ProCreations/booper-chat

This model was asked to be published under my account, not the creators. The compute came from https://huggingface.co/posts/ProCreations/855858308074329

Booper chat

A small chat model: the babble / booper transformer, pretrained on English web text, then supervised-fine-tuned on Discord conversations so it replies instead of only continuing prose.

• Base: ProCreations/booper-pretrain (34.1M params, Ultra-FineWeb-L1)
• Post-train data: mookiezi/Discord-Dialogues (ChatML two-author Discord threads)
• This is still a tiny model. It will sound like Discord, and it will be clumsy.

How to prompt it

Training used babble's pair layout, with loss only on the assistant side:

<bos> {user message} <sep> {reply} <eos>

At inference, feed <bos> your message <sep> and sample until <eos>. Multi-turn: put earlier turns in the prompt (newlines), last user message at the end, then <sep>.

Training

Parameters	34,096,128 (8 layers, 512 wide, 8 heads, context 1024)
Tokenizer	same byte-level BPE as the pretrain (16,384 tokens)
Objective	SFT, assistant tokens only
Assistant tokens	150,001,494
Steps	36,017
Hardware	1× NVIDIA H200 (Hugging Face Jobs)
Wall clock	73 minutes (72 min of training at ~34.6k target-tok/s)
Val loss	3.05 → 2.590

Job: ProCreations/6a8a1cc87c5c7dd37923553d

End-of-run samples

Temperature 0.7, top-k 40:

• hey → Hii hru
• what's up → I just got on mate
• lol → I think they both are too small
• can you help me → what level is that

Files

• latest.pt — checkpoint (model, config, optimizer, stage: posttrain-discord)
• tokenizer.json — BPE merges (babble.subword.BPETokenizer.from_json)
• loss.jsonl — val loss, throughput, and samples per checkpoint
• run_meta.json — dataset / LR / batch used for this run

Source architecture and tokenizer scheme: kowo-co/babble.