README.md

metadata

license: mit
datasets:
  - ethanker/nanomind_1m
language:
  - en
library_name: transformers
tags:
  - gpt
  - decoder-only
  - llama
  - tiny
pipeline_tag: text-generation

nanomind-step-002000 (early experiment checkpoint)

This is an early checkpoint (step 2,000) from a small decoder-only GPT-style experiment. It is shared primarily for transparency and to help others reproduce or build upon the setup. This checkpoint is not production-ready.

What this is

• Model: small LLaMA-style decoder-only (RMSNorm, SwiGLU, RoPE, MQA/GQA-compatible)
• Checkpoint: step_002000 from run1
• Data: curated 1M-doc mix (English), hosted at the public dataset repo: ethanker/nanomind_1m
• Intended use: research/experimentation only

How it was trained (run1)

• Script: train_run1.py (included here) with the exact launch command in RUN_COMMAND.txt.
• Key settings used for run1:
  • seq_len 2048, hidden_size 512, n_layers 16, n_heads 8, n_kv_heads 1
  • global_batch_size 64, micro_batch_size 1, AdamW lr 1e-3, warmup 2000
  • bf16 autocast, gradient clipping 1.0

Quick eval snapshot (for context only)

• In-domain ppl (small slice): ~1.06 (expected to be low given early-stage in-domain evaluation)
• Generations: fluent but sometimes regurgitative; this is a very early checkpoint

Optimizations implemented for subsequent runs

These were implemented in the training/data pipeline for future iterations (beyond this checkpoint):

• Near-duplicate filtering (MinHash+LSH) and stronger boilerplate heuristics
• Optional gradient checkpointing and torch.compile for better memory/throughput
• Periodic quick perplexity checks on a small token budget

References:

• Chinchilla compute-optimal scaling: https://arxiv.org/abs/2203.15556
• Deduplication improves LMs: https://arxiv.org/abs/2107.06499
• Dedup mitigates privacy risks: https://arxiv.org/abs/2202.06539
• FlashAttention-3: https://arxiv.org/abs/2407.08608
• YaRN long-context: https://arxiv.org/abs/2309.00071

Load and sample

from transformers import AutoTokenizer, LlamaForCausalLM
import torch
m = 'ethanker/nanomind-step-002000'
tok = AutoTokenizer.from_pretrained(m, use_fast=True)
model = LlamaForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32)
model.eval().to('cuda' if torch.cuda.is_available() else 'cpu')

prompt = "Once upon a time,"
inputs = tok(prompt, return_tensors='pt').to(model.device)
out = model.generate(**inputs, do_sample=True, top_p=0.9, temperature=0.8, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))

Files

• model.safetensors, tokenizer/config files
• train_run1.py (training code snapshot)
• RUN_COMMAND.txt (exact command used)

Notes

• Early and exploratory; expect limited generalization and occasional regurgitation.
• Please prefer the referenced dataset repo and scripts for reproducibility and your own experiments.