A 100M-parameter base model trained from scratch on 29.6B tokens, as a plain Llama (LlamaForCausalLM): no custom code. It is TinyBrainBot-100M-v3-Base continued for 4.6B tokens with knowledge-dense data, and it beats it on every Open SLM Leaderboard task, without any benchmark training data.
ArithMark-3 is kept honest: on 1,380 held-out arithmetic problems in topics and wordings the model never saw, it scores 40.7, close to its public 47.0.
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v5.1-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v5.1-base")
ids = tok("The water cycle begins when the sun heats water in oceans and lakes, causing it to", return_tensors="pt")
print(tok.decode(model.generate(**ids, max_new_tokens=24, do_sample=False)[0]))
GGUF files for llama.cpp, LM Studio, Ollama and Jan: F16 (201 MB), Q8_0 (107 MB) and Q4_K_M (66 MB), with add_space_prefix=false set so they tokenize exactly like transformers (greedy output matches).
This is a base model: it continues text and does not chat.
29.6B tokens: the 25.06B of 100M-v3-Base, then 4.56B tokens in five stages, each re-warming the learning rate (peak 3e-4 to 6e-4) and decaying it again. The published weights are the final step of the last stage (step 113,000), not a merge.
Grade-school science Q&A. The final stage includes about 420k grade-school science questions with short explained answers, written by Ling-3.0-Flash: no answer options, and any item sharing an 8-word phrase with an ARC question was removed before training.
No benchmark train or test data was used in the continuation.
A 100M base model: good at short factual and commonsense continuations for its size, but open-ended generation is limited and can be wrong. Not aligned or safety-tuned. Its chat version is TinyBrainBot-100M-v5.1-Instruct.
Files
11 files · 579 MBWeights 66.2 MB (smallest of 3 GGUF variants). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v5-base", revision="18b1fe07e20e39da1091be448b44a227b397fe57")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v5-base", revision="18b1fe07e20e39da1091be448b44a227b397fe57", device_map="auto")
llama.cppDownload one GGUF variant (tinybrainbot-100m-v5.1-base-Q4_K_M, 66.2 MB), then serve it.
