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Cataloged from BananaMind/BananaMind-2-Micro
All models by BananaMind β[Image blocked: BananaMind 2 Micro]
[Image blocked: Base Bench parameter efficiency]
BananaMind 2 Micro is a 2,933,193-parameter base causal language model trained from scratch on 74,998,349,824 tokens. It is a pretrained completion model, not an instruction-tuned chat model.
All results below use the final checkpoint. BananaMind Base Bench 1.1 and ArithMark use raw continuation likelihood rather than generated answers.
ARC, PIQA, HellaSwag, and ArithMark 3 values are acc_norm. ArithMark 2
uses raw accuracy. CMA-1M-Mini was not run on either ArithMark version, so
those cells are intentionally blank.
Base Bench values for GPT-S2-5M and CMA-1M-Mini come from the BananaMind Base Bench leaderboard. GPT-S-5M was not listed there when this card was prepared, so its complete official local Base Bench 1.1 result is shown.
The comparison chart uses raw BananaMind Base Bench 1.1 accuracy and removes the four-choice random baseline before normalizing by model size:
The result is accuracy percentage points above random per 100K parameters. For example, 30% raw accuracy becomes 5 points above random before the size normalization. Higher is better.
This is a narrow parameter-efficiency measurement, not a complete model quality score. The chart can be regenerated with:
The refresh path reads the detached attention output as a signal, combines a learned projection with a strictly causal depthwise convolution, and gates a projection of the original token embedding. Its learned residual scalar was initialized to zero. During cached generation, convolution history is carried alongside each layer's key/value cache.
The exact source revisions and observed source-token totals are preserved in
checkpoint_metadata.json. The tokenizer corpus and revisions are preserved
in tokenizer_training_manifest.json.
This repository contains custom Transformers architecture code. Review
configuration_bananamind2micro.py and modeling_bananamind2micro.py before
enabling trust_remote_code.
f45a0e949494c0e75892f65e0e5245f6060ae303f7a410921a02cd2617cd72c7cfe694da0501edcc6e4ca0747e2cdcdb5d2d14d9Released under the Apache License 2.0.
efficiency = (accuracy - 25.00) / (parameters / 100,000)python make_parameter_efficiency_chart.pyfrom transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "BananaMind/BananaMind-2-Micro"
tokenizer = AutoTokenizer.from_pretrained( model_id, trust_remote_code=True,)model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, dtype="auto",)
inputs = tokenizer("The future of small language models is", return_tensors="pt")outputs = model.generate( **inputs, max_new_tokens=80, do_sample=True, temperature=0.8, top_p=0.95,)print(tokenizer.decode(outputs[0], skip_special_tokens=True))