Text Generation
Transformers
Safetensors
MLX
qwen3
oeis
integer-sequences
causal-lm
text-generation-inference
Instructions to use N8Programs/NextTerm-440M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use N8Programs/NextTerm-440M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="N8Programs/NextTerm-440M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("N8Programs/NextTerm-440M") model = AutoModelForCausalLM.from_pretrained("N8Programs/NextTerm-440M") - MLX
How to use N8Programs/NextTerm-440M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("N8Programs/NextTerm-440M") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use N8Programs/NextTerm-440M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "N8Programs/NextTerm-440M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N8Programs/NextTerm-440M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/N8Programs/NextTerm-440M
- SGLang
How to use N8Programs/NextTerm-440M 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/NextTerm-440M" \ --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/NextTerm-440M", "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/NextTerm-440M" \ --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/NextTerm-440M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use N8Programs/NextTerm-440M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "N8Programs/NextTerm-440M" --prompt "Once upon a time"
- Docker Model Runner
How to use N8Programs/NextTerm-440M with Docker Model Runner:
docker model run hf.co/N8Programs/NextTerm-440M
File size: 4,737 Bytes
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"name": "final_latest",
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"prewarm_verify_restore": true,
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"prewarm_materialize_optimizer_state": true,
"gradient_checkpointing": false,
"native_gqa": true,
"amp": true,
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"val_data": "/root/oeis_val_decontam.jsonl",
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"safe_preflight": true,
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"preflight_only": false,
"wandb": true,
"wandb_project": "oeis-massive",
"wandb_entity": "n8programs",
"wandb_run_name": "oeis-440m-14b-full-20260525_025101",
"wandb_id": "oeis440m14b_20260525_025101",
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"transformers_compatible": true,
"rope_basis": "HF/Qwen split-half RoPE basis",
"source_rope_basis": "custom interleaved even/odd RoPE basis",
"conversion": "q_proj/k_proj rows and q_norm/k_norm weights permuted with run_oeis_nextterm_eval_torch._map_custom_state_to_transformers",
"oeis_vocab": {
"0-9": "digit tokens",
"10": "negative sign",
"11": "term separator",
"12": "BOS",
"13": "EOS",
"14": "PAD",
"15": "reserved"
},
"generation_defaults": {
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"dtype": "bfloat16",
"elapsed_seconds": 7.9373568799346685,
"local_conversion": {
"source": "/Users/natebreslow/Documents/khashabiLab/bigOEIS/hf_downloads/NextTerm-440M-Checkpoints/checkpoints/final_latest",
"conversion": "float tensors downcast from fp32 safetensors to bf16 safetensors for local MLX inference throughput"
}
}
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