Model Families
1bit auto-detects 552 architecture tokens / 1,774 HF arch strings from GGUF/1BP/safetensors headers — no config files, no per-model glue code. Point the engine at a model and run. Coverage is measured against a full HF census: 317,310 / 317,310 arch-bearing text-gen checkpoints (100.00%) map to an engine token — see the models SSOT.
Every family below has its own page with a full breakdown: parameter sizes, 1BP file size, supported backends, and real measured performance. Numbers trace back to the performance SSOT; model-support detail traces back to wiki/models.md.
Backend legend: 🟢 supported & validated · 🟡 functional, perf pending · 🔴 not yet · 🔬 experimental Pipeline legend: 🧠 LLM · 👁️ vision · 🗣️ voice/TTS · 🧬 EEG · 🏁 end-to-end validated
Featured: Zyphra
The flagship ecosystem — a complete stack from EEG → LLM (dense, MoE, Mamba) → TTS → voice cloning, all running on the same binary.
All families
| Family | Type | Sizes | Page |
|---|---|---|---|
| Zyphra (Zaya1, ZR1, BlackMamba, Zamba/Zamba2) | MoE · SSM · dense | 1.2B–74B | zyphra.md |
| Qwen (Qwen2/2.5/3/3.5/3.6, Qwen-VL) | Dense · MoE · VL | 0.5B–72B | qwen.md |
| Llama (3.1 / 3.2, TinyLlama) | Dense | 1B–8B | llama.md |
| Mistral (Mistral, Ministral, Pixtral) | Dense · VL | 7B–12B | mistral.md |
| Gemma (Gemma 3/4, MedGemma, Embedding-Gemma) | Dense · embedding | 300M–4B | gemma.md |
| Phi (Phi-3 / 3.5 / 4-mini) | Dense | 3.8B | phi.md |
| Falcon (Falcon3) | Dense (parallel attn) | 1B–40B | falcon.md |
| OLMo (OLMo-2) | Dense (LayerNorm) | 7B–13B | olmo.md |
| Granite (Granite-3.2) | Dense | 2B–8B | granite.md |
| SmolLM (SmolLM2) | Dense | 135M–1.7B | smollm.md |
| DeepSeek (V2/V3/R1 + distills) | MoE (MLA) | 8B–671B | deepseek.md |
| GPT-OSS | MoE | 20B | gpt-oss.md |
| Laguna (Poolside) | Dense · MoE | 3B–7B | laguna.md |
| Moonshot Kimi (Moonlight, Kimi-VL) | Gated MLA MoE | 16B (3B active) | kimi.md |
| BitNet / Bonsai (Deepgrove) | Ternary-native (TQ2) | 1.7B–27B | bitnet-bonsai.md |
| Whisper | Speech-to-text | V3 Turbo | whisper.md |
The
Mage-ViT/Mage-VLvision path and Whisper speech path together feed the JARVIS pipeline.
1BP format note
Dense (non-ternary-trained) models use Q4NX 4-bit; only ternary-native checkpoints (BitNet/Bonsai) use TQ2 2-bit. Converting a dense model to TQ2 is quality-destructive — see the 1BP format policy.