Banaxi-Tech/BananaMind-2.1-NanoCoder

BananaMind 2.1 NanoCoder

BananaMind 2.1 NanoCoder is an under-10M-parameter code language model.

• 9,895,690 total parameters
• 7,975,688 Transformer parameters
• 1,920,002 parameters in causal bigram/four-gram memory
• BananaMind 2 Nano's 8,192-token tokenizer
• 8 physical layers and 11 effective passes
• execution: L1 → L2 → L3 → L4 → L5 → L3 → L4 → L5 → L6 → L7 → L8
• the complete L3-L5 middle stack shares weights across its two passes
• 4,096-token context

The n-gram module has independent 29,744-entry bigram and four-gram hash tables, each with 32-dimensional values. Their concatenated representation is projected to the 256-wide residual stream. It is injected through separate learned gates at the beginning of both middle-stack passes.

Training data

The exact 30B-token streamed mixture is:

Source	Tokens	Share
The Stack v3 train	22.5B	75%
FineWeb-Edu	7.5B	25%

Stack v3 is streamed as repository-ordered source files. Vendored files are skipped, while repository path, file path, and detected language are included in the training text. FineWeb-Edu supplies prose, naming, comments, and general language knowledge.

Checkpoints are uploaded every 5% with safetensors, tokenizer files, metrics, pinned dataset revisions, exact source-token accounting, and optimizer state.

Launch

./launch_training_hf_job.sh 4 fresh
./launch_training_hf_job.sh 4 resume
./launch_training_hf_job.sh 8 resume