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Model: SupraLabs/Supra-1.5-50M-base-exp
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- supra
+- supra-1.5
+- llama
+- 50m
+- base
+- continued-pretraining
+- long-context
+- 5k-context
+- Supra
+- Supra-50M
+---
+
+<h1 align="center">Supra1.5-50M Base</h1>
+
+<p align="center">
+  Continued Pretraining • 50M Parameters • 5K Context
+</p>
+
+![Supra-1.5-Base-EXP](https://cdn-uploads.huggingface.co/production/uploads/68a5d0966d33a07f8aad2e51/Lh7KcQs60Ht9iray8WFbp.png)
+
+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