Text Generation
Transformers
Safetensors
qwen3
oeis
integer-sequences
sequence-modeling
slerp
text-generation-inference
Instructions to use N8Programs/BestTerm-440M-Checkpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # 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
Download benchmarks/provenance.json from N8Programs/BestTerm-440M-Checkpts: direct link, hf CLI and curl.
- Browser
- Download file 2.43 kB
-
https://huggingface.co/N8Programs/BestTerm-440M-Checkpts/resolve/main/benchmarks/provenance.json
- Command line
-
hf download hf://N8Programs/BestTerm-440M-Checkpts/benchmarks/provenance.json
-
curl -L -o provenance.json https://huggingface.co/N8Programs/BestTerm-440M-Checkpts/resolve/main/benchmarks/provenance.json
2.43 kB
| { | |
| "recovered": "2026-09-25", | |
| "experiments": "July 2026", | |
| "normalization": "Absolute machine-specific paths in copied JSON summaries replaced by their basenames. Metrics and evaluation settings preserved.", | |
| "weights_sha256": "a6a4244107d621c7138060d37168345b9e83a2e8036ccf4ccbcd465b048068f2", | |
| "sources": [ | |
| { | |
| "published_file": "original_ryskina.json", | |
| "source_filename": "ryskina_hf_transformers_generate_beam_summary.json", | |
| "original_source_sha256": "98bfad6b14b801f85f810af9f5a43b1bb3548a8a79ec074ec850ed8933ade53e" | |
| }, | |
| { | |
| "published_file": "original_slerp_sweep.json", | |
| "source_filename": "full_candidate_sweep_summary.json", | |
| "original_source_sha256": "6b42dca5944cb49f52193b328340d4723235292574f8a08cb838afc7cca01c73" | |
| }, | |
| { | |
| "published_file": "original_base_oeis.json", | |
| "source_filename": "base_oeis_eval_neo_mlx_full_b512_summary.json", | |
| "original_source_sha256": "93524fa7150d062f1eeb0003e0beb358955824aa0190a7440bea3822716e9e72" | |
| }, | |
| { | |
| "published_file": "original_base_m1.json", | |
| "source_filename": "base_m1_competition111_mlx_b64_summary.json", | |
| "original_source_sha256": "c8ae6aa834dc1a7855aaaa197ae9d7a3e00bdf4f8b7ae88e84414313ef88aa02" | |
| }, | |
| { | |
| "published_file": "original_base_polynomials.json", | |
| "source_filename": "base_polynomial_continuation_mlx_b128_summary.json", | |
| "original_source_sha256": "ec060c99c7468919bb862ee48153e9b7497d7b92abb7270fb891bc47b54c685e" | |
| }, | |
| { | |
| "published_file": "alice_reproduction.json", | |
| "source_filename": "bestterm_repro_summary.json", | |
| "original_source_sha256": "d3336fde463c4aedbef6772a6d0f7537ad951e6bbee7bf8875aff3079a24b072" | |
| }, | |
| { | |
| "published_file": "alice_m1.json", | |
| "source_filename": "bestterm_m1_competition111_mlx_b64_clean_summary.json", | |
| "original_source_sha256": "bde3be1a84083623cbff0fbdd8ac7cc17ea0cd5a5dc87484b5e629a2756aa2dc" | |
| }, | |
| { | |
| "published_file": "alice_oeis.json", | |
| "source_filename": "bestterm_oeis_eval_neo_mlx_b32_summary.json", | |
| "original_source_sha256": "3bff5e3eab2100139a8a903732f255fdc32c7841802805e5e13eb5b71d531905" | |
| }, | |
| { | |
| "published_file": "alice_polynomials.json", | |
| "source_filename": "bestterm_polynomial_continuation_mlx_b128_summary.json", | |
| "original_source_sha256": "c389efedefc295d3d13c5c1473a435b558742173806982ecb03034279073cf58" | |
| } | |
| ] | |
| } | |