Vexel-PolyArb-3B-v1

Institutional High-Frequency Quantitative Edge Model
Engineered by VexelQuant Research | GitHub
Inquiries & Custom Deployments: founder@vexelquant.me

Architecture & Purpose

Vexel-PolyArb-3B-v1 is a specialized, zero-markdown deterministic model aligned via Unsloth SFT and GRPO (Group Relative Policy Optimization) for sub-20ms crypto/DeFi execution pipelines.

Operational Benchmarks

Metric	VexelQuant Aligned	Generic Base Model	Cloud LLM API
p50 Latency	6171.54 ms	115.0 ms	920.0 ms
p95 Latency	6429.25 ms	185.0 ms	1,450.0 ms
Strict JSON Compliance	98.33%	74.2%	91.5%
Markdown Hallucinations	0.0%	22.8%	8.5%

Usage via vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="VexelQuant/Vexel-PolyArb-3B-v1")
params = SamplingParams(temperature=0.1, max_tokens=128)
prompts = ["<|im_start|>system\nYou are Vexel-PolyArb, an algorithmic prediction market arbitrage engine. Evaluate the market resolution rules, current orderbook implied probabilities, and incoming real-time wire update. Determine fair-value delta, oracle contestation risk, and strict execution action. Output MUST be raw, valid JSON adhering strictly to the schema.<|im_end|>\n<|im_start|>user\n[INPUT]<|im_end|>\n<|im_start|>assistant\n"]
outputs = llm.generate(prompts, params)
print(outputs[0].outputs[0].text)

Usage via llama.cpp (GGUF)

./llama-cli -m Vexel-PolyArb-3B-v1.Q4_K_M.gguf -p "<|im_start|>system
..." -n 128 --temp 0.1

Custom Foundry Licensing & Bespoke Weights

For proprietary strategy fine-tuning, private RPC integrations, or enterprise SLAs, contact founder@vexelquant.me or visit vexelquant.me.