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: 619 Bytes

6c2b324

Benchmark,NextTerm-440M control,BestTerm original sweep,BestTerm saved Alice reproduction
Ryskina greedy,30/57 (52.63%),38/57 (66.67%),38/57 (66.67%)
Ryskina beam-4,32/57 (56.14%),40/57 (70.18%),"40/57 (70.18%), separate teacher sweep"
OEIS-Eval-Neo,6555/19034 (34.4384%),6532/19034 (34.3175%),6536/19034 (34.3386%)
M1 macro MAPE (lower better),17.623927,17.582548,17.813680
Polynomial arithmetic,94.2917%,94.5625%,94.5417% (4538/4800)
Polynomial quadratic,86.3696%,86.3043%,86.2174% (3966/4600)
Polynomial cubic,74.8409%,74.5682%,74.5682% (3281/4400)
Polynomial quartic,68.1190%,67.9524%,68.1905% (2864/4200)