Instructions to use Arioron/Vex-Amber-Fable-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arioron/Vex-Amber-Fable-2.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Arioron/Vex-Amber-Fable-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Arioron/Vex-Amber-Fable-2.0")
model = AutoModelForCausalLM.from_pretrained("Arioron/Vex-Amber-Fable-2.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Arioron/Vex-Amber-Fable-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arioron/Vex-Amber-Fable-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Vex-Amber-Fable-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arioron/Vex-Amber-Fable-2.0

SGLang

How to use Arioron/Vex-Amber-Fable-2.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Arioron/Vex-Amber-Fable-2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Vex-Amber-Fable-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Arioron/Vex-Amber-Fable-2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Vex-Amber-Fable-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Arioron/Vex-Amber-Fable-2.0 with Docker Model Runner:
```
docker model run hf.co/Arioron/Vex-Amber-Fable-2.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🏆 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}}
}

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Safetensors

Model size

2B params

Tensor type

F16

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Quantizations

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