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
File size: 3,641 Bytes
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"checkpoint": "checkpoint_tokens_000500020611.pt",
"trainer_state": {
"completed_steps": 20486,
"train_tokens_seen": 500020611,
"last_loss": 0.759232759475708
},
"checkpoint_args": {
"data": "oeis_train_bfile_prefix4096.packed",
"model_backend": "custom",
"param_dtype": "fp32",
"weight_update_mode": "bf16_live_fp32_master",
"batch_size": 1,
"seq_len": 4096,
"steps": 5,
"warmup_steps": 2,
"target_tokens": 754629901,
"max_steps": 0,
"batch_mode": "bucketed",
"pad_to_seq_len": false,
"index_dir": "oeis_train_bfile_prefix4096.packed.idx",
"bucket_tokens_per_batch": 16384,
"bucket_token_budget_spec": "512:32768,*:24576",
"bucket_max_batch_size": 512,
"bucket_pad_multiple": 8,
"bucket_pad_to_upper": true,
"bucket_replacement": false,
"bucket_repeat_epochs": true,
"bucket_sampling": "token_mass",
"vocab_mode": "oeis",
"hidden_size": 1024,
"intermediate_size": 3072,
"num_hidden_layers": 28,
"num_attention_heads": 16,
"num_key_value_heads": 8,
"head_dim": 0,
"compile": true,
"compile_mode": "reduce-overhead",
"compile_dynamic": true,
"compile_skip_dynamic_cudagraphs": true,
"prewarm_bucket_shapes": true,
"prewarm_bucket_passes": 2,
"prewarm_restore_state": true,
"prewarm_verify_restore": false,
"prewarm_update_optimizer": false,
"prewarm_materialize_optimizer_state": true,
"gradient_checkpointing": false,
"native_gqa": true,
"amp": true,
"lr": 0.0003,
"optimizer_mode": "torch_muon_hybrid",
"adamw_lr": 0.0001,
"muon_lr": 0.01,
"body_lr_mult": 1.0,
"adamw_weight_decay": 0.01,
"muon_weight_decay": 0.0,
"muon_momentum": 0.95,
"muon_ns_steps": 5,
"muon_adjust_lr_fn": "",
"no_muon_nesterov": false,
"lr_schedule": "warmup_cosine_cooldown",
"lr_total_tokens": 754629901,
"lr_warmup_tokens": 0,
"lr_warmup_fraction": 0.005,
"lr_decay_end_fraction": 0.95,
"lr_peak_factor": 1.0,
"lr_min_factor": 0.1,
"lr_final_factor": 0.0,
"checkpoint_dir": "checkpoints",
"checkpoint_every_tokens": 250000000,
"checkpoint_every_steps": 0,
"keep_last_checkpoints": 4,
"resume": "",
"init_from_hf_dir": "base_model",
"save_final": true,
"trim_final_batch": true,
"allow_token_overshoot": false,
"val_data": "oeis_val_decontam.jsonl",
"val_format": "jsonl",
"val_index_dir": "",
"val_every_tokens": 250000000,
"val_every_steps": 0,
"val_batches": 128,
"val_batch_size": 32,
"val_max_examples": 0,
"val_max_context_tokens": 4096,
"oeis_eval_data": "",
"oeis_eval_every_tokens": 0,
"oeis_eval_every_steps": 0,
"oeis_eval_batch_size": 64,
"oeis_eval_max_examples": 0,
"oeis_eval_max_new_tokens": 20,
"oeis_eval_max_context_tokens": 0,
"oeis_eval_collect_examples": 3,
"oeis_eval_generation_backend": "legacy",
"expected_loss_tokens": 754629901,
"safe_preflight": true,
"allow_synthetic_only": false,
"allow_replacement_sampling": false,
"preflight_only": false,
"wandb": true,
"wandb_project": "oeis-massive",
"wandb_entity": "n8programs",
"wandb_run_name": "nextterm-440m-bfile-only-1epoch-20260701-gqa-lrpeak100",
"wandb_id": "nextterm440m-bfile-only-1epoch-20260701-gqa-lrpeak100",
"wandb_resume": "allow",
"wandb_mode": "online",
"wandb_anonymous": "",
"wandb_tags": "bfile-only,continued-pretraining,440m,no-synth,invlen-repair,lrpeak100",
"log_every_steps": 10,
"seed": 0,
"report_json": "report.json"
}
}
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