GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
malaiwah
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
malaiwah
Model Tree
malaiwah/GLM-5.2-SIQ-Fruit
Input Modalities
Output Modalities
Supported Functionality
Intel Core i7-14700K, 20 Torch threads, BF16, Transformers 5.14.1; 128-token greedy decode after a 16-token warmup:
| measurement | result |
|---|---|
| decode | 33.12 tok/s |
| warm-cache load | 2.51 s |
| resident memory after load | 16.01 GiB RSS |
| peak resident memory | 16.76 GiB RSS |
An earlier card estimated “about 10 GB RAM.” That was not an end-to-end process measurement and has been withdrawn.
python
import torchfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizermodel_id = "malaiwah/GLM-5.2-SIQ-Fruit-bf16"torch.set_num_threads(20)config = AutoConfig.from_pretrained(model_id)assert config.rope_theta == config.rope_parameters["rope_theta"] == 500000.0tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,config=config,dtype=torch.bfloat16,).eval()inputs = tokenizer("Once upon a time", return_tensors="pt")output = model.generate(**inputs, max_new_tokens=40, do_sample=False)print(tokenizer.decode(output[0], skip_special_tokens=True))
Expected output begins with a TinyStories-style continuation about a little girl named Lily; exact wording can vary with library kernels and version.
Revision b97f91d222906142e46827a010a5caf5a9e35928 encoded the trained RoPE
theta incorrectly: it retained 8,000,000 in the nested field and omitted the
legacy top-level field. The 2026-08-07 correction writes 500,000 to both
locations and regenerates MANIFEST.sha256. Weight shards did not change.
The fail-closed assertion in the usage example protects pinned or cached stale
configurations.
The export also carries the same trainer-to-serving RoPE channel permutation
and MTP eh_proj input-half conversion as the SIQ artifact; those
transformations preserve the trained function before stock Transformers drops
the unsupported indexer/MTP modules.
MANIFEST.sha256 authenticates all serving files except the card and Git
attributes. The exporter invocation uses FRUIT_BF16=1; source checkpoint,
training recipe, gauntlet, and review evidence are documented in
proxy-fruit and the
base model card.
MIT; see LICENSE. Earlier model-card metadata incorrectly reported Apache-2.0; the packaged license file has always been MIT.