0.1.45-poly.6

A new model, Clover-35B-A3B-Preview, IsValorum's APEX-I NanoPlus files, faster attention at long contexts for Qwen3.6 and the models built on it, and an opt-in router bias toward the experts in VRAM.

What changed since 0.1.45-poly.5

• Clover-35B-A3B-Preview by DiffuSpace, Qwen3.6-35B-A3B trained further to also write a block of tokens at once,
  runs on strata-poly: pick it in START-HERE or run ./setup.sh --family clover. Three sizes, 13 to 21 GB:
  maternion's Q3_K_M (the default), IsValorum's APEX-I-NanoPlus (the least RAM) and maternion's Q4_K_M. On an RTX 4070
  laptop (8 GB) the Q3_K_M writes at 68 to 73 tokens/s and reads a 32,000-token prompt at 1,560 to 1,700. It reads
  text only. Its own block guesses wrote slower than one token at a time even with every expert in VRAM, so they are
  off; STRATA_BLOCK_GUESS=1 turns them on. Its page is
  docs/models/clover.md.
• Files whose experts keep gate and up in two different types, as IsValorum's APEX-I NanoPlus files do, load now
  instead of stopping with "have different types". Flint's NanoPlus file picks llama.cpp's top token at 119 to 124 of
  128 greedy steps. Reported by Aleksisin
  (issue 103).
• Qwen3.6 and the models built on it (Ornith, Tiel Coder, MiniCPM-V, Clover) read prompts through llama.cpp's
  flash-attention kernel, and from 4,096 tokens of context write through a tensor-core decode kernel, on RTX 30
  cards and newer. Both were opt-in before. On an RTX PRO 6000 held to 12 GB at 100K of context, Qwen3.6 read 8,210
  tokens/s instead of 5,837 and wrote 230 instead of 106; on an RTX 4060 laptop at 118K,
  GodrezJr2 measured 1,318 instead of 814 reading and 51.0 instead of 32.1 writing
  (PR 108). Where two tokens are almost equally likely, an answer can
  now part from the one 0.1.45-poly.5 gave. STRATA_FATTN=0 and STRATA_FDEC=0 turn each one off.
• --cache-bias F, off unless set and for those same models only, makes a token that comes a few at a time
  lean toward the experts already in VRAM. At 12 GB with F = 0.5 the top token stayed the same at 121 to 126 of 128
  steps and writing went from 196 to 234 tokens/s. By drakarah
  (PR 104, issue 81).
• Changes that UPDATE.bat or ./update.sh brought before this release: when the context cuts a reply short, the
  thinking still closes in time to answer; tools/histogram_profile.py makes an expert profile from counted routing
  (festeraeb, PR 101); the RX 5700 XT
  (gfx1010) installs as an unvalidated RDNA1 card (firetussin,
  PR 102).

The engine changed, so the zip below is a new ready-made Windows engine. With STRATA_FATTN=0 and STRATA_FDEC=0
it gives 0.1.45-poly.5's greedy tokens with Qwen3.6, LFM2.5, Gemma 4, Ornith, Tiel Coder, MiniCPM-V and Flint on an
RTX 4070 laptop under Windows, and with Qwen3.6 on an RTX PRO 6000 under Linux. With the defaults the short prompts
gave the same tokens and the long ones parted where two tokens were close: on the RTX PRO 6000, after a 4,833-token
prompt, Qwen3.6 picked 0.1.45-poly.5's top token at 124 of 128 steps, and at the other four that token was in its
top three. Clover picks llama.cpp's top token at 125 to 127 of 128 steps on the laptop. An install of 0.1.45-poly.5
updated to it with UPDATE.bat and kept its config as it was. GLM 5.3 Flash, DeepSeek V4 Flash and Kolibri 1 were not
run for this release.

Install

Download the source below, or git clone the repository. Then run START-HERE.bat on Windows or ./setup.sh on
Linux. An earlier install updates with UPDATE.bat or ./update.sh.

Limits

• The eleven added models are experimental.
• The ready-made engine is for Windows and CUDA 13 only; CUDA 12 and AMD cards compile on your PC.
• GLM 5.3 Flash and DeepSeek V4 Flash need 70 to 100 GB of RAM, and about 8 GB more than their files in a container.
  A DeepSeek file larger than the RAM runs several times slower than llama.cpp with the same file.
• GLM 5.3 Flash, DeepSeek V4 Flash, Flint, Kolibri 1 and Clover have not been tried on an AMD card. On Windows, AMD
  cards have run one strata-poly model (Tiel Coder on an RX 9070 XT).

Most of this project is the work of Strata's author and contributors.

Planned: SSD optimization for strata-poly.