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Model: spitfire4794/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
+- chatml
+---
+
+<h1 align="center">Supra1.5-50M Base (ChatML)</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 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 | `<|im_end|>` |
+
+## 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:
+
+```python
+modules_to_save=["embed_tokens", "lm_head"]