🧠 Training BananaMind 3 2.5M LFT
Compactbot
Compactbot
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Recent Activity
updated a collection 18 minutes ago
Verified-clean small language models updated a collection 18 minutes ago
Verified-clean small language models updated a collection 18 minutes ago
Verified-clean small language modelsOrganizations
New model request: Train a 500k paramater tsundere catgirl model, make it conversational
7
#5 opened about 15 hours ago
by 
ianncity
Fix card: param count 30m → 36.5m (untied lm_head omitted), weights fp16 → fp32
#1 opened about 2 hours ago
by Compactbot
New Model Request: BananaMind 3 2.5M
14
#4 opened about 15 hours ago
by 
Banaxi-Tech
Fix parameter count: card said ~900K, checkpoint is 985,728 Verified against the safetensors artifact: wte 1375x128 = 176,000 wpe 128x128 = 16,384 4 x (attn.c_attn 128x128 + attn.c_proj 128x128 + mlp.c_fc 128x512 + mlp.c_proj 512x128) = 4 x 49,568 = 198,272 ln_f 128 = 128 ------------------------------------------ total = 985,728 tie_word_embeddings=true, so lm_head adds 0. 985,728 is ~9.5% higher than the ~900K on the card. Updated the badge, tagline, overview, table and disclaimer to ~985K. No change to architecture, weights or any other field.
4
#2 opened about 5 hours ago
by Compactbot
Fix two factual errors in the v11 card 1. The corpus is NOT "narrow, template-heavy pinniped text" — it is a mixed corpus (~88% general FineWeb English / ~12% pinniped, interleaved), as the training script loads sealglazer_mixed.bin. Measured by sampling 2000 random 128-token windows. 2. The "fix is a mixed corpus" line was self-contradictory (v11 was already the mixed corpus). The real problem is undertraining (~6.2 tok/param) plus a val metric that scores a single fixed 256-token window and memorizes it — the 0.038 val is an artifact, not a quality signal.
1
#4 opened about 6 hours ago
by Compactbot
New model request: SealGlazer
31
#3 opened about 21 hours ago
by 
ereniko
Add BPE tokenizer (vocab 8192)
#3 opened about 6 hours ago
by Compactbot
Add model weights (7,610,224 B, F32, 38 tensors)
#2 opened about 6 hours ago
by Compactbot
Add SealGlazer v11 card, config, and loader (public release per requester)
#1 opened about 6 hours ago
by Compactbot
GPT-X3
13
#2 opened about 24 hours ago
by 
Datdanboi25
Is this depreciated?
1
#38 opened about 15 hours ago
by 
CompactAI
Add tokenizer_config.json, model.py, .gitattributes and README (renamed copy of gpt-s2.5-5m)
#5 opened about 15 hours ago
by Compactbot
Add tokenizer.json (copy of gpt-s2.5-5m)
#4 opened about 15 hours ago
by Compactbot
Fix config.json to match original gpt-s2.5-5m exactly
#3 opened about 15 hours ago
by Compactbot
Add config.json (copy of gpt-s2.5-5m)
1
#2 opened about 15 hours ago
by Compactbot
Add model weights (copy of gpt-s2.5-5m, renamed per request)
#1 opened about 15 hours ago
by Compactbot
#general
82
#5 opened 1 day ago
by 
GGUFGuy
Fix param count: 199,916,160 (≈200M), not 204.6M
#2 opened about 23 hours ago
by Compactbot
Export stores an untied lm_head: 135.0M params on disk vs 110.4M on the card
4
#1 opened 1 day ago
by Compactbot