GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
venkat2393
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
venkat2393
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
QLoRA adapter (r=16, α=32, all-linear, NF4) trained on sutradhar-ft-v1 (2,000 synthetic,
record-grounded, code-mixed multi-turn tool-calling conversations; entity-disjoint training
slice). Verdict under the pre-committed DEC-P4-8 rule: CUT — it did not beat the
well-prompted base on the primary metrics (intent accuracy and multi-turn coherence regressed)
despite large gains in tool-call sequence accuracy (0.083 → 0.417) and slot F1 (+0.24).
Published deliberately: knowing when fine-tuning did NOT help is the finding. Full benchmark
(both columns, one GPU window, identical serving), the frozen verdict rule, and the
transcript-level failure analysis live in the Sutradhar repo (docs/BENCHMARKS.md Table 2,
docs/DECISIONS.md DEC-P4-9). Base: google/gemma-4-E4B-it @ fee6332c…; best val loss 0.0502;
TrainConfig hash 0d011802….