Supra-1.5-50M-Base-exp is a continued-pretrained 50M parameter Llama-style base
model derived from SupraLabs/Supra-50M-Base. The target update expands the
usable context window from 1,024 tokens to 5,120 tokens using RoPE scaling and
full-weight continued pretraining.
Architecture
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 |
Continued Pretraining Objective
This is CPT, not instruction fine-tuning. Training uses packed raw text with standard causal language-modeling loss:
labels = input_ids- all non-pad tokens are trained
- no response-only masking
- no system/user/assistant masking
- no LoRA adapters in the default run
Data Mix
The current local training mix prepared for this run is:
- 3,000,000,062 CPT tokens
- 30% Tool Calling
- 30% ChatML Conversations
- 25% Factual Text (articles, essays, blogs)
- 15% Math & Logic Questions
Intended Use
Supervised Fine-Tuning (SFT) and Reinforcement Learning
Credit goes to @QyrouNnet-AI