GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
SupraLabs
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
SupraLabs
Model Tree
Base
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
The model keeps the original Supra-50M architecture and tokenizer:
| Specification | Value |
|---|---|
| Architecture | LlamaForCausalLM |
| Parameters | ~50M |
| Vocabulary Size | 32,000 |
| Hidden Size | 512 |
| Layers | 12 |
| Attention Heads | 8 |
| KV Heads | 4 |
| Context Length | 5,120 tokens |
| Tokenizer | Original Supra byte-level BPE tokenizer |
This is CPT, not instruction fine-tuning. Training uses packed raw text with standard causal language-modeling loss:
labels = input_idsThe current local training mix prepared for this run is:
Supervised Fine-Tuning (SFT) and Reinforcement Learning