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v0.9.0
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Release notes, v0.9.0

TandemLLM 0.9.0 on GitHub, published 2026-10-14. The changelog chapter of this version: Changelog summary; the full file: CHANGELOG.md.

TandemLLM 0.9.0 adds a third model, Liquid AI's LFM2.5-24B-A2B, beside Qwen3.8-27B and Qwen3.8-Flash-Next. The Qwen models write the same text at the same speed as in 0.8.3. Every change is in CHANGELOG.md, and the PDF below is the manual.

What's new

  • LFM2.5-24B-A2B runs on the engine: 24B parameters, 4 of 64 experts a token, a 131,072-token context and three slots. The decode step, the verify block and the commit each run as one CUDA graph.
  • Seven LFM2.5 profiles: Liquid's BF16 checkpoint and six EXL3 packs from 2.5 to 6.0 bits a weight. The default is lfm25-exl3-3.5, the fastest in chat whose top token matches BF16's on at least 85 % of chat positions.
  • The lookup drafts with no draft head. Edit and copy asks run 1.8 to 3.6 times faster, and the text equals plain decoding.
  • The shared EXL3 kernels read half-integer rates (1.5, 2.5 and 3.5 bits a weight), as exllamav3 1.6.0 writes them below 4 bits. At whole rates they give the same bits as before.
  • The conductor, Laya and the narrator are on for LFM2.5, fitted to the model. On 2.5, four short asks used 2,234 reasoning tokens, against 2,155 with no conductor and 9,098 with the Qwen router as written.
  • Chat tab: the plus menu is no longer cut off, a page that a tool wrote opens from its tool card, and the Context panel counts every tool call, file, package, KV row and the resident prefix, or says why a figure is missing.
  • Fixes: /v1/completions with a text prompt and two or more slots no longer fails with 500, and a pack stored as one model.safetensors counts as complete.

LFM2.5-24B-A2B profiles

One DGX Spark, 2026-10-14. Chat: one greedy request through the server, 512 tokens, thinking off, plain decoding. Top-1: how often the top token matches BF16's, from exllamav3's model_diff on oasst1 chat.

profile download chat, tok/s chat top-1
lfm25-exl3-2.5 8.20 GB 185.4 78.9 %
lfm25-exl3-3.0 9.68 GB 171.3 83.6 %
lfm25-exl3-3.5 (default) 11.16 GB 150.9 86.7 %
lfm25-exl3-4.0 12.64 GB 143.7 88.5 %
lfm25-exl3-4.5 14.12 GB 128.2 91.2 %
lfm25-exl3-6.0 18.63 GB 105.2 93.4 %
lfm25-bf16 47.9 GB 44.5 100 %

The six packs are one Hugging Face collection: TandemLLM LFM2.5-24B-A2B EXL3. They are public, and a download needs no token.

Install

On a new DGX Spark:

curl -fsSLO https://raw.githubusercontent.com/0xBakeer/TandemLLM/v0.9.0/install.sh
bash install.sh --setup --yes
~/TandemLLM/run/ops/use-profile.sh lfm25-exl3-3.5 --dry-run   # each file's URL and size
~/TandemLLM/run/ops/use-profile.sh lfm25-exl3-3.5

To upgrade a box you installed with --setup, check out 0.9.0 and switch to your profile again. Its files are on disk already, so nothing downloads twice:

curl -fsSLO https://raw.githubusercontent.com/0xBakeer/TandemLLM/v0.9.0/install.sh
bash install.sh --setup --force-setup --yes
~/TandemLLM/run/ops/use-profile.sh <profile>

A box installed without --setup upgrades with bash install.sh --yes and the same --dir. Saved Qwen conversations carry over.

Known limits

  • LFM2.5 has no draft head yet. Liquid has not published its drafter, so the lookup drafts alone.
  • The shadow reader is off on LFM2.5, because it reads Qwen's token ids.
  • The KV pool is off on LFM2.5 and not verified there. Each slot reserves its window at load.
  • On LFM2.5 the conductor adds only the class's core skill. General packages, effort sentences and memory notes stay out until they can go somewhere other than the end of the user's message.
  • Laya stays below its 0.8 confidence bar on many short chats. Those turns run lean, at the profile's default level.
  • At the same bit rate the 24B packs lose more to BF16 (KL) than the 8B sibling does. The cause is not known.
  • Carried over from 0.8.3: the Chat tab is in beta, there is no video input, and the Flash-Next 5.05 pack does not load yet.

Credits

  • Liquid AI for LFM2.5-24B-A2B. The packs are quantizations of it and carry its license, the LFM Open License v1.0.
  • turboderp for exllamav3, which made the packs. Our EXL3 decode matches its LinearEXL3 bit for bit.

Files of the release