GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
s1lv3rj1nx
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
s1lv3rj1nx
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| model | accuracy | hard (5-num) | format_rate | avg_tokens (correct) |
|---|---|---|---|---|
| base Qwen2.5-0.5B-Instruct (floor) | 0.33% | 0.00% | 0.00% | 20.0 |
| this model (GRPO, lr 3e-6) | 12.00% | 1.67% | 0.00% | 16.9 |
36x over the base floor (1/300 -> 36/300). Ablations: learning rate was the only lever
that moved accuracy (1e-6 -> 3e-6 gave 1% -> 12%); more steps (3000) and larger group (16)
both plateaued. Known failure mode: reasoning collapse — the shaped reward scores only the
<answer>, so the model emits bare answers with no <think> (format_rate = 0).
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("<your-username>/countdown-qwen2.5-0.5b-grpo-lr3e6")tok = AutoTokenizer.from_pretrained("<your-username>/countdown-qwen2.5-0.5b-grpo-lr3e6")Trained for the RLVR Arena capstone (RL in Production Bootcamp).