README.md

metadata

language:
  - en
tags:
  - nanochat

Small language models trained with nanochat (Assignment 1, Agentic LLMs, Leiden University)

Small language models trained with nanochat on one NVIDIA RTX 2070 SUPER (8 GB), for Assignment 1 of the Agentic LLMs course. Authors: Kevin Bretz and An Nguyen. Code, setup and all commands: https://github.com/kevin-bretz/Small-Language-Model

Folder	Model	Training
checkpoints/d2_pretraining	depth 2, 13.0M parameters	pre-training on ClimbMix, 420 steps, 55M tokens
checkpoints/d4_pretraining	depth 4, 36.7M parameters	pre-training on ClimbMix, 528 steps, 138M tokens
checkpoints/d2_mid, checkpoints/d4_mid		stage 1, mid-training on MMLU + GSM8K, 310 steps
checkpoints/d2_sft, checkpoints/d4_sft		stage 2, SFT on SmolTalk, 500 steps
tokenizer	BPE tokenizer with 32,768 tokens, used by all models	500M characters of ClimbMix
tokenizer_8192	BPE tokenizer with 8,192 tokens (tokenizer comparison only)	500M characters of ClimbMix

Every checkpoint folder contains the weights (model_*.pt), nanochat's metadata (meta_*.json) and the optimizer state (optim_*_rank0.pt).

Using the checkpoints

1. Set up the code and the virtual environment as described in the README of our GitHub repository.

2. Copy the folders of this repository into nanochat's cache folder ~/.cache/nanochat/:

   This repository	~/.cache/nanochat/
   checkpoints/d2_pretraining, checkpoints/d4_pretraining	base_checkpoints/d2, base_checkpoints/d4
   checkpoints/d2_mid, d2_sft, d4_mid, d4_sft	chatsft_checkpoints/d2_mid, d2_sft, d4_mid, d4_sft
   tokenizer	tokenizer and tokenizer_32768 (the same files)
   tokenizer_8192	tokenizer_8192

3. From the repository root, run for example:

   python -m assignment.task3 eval d4     # ARC-Easy, ARC-Challenge and GSM8K after each training stage
   python -m assignment.task4 d4_sft      # our temperature study through nanochat's scripts/chat_cli.py
   

   or chat with a model yourself: python -m scripts.chat_cli -i sft -g d4_sft