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On 2026-07-19 Volatile Markets reports achieving 94% on the…

2026-07-19 · 03:30 UTC ·@alexocheema ·1 min read

Brief

Volatile Markets reports on July 19, 2026 that a fine‑tuned GLM 5.2 (LORA-trained) reached 94% on the SeraphimSerapis/tool-eval-bench, up from an 83% GLM 5.2 API baseline. The author credits a custom FP16 Indexer and a 753 GB FP8 setup for the ~10% gain, thanks @alexocheema, @exolabs, @Zai_org and @louszbd, and says GLM 5.2 debugged lines Fable missed.

Why it matters

On 2026-07-19 Volatile Markets reports achieving 94% on the SeraphimSerapis/tool-eval-bench using a fine-tuned GLM 5.2 (LORA training); the post states the standard GLM 5.2 API benchmark was 83% and attributes a ~10% gain to fine-tuning plus a custom FP16 Indexer.

Key details

  • The setup used a 753 GB FP8 configuration and a custom FP16 Indexer; the author thanks @alexocheema, @exolabs, @Zai_org, and @louszbd and claims GLM 5.2 identified and debugged several lines that Fable(s) missed (noting Fables at 100%).
Source evidence

nice setup

Volatile Markets (@volatilemarkts)

Thanks to @alexocheema @exolabs I’ve been able to run a 94% on SeraphimSerapis/tool-eval-bench using
a fine tuned GLM 5.2 model. 753GB FP8 and using a custom FP16 Indexer I’ve been building.
The standard benchmark for the model was 83% with GLM 5.2 api (which is amazing) compared to Fables 100%.. fine tune and the FP16 Indexer added 10%.. largest jump I’ve been able to acquire. GLM 5.2 fine tune and LORA training. @Zai_org @louszbd I am very thankful for your hard work and dedication to open source community. GLM 5.2 continues to amaze me. I’m grateful for the model. The model has identified and debugged several lines fable missed. Which to me is incredible.

— https://nitter.net/volatilemarkts/status/2078663037825831172#m