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JANGQ-AI/ZAYA1-8B-JANGTQ4
ZAYA1-8B-JANGTQ4 is a text generation model from JANGQ-AI. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
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Downloads · 30 days
62
2% of all-time downloads
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.safetensors5 GB · 99%
How the weights are stored.
U321.8B · 98%
From the Hugging Face model README
Quantized Zyphra/ZAYA1-8B for Apple Silicon runtimes.
| Source | Zyphra/ZAYA1-8B |
| License | Apache-2.0, inherited from upstream |
| Format | JANGTQ4 |
| Modality | text |
| Bundle size | 4.65 GiB |
| Tensor keys | 1965 |
| Expert layout | Pre-stacked zaya_block.experts.switch_mlp |
ZAYA is not a stock mlx_lm architecture. It alternates CCA attention layers and top-1 MoE layers. Use this bundle only with a runtime that implements the ZAYA CCA state contract and the converted pre-stacked expert layout.
Use a vmlx-swift-lm build that includes the ZAYA Swift runtime (Libraries/MLXLLM/Models/Zaya.swift + MLXLMCommon/Cache/ZayaCCACache.swift + BatchEngine/BatchZayaCCACache.swift). The first verified pin is commit b9da180 or newer.
conv_state [B,1280,2] and prev_hs [B,2048]rope_theta=50000004-bit MXTQ routed experts + 8-bit affine non-routed tensors.
Passthrough floor for first release prep:
conv_qk.*, temp, norms, residual scaling, router path, biases, and balancing biases are preserved as float tensors.lm_head use 8-bit affine in the prepared bundles.jangtq_runtime.safetensors is included: true.mxtq_bits:
{
"routed_expert": 4,
"attention": 8,
"router": 16,
"embed_tokens": 8,
"lm_head": 8,
"cca_conv": 16,
"norms_residual": 16
}
local_experts removal.switch_mlp layout.family=zaya, supports_thinking=False, tool_parser=zaya_xml.이 번들은 Zyphra/ZAYA1-8B를 Apple Silicon MLX/JANG 런타임용으로 양자화한 모델입니다. ZAYA의 CCA attention 상태와 MoE 라우팅을 정확히 구현한 런타임에서만 사용해야 합니다.
config.json carries weight_format=mxtq, zaya_expert_layout=split_switch_mlp.jang_config.json carries cache_subtype=zaya_cca.chat_template.jinja are preserved from the upstream source snapshot.