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

Hanuman2/Supra-1.5-50M-Base-exp

Supra1.5-50M Base

Architecture

Continued Pretraining Objective

Data Mix

Intended Use

Hanuman2/
Supra-1.5-50M-Base-exp