license, language, pipeline_tag, library_name, tags

license	language	pipeline_tag	library_name	tags
apache-2.0	en	text-generation	transformers	supra supra-1.5 llama 50m base continued-pretraining long-context 5k-context Supra Supra-50M chatml

Supra1.5-50M Base (ChatML)

Continued Pretraining • 50M Parameters • 5K Context

Supra-1.5-50M-Base-exp is a continued-pretrained 50M parameter Llama-style base model derived from SupraLabs/Supra-50M-Base. The context window has been expanded from 1,024 to 5,120 tokens using RoPE scaling and full-weight continued pretraining.

This specific repository has been modified to natively support ChatML. The tokenizer and embedding layers have been updated to include <|im_start|> and <|im_end|> as single special tokens.

Architecture & Updates

Specification	Value
Architecture	LlamaForCausalLM
Parameters	~50M
Vocabulary Size	32,002 (Expanded for ChatML)
Hidden Size	512
Layers	12
Attention Heads	8
KV Heads	4
Context Length	5,120 tokens
Tokenizer	BPE tokenizer (ChatML Jinja template applied)
Native EOS Token	`<

Vocabulary Expansion & Initialization

The model's 3B token CPT corpus included raw ChatML text. As a result, the base model originally learned the tags <|im_start|> and <|im_end|> as sequences of 7 separate subwords.

To convert these tags into single tokens without destroying the learned pre-trained representations, we used subword-mean initialization:

1. Extracted the attention embeddings for the original 7-token sequences.
2. Averaged the weights for each sequence.
3. Assigned these mean weights to the new single-token IDs.

This ensures stable loss at the start of fine-tuning and allows inference frameworks to natively stop generation when <|im_end|> is predicted.

Continued Pretraining Objective

This is a base model, not an instruct model. Training used 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 training mix prepared for the CPT run was:

• 3,000,000,062 CPT tokens
  • 30% Tool Calling
  • 30% ChatML Conversations
  • 25% Factual Text (articles, essays, blogs)
  • 15% Math & Logic Questions

Fine-Tuning Guide

This model is intended for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), including reasoning-style (R1) alignments.

You can use standard Hugging Face tools and call tokenizer.apply_chat_template() directly on your datasets.

LoRA Configuration Note: Because the vocabulary size was expanded from 32,000 to 32,002, you must train the embedding layers during fine-tuning so the model can update the new ChatML tokens. Add the following to your PEFT/LoRA configuration:

modules_to_save=["embed_tokens", "lm_head"]