Model reference · open weights

Supra1.5-exp

LLMs SupraLabs Text gen 1 build Open weights 649 dl/mo

Supra1.5-exp is an open-weight language model from SupraLabs. Supra1.5-50M-Base-exp (FP32) weighs 104 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • Supra1.5-exp is a 52M parameter base model for text generation developed by SupraLabs.
  • It is a continued-pretrained Llama-style model designed for supervised fine-tuning and reinforcement learning, supporting English with a context length of 5,120 tokens.
  • The model is released under the Apache 2.0 license.

Summary of the SupraLabs/Supra1.5-50M-Base-exp model card, 2026-10-01

What it is

Released bySupraLabs
Released2026-06-10
Parameters52M
VRAM104 MB for the weights

What it runs on

Memory and cards for Supra1.5-50M-Base-exp (FP32)

104 MBweights, file size
12 MBcache per 1K tokens
482 MBruntime overhead, at least
5,120 tokenscontext max
CardRequests at onceContext maxMemory
5K each
RTX 3060 12 GB175all 5K11.6 GB
RTX 4060 Ti 16 GB235all 5K15.4 GB
RTX 3090 24 GB362all 5K23.4 GB
RTX 4090 24 GB361all 5K23.4 GB
RTX 5090 32 GB483all 5K31.0 GB
L40S 48 GB689all 5K44.0 GB
A100 80 GB1000+all 5K78.2 GB
H100 80 GB1000+all 5K78.1 GB
RTX PRO 6000 Blackwell 96 GB1000+all 5K93.8 GB
DGX Spark (GB10) 128 GB unified1000+all 5K107 GB
H200 141 GB1000+all 5K138 GB
B200 180 GB1000+all 5K176 GB
Memory needed at each load
Requests at once5K tokens each
1649 MB
5900 MB
81.1 GB
161.6 GB
322.6 GB
644.6 GB

One card, with vLLM's small-card settings.

From the model card

What SupraLabs says about Supra1.5-exp

Read the model card

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 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:

SpecificationValue
ArchitectureLlamaForCausalLM
Parameters~50M
Vocabulary Size32,000
Hidden Size512
Layers12
Attention Heads8
KV Heads4
Context Length5,120 tokens
TokenizerOriginal 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

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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