Calandracas/minnow-pico-50M-base

minnow-pico-50M-base

A 48.5M parameter DeepSeek-V4 architecture model trained from scratch on a multilingual blend of C4 data (108 languages). This is a research sandbox model for studying the DeepSeek-V4 architecture at small scale — it is not a production model and has no practical use.

Architecture

Based on the DeepSeek-V4 architecture, consumed via HuggingFace DeepseekV4ForCausalLM (transformers ≥ 5.14):

• —256-dim hidden, 8 layers, 4 heads × 64 head_dim
• —16 experts, top-2 routing (Sqrt(Softplus) affinity)
• —Hash-routed MoE on first 3 layers, learned routing on last 2
• —Hybrid attention (CSA + HCA + sliding)
• —Manifold-Constrained Hyper-Connections (mHC)
• —Grouped low-rank attention output projection
• —48,538,724 parameters total

Training

• —Data: 1B tokens from a multilingual C4 blend (108 languages)
• —Optimizer: AdamW (betas 0.9, 0.95)
• —Learning rate: 6e-4, cosine schedule, 763 warmup steps
• —Batch size: 131,072 tokens/step (2× RX 7900 XTX, NCCL DDP)
• —Steps: 7,629
• —Final loss: 3.49
• —Hardware: 2× AMD RX 7900 XTX (gfx1100, RDNA3)

This model was trained from scratch (random initialization) — it does not inherit weights from any pretrained model.

Tokenizer

Uses the DeepSeek-V4-Flash-0731 tokenizer (vocab 129,280).

Disclaimer

This model exists purely for architectural experimentation. At 48.5M parameters, it cannot perform any useful tasks. Do not use it for anything.