RMDWLLC/Jah-5.0

Jah 5.0

Jah 5.0 is the model behind Kiyomi, RMDW's private AI platform. These are the exact weights serving Kiyomi customers right now. Open, downloadable, verifiable. Don't take our word for what powers your work: inspect it.

Same promise as every Jah release: open weights, no secrets.

What's new in 5.0

• A 12x jump in knowledge capacity over Jah 4.0. 321B parameters with 18B active per token. Only a fraction of the model wakes for each token, so it streams fast for its size.
• Thinking on demand. Ask for deep reasoning and it thinks before it answers, in a cleanly separated reasoning channel. Ask plain and it answers instantly.
• Perfect run on our hardest internal suite with the orchestrated engine, and it solves problems single-shot that Jah 4.0 could not solve at any setting.
• Vision. Send a screenshot, an invoice, a whiteboard photo. Jah reads it like text.
• Native 1M token context. 1,048,576 positions, no scaling tricks.

Specs

Parameters	321B total, 18B active per token
Attention	Hybrid: linear attention + sparse attention, 45 layers
Context	1,048,576 tokens native
Precision	FP8 (block quantized), BF16 sensitive paths
Modalities	Text + vision in, text out
Tool calling	Native
License	MIT

Running it

Full precision wants roughly 330GB of GPU memory plus KV cache. A 4x RTX PRO 6000 class rig (384GB) serves it comfortably; that is exactly what Kiyomi runs on. Community 4-bit quantizations bring it to a 512GB Mac Studio.

Serve with SGLang (a current build with this architecture's day-0 support):

python3 -m sglang.launch_server \
  --model-path RMDWLLC/Jah-5.0 \
  --tp 4 --mem-fraction-static 0.85 \
  --context-length 1048576 \
  --kv-cache-dtype bfloat16 \
  --reasoning-parser glm45 --tool-call-parser glm47 \
  --enable-multimodal --trust-remote-code

On workstation Blackwell GPUs (RTX PRO 6000 class, SM120) add --attention-backend triton and disable DeepGEMM (SGLANG_ENABLE_JIT_DEEPGEMM=0 SGLANG_ENABLE_DEEP_GEMM=0 SGLANG_OPT_DEEPGEMM_HC_PRENORM=false); datacenter kernels for this architecture have not landed for SM120 yet.

Recommended sampling: temperature=1.0, top_p=0.95 with thinking on; temperature=0.6 for instant mode.

Thinking off (instant mode): pre-close the reasoning block by prefilling the assistant turn with <think></think> and continuing the final message. Thinking on is the default.

No rack at home?

That's the API: the same Jah 5.0, on our hardware, one line away. Keys at kiyomibot.ai.

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Built and served by RMDW, Atlanta. Private by physics: your data never leaves the machine.