GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
amildravid4292
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
amildravid4292
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
| prompt | Problem: {q}\nPlease reason step by step, and put your final answer within \boxed{}.\nSolution: |
| data | MATH train, 7,393 problems (MATH-500 excluded), no difficulty filtering |
| reward | binary correctness of the last \boxed{} (math-verify); truncated rollouts get no reward and no gradient |
| algorithm | GRPO, 32 prompts x 16 rollouts per step, T=1.0, no KL, unnormalised advantages, on-policy (1 iteration), DAPO-style token-level loss |
| lr / steps | 1e-5 constant, 300 steps (153,600 rollouts) |
| adapter | LoRA r=64, alpha=128, on q/k/v/o/gate/up/down projections, merged here into bf16 |
| cap | 4,096 completion tokens during training |
| stack | TRL 1.10, vLLM 0.26, transformers 5.15, PEFT 0.20; 8 x A100 80 GB |
Note: the trainer held the frozen base in fp32 during training (TRL's default); the merge adds the LoRA delta to the bf16 base weights.
| step | 300 (this model) |
|---|---|
| pass@1 | 0.767 |
At step 300: commit 0.955, P(correct | committed) 0.803, pass@8 0.904. The base model with a typed
opening in the same harness: " To determine" 0.707, " Alright," 0.799.
AIME 2025 (30 x 32, 16,384-token cap), this model, no prefix: pass@1 0.114, pass@32 0.333.
During training the share of rollouts opening with To rose from 23% to 57%; Alright-type
openings from 0.2% to 1.6%. Every rollout of the run (text, reward, opener) was logged.