GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
sabari2005
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
sabari2005
Model Tree
Input Modalities
Output Modalities
Supported Functionality
Decoder-only transformer (Llama-style, loadable with LlamaForCausalLM):
| Parameters | 33,531,264 |
| Hidden dim | 384 |
| Layers | 12 |
| Attention heads | 6 (MHA, head_dim 64) |
| FFN | SwiGLU, inner dim 1024 |
| Norm | RMSNorm (pre-norm), eps 1e-6 |
| Positional | RoPE, θ = 10,000 |
| Context | 4096 |
| Vocab | 32,000 (SentencePiece BPE, byte_fallback) |
| Weight tying | yes |
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("sabari2005/cyberslm-33m-base")model = AutoModelForCausalLM.from_pretrained("sabari2005/cyberslm-33m-base")ids = tok("A SQL injection attack is", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=100, do_sample=True, temperature=0.7, top_p=0.9)print(tok.decode(out[0], skip_special_tokens=True))
Special tokens: pad=0 (<pad>), unk=1 (<unk>), bos=2 (<bos>), eos=3 (<eos>).
Base model — no instruction tuning; it continues text rather than follows instructions. Trained on ~0.5B tokens, so expect limited general knowledge outside the cyber/CS domain. English only.