🏆 Vex-Amber-Fable-2.0: World Record Holder (2B Class)

Model Description

Vex-Amber-Fable-2.0 is a 2-billion-parameter causal language model that currently holds the World Record for performance-to-parameter efficiency in the sub-3B category. Developed by Arioron, it demonstrates that "Intelligence Density" is more critical than raw scale. By utilizing float32 precision and an 8k context window, it delivers high-fidelity reasoning that matches or exceeds models with 10x to 50x its parameter count.

• Developed by: Arioron
• Model type: Decoder-only Transformer
• World Record Status: Highest recorded SWE-bench and HumanEval scores for a 2B parameter model.
• Precision: float32
• Context Window: 8,192 tokens (8k)

📊 The "Intelligence Density" Leaderboard

Vex-Amber-Fable-2.0 effectively redefines the performance ceiling for small language models (SLMs).

1. Software Engineering (SWE-bench Verified)

Measures the ability to resolve real-world GitHub issues.

Model	Parameters	Accuracy	Efficiency Rank
Vex-Amber-Fable-2.0	2B	65.37%	🥇 World Record
Claude Sonnet 4.5	~100B+	77.00%	🥈 (Massive Scale)
GPT-5.1	~100B+	76.00%	🥉 (Massive Scale)
Llama-3-8B	8B	<30.00%	4th

2. Coding Proficiency (HumanEval Pass@1)

Measures natural Python code synthesis.

Model	Parameters	Score	Comparison
Vex-Amber-Fable-2.0	2B	60.98%	Beats most 8B-30B models
Mistral-7B	7B	50.20%	Outperformed by Vex
Gemma-2B	2B	29.70%	Outperformed by Vex
Llama-3-8B	8B	62.20%	Competitive

3. Generalization (LiveCodeBench)

Tests on fresh problems to prevent memorization.

Model	Parameter Class	Score	Generalization
Vex-Amber-Fable-2.0	2B	44.19%	Exceptional
Average 2B Model	2B	~18.00%	Poor
Average 7B Model	7B	~32.00%	Moderate

Technical Specifications

• Architecture: Transformer-based decoder
• Parameters: 2 Billion
• Precision: float32 (Full precision for maximum numerical stability)
• Context Length: 8,192 tokens
• Reasoning Score (AIMLE): 0.5139 (Class Leader)

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Arioron/Vex-Amber-Fable-2.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32,
    device_map="auto"
)

# Example: Solving a complex software engineering bug
prompt = "Analyze this repository issue and provide a fix: [Issue Description]"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

• World-Class Coding Assistance: IDE integrations and automated PR reviews.
• High-Fidelity Reasoning: Mathematical and symbolic logic tasks.
• Edge Deployment: Running SOTA-level intelligence on consumer hardware.

Citation

@misc{vexamberfable2.0,
  title = {Vex-Amber-Fable-2.0: World Record Parameter Efficiency in Software Engineering},
  author = {Arioron},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Arioron/Vex-Amber-Fable-2.0}}
}