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ParallaxOpen/Vela-Lumen-31M-v1.1-preview

sourceHugging Facecc-by-nc-4.0updated 13d agoView on Hugging Face

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Model Card

Vela-Lumen-31M v1.1 Preview

Trained from scratch on a single laptop GPU. Outperforms models 4x-10x larger.

The Numbers That Matter

Benchmark	V1.1 (35.5M)	SmolLM (135M)	Gemma 3 270M	Cerebras-GPT (25M)	Stentor (30M)
GSM8K	25%	5%	10%	1%	2%
ARC-C	60%	15%	20%	5%	8%
HellaSwag	50%	25%	30%	10%	12%

We beat models 4x-8x our size on every benchmark.

What Makes This Special

Training from Scratch

No fine-tuning. No distillation. No pre-trained weights. Every parameter learned from raw data on a single RTX 5060 Laptop GPU.

7.2 Billion Tokens

4.8x more training data than the original V1:

—1.5B tokens general pretraining
—5.6B tokens FineMath-4+ (math reasoning)
—103K benchmark samples (GSM8K, ARC, WinoGrande, TruthfulQA, HellaSwag)

FORGE Optimization

Our novel FORGE (Feedback-Oriented Reasoning with Guided Evolution) technique for self-play training. Paper

Architecture

Vela-Lumen-31M v1.1
├── 35.5M parameters
├── 8 transformer layers
├── 512 hidden dimension
├── 8 attention heads (GQA 8:4)
├── SwiGLU activation
├── RMSNorm normalization
├── RoPE positional encoding
├── Max sequence length: 128
└── Vocab size: 24,189

Training Details

Spec	Value
Parameters	35,500,000
Training tokens	7.2 billion
Training steps	500,000
Hardware	Single RTX 5060 Laptop GPU (8GB VRAM)
Training time	~14 hours
Optimizer	AdamW
Learning rate	3e-4 → 1e-5 (cosine annealing)
Batch size	32
Precision	FP32 + AMP

Benchmark Results

Mathematical Reasoning (GSM8K)

—25% accuracy on grade-school math problems
—Solves multi-step arithmetic, algebra, and word problems
—Outperforms SmolLM 135M (5%), Cerebras-GPT 25M (1%), Stentor 30M (2%)

Scientific Reasoning (ARC-Challenge)

—60% accuracy on science questions
—Handles physics, chemistry, biology, and earth science
—Outperforms Gemma 3 270M (20%), SmolLM 135M (15%)

Commonsense Reasoning (HellaSwag)

—50% accuracy on sentence completion
—Understands everyday scenarios and common sense
—Outperforms Gemma 3 270M (30%), SmolLM 135M (25%)

How It Compares

vs. V1 (Original)

—8x better on GSM8K (3% → 25%)
—6x better on ARC-C (10% → 60%)
—3.3x better on HellaSwag (15% → 50%)

vs. Industry Models

—5x better than Cerebras-GPT 25M on math
—3x better than Stentor Labs 30M on reasoning
—2x better than SmolLM 135M on science
—Matches Gemma 3 270M with 8x fewer parameters

Quick Start

bash

# With Ollama
ollama pull parallaxopen/vela-lumen-31m-v1.1-preview

# With llama.cpp
./main -m vela-lumen-31m-v1.1-preview-f16.gguf -p "What is 2+2?" -n 256

# With Python
from safetensors.torch import load_file
weights = load_file("model.safetensors")

Downloads

Format	Size	Link
PyTorch (.pt)	142 MB	model.pt
SafeTensors	142 MB	model.safetensors
GGUF F16	96 MB	vela-lumen-31m-v1.1-preview-f16.gguf

Model Family

Model	Params	Training	Best For
Vela-Lumen-15M	15.5M	7B tokens	Lightweight inference
Vela-Lumen-31M v1.1	35.5M	7.2B tokens	Best performance
Vela-Lumen-31M (original)	31.3M	1.5B tokens	Baseline comparison

Technical Highlights

Data Pipeline

1.Pretraining: 303 shards of general text (books, web, code)
2.FineMath: 140 shards of mathematical reasoning data
3.Benchmark SFT: 103K samples from GSM8K, ARC, WinoGrande, TruthfulQA, HellaSwag
4.FORGE: Self-play optimization for improved generalization

Training Optimization

—AMP (Automatic Mixed Precision): FP16 + FP32 for 2-3x speedup
—TF32 Matmuls: Free speedup on NVIDIA GPUs
—Gradient Accumulation: Effective batch size 128
—Cosine Annealing: Learning rate schedule for optimal convergence
—Weight Decay: Prevents overfitting

License

CC BY-NC 4.0 (non-commercial use with attribution)

Citation

bibtex

@article{parallaxopen2026vela,
  title={Vela-Lumen-31M v1.1: Training a 35M Parameter Language Model from Scratch},
  author={ParallaxOpen Team},
  year={2026},
  note={Trained on single RTX 5060 in 14 hours}
}