick Scores

Ryskina & Knight sequence completion, exact next-term accuracy:

- Greedy, old PyTorch/Transformers sanity path: `38/57` (`66.67%`).
- Beam search, `num_beams=4`, comma stop and EOS/PAD suppressed: `40/57` (`70.18%`).

Other preservation checks from the same sweep:

- OEIS-Eval-Neo: `6532/19034` (`34.318%`).
- M1 Competition 111 macro MAPE: `17.582548`.
- Polynomial continuation: arithmetic `94.5625%`, quadratic `86.3043%`, cubic `74.5682%`, quartic `67.9524%`.

## Notes

This checkpoint is tentative and was selected as the aggressive Ryskina/M1 point on the base-to-hot500 SLERP sweep. The more conservative preservation point was `t=0.60`.