CoolFace
Modelpublic

0xTank/Kimi-K3-IQ1S-REAP568-64K-4XSPARKS

sourceHugging Faceotherupdated 2mo agoView on Hugging Face
1likes1.8kdownloads
Model Card

Kimi-K3 IQ1_S REAP568 — current 600K four-Spark profile

This repository contains the four-Spark Kimi-K3 IQ1_S derivative currently served as kimi-k3-f16k-600k-u1024. It retains 568 of 896 routed experts per layer using the disclosed deterministic REAP568 selection and is packaged as fourteen GGUF shards.

Current serving profile

SettingValue
Model nameKimi-K3 IQ1_S REAP568 — FP16-K/F16-V 600K uBatch-1024
Context600,000 tokens (n_ctx=600064)
KV cacheK F16, V F16
Logical / physical batch2,048 / 1,024
Parallel slots1
CPU threads16 / batch threads 20
DistributionLocal CUDA plus three RPC workers over RoCE; layer split 1:1:1:1
API aliaskimi-k3-f16k-600k-u1024

The production launcher is `recipes/launch_4x_spark_600k_f16k.sh`. The 64K uBatch-1024 launcher remains available as a lower-context portable profile.

Current measured prefill

A unique natural-language request with cache_prompt=false on the live 600K service measured 3,255 prompt tokens at 75.09 tok/s. A separate cold 1,769-token request measured 61.30 tok/s and 2.65 tok/s for a 16-token decode. These are request-specific measurements; prompt length, graph shape, and cache state materially affect throughput. See `BENCHMARKS.md` for the complete record and the FP16-K comparison.

What is included

  • —The complete REAP568 GGUF checkpoint and tokenizer/configuration files.
  • —The current 600K FP16-K/FP16-V launch recipe.
  • —Checksum and release-verification scripts.
  • —Candidate-only KDA/FlashKDA optimization notes and maintenance procedure.

Expert selection and limitations

The derivative retains 568 of the original 896 routed experts in every Kimi-K3 MoE layer. Attention, KDA, MLA, AttnRes, shared experts, embeddings, latent projections, normalization, and output tensors are unchanged. The public sources did not provide complete per-expert REAP saliency values, so this is a disclosed deterministic routing proxy, not a claim of lossless pruning.

FlashKDA status

FlashKDA is not enabled in the production service. The earlier candidate did not activate the bridge because the RPC workers were using older CUDA/RPC libraries. Bridge-enabled libraries are staged separately on all three ranks; the production workers and API were not interrupted. The maintenance-window procedure and promotion gates are in `recipes/FLASHKDA_CANDIDATE_RUNBOOK.md`.

Validation

bash
sha256sum -c MANIFEST.sha256
python recipes/verify_release.py /path/to/Kimi-K3-UD-IQ1_S-REAP568

Report cold prefill, warm prefix reuse, decode, TTFT, and quality outputs separately. Do not treat throughput alone as an intelligence or safety claim.

Attribution, license, and responsibility

This is an independent derivative. It is not affiliated with or endorsed by Moonshot AI, Unsloth, llama.cpp, NVIDIA, or contributors to those projects. Comply with the upstream Kimi-K3 checkpoint, IQ1_S conversion, and runtime licenses and notices. This release is provided for research and evaluation; the model may produce incorrect, biased, unsafe, or unsuitable content. Validate outputs and use appropriate access controls. This card is not legal advice.

Sources