HarleyCoops - Overview

Christian H. Cooper

GitHub Stats

About Me

Current Focus:

Building AI-driven financial analysis tools leveraging LLMs and reinforcement learning
Training small-scale language models for specialized reasoning tasks
Exploring Group Relative Policy Optimization (GRPO) for model alignment
Developing mathematical visualization tools with Manim

Expertise Areas:

CFA training, Derivatives, and Options Trading
Congressional Trading Analysis and Market Microstructure
Quantitative Finance and Risk Management
Machine Learning Engineering (PyTorch, TensorFlow)
Mathematical Visualization and 3D Graphics

Unique Expertise

Advanced PyTorch implementations
Reinforcement Learning from Human Feedback (RLHF)
Large Language Model fine-tuning and alignment
Computational geometry and fractal mathematics

Nerd Trophy Case

I once got Karpathy to reply "Nice."

My meager Google Scholar reference

Featured Projects & Research

Quantitative Finance Visualizations

Volatility Surface Visualization

3D visualization of options volatility skew across strikes and maturities. This surface represents the Black-Scholes implied volatility smile, crucial for pricing exotic derivatives and understanding market risk premia.

Recursive Rhombicosidodecahedron

A fractal Archimedean solid demonstrating computational complexity in 3D geometry. Each vertex recursively contains another complete polyhedron, requiring precise transformations across 62 unique faces - a challenging test case for LLM mathematical reasoning.

Technical Highlights

Volatility Surface: Real-time Black-Scholes implied volatility calculations
3D Rendering: Custom Manim animations for financial mathematics
Computational Geometry: Precise coordinate transformations in 3D space
Data Visualization: Market data analysis and pattern recognition

Tech Stack & Skills

Programming Languages

Frameworks & Libraries

Cloud & DevOps

Financial & Quantitative Tools

Public models on Hugging Face

As of Nov 2025: 9 models / 6 datasets / 6 Spaces. Featured models and datasets:

Featured Models

Model	Focus	Base	Size	Updated
Qwen3-0.6B-Dakota-Grammar-RL	Dakota language grammar via GRPO	Qwen3-0.6B	0.8B	1 day ago
nanochat-AquaRat	RL training on algebraic reasoning (AQuA-RAT)	nanochat	-	18 days ago
nanochat561	Text generation experiments	nanochat	-	20 days ago
Qwen.5B-OpenR1Math	Math reasoning (Open-R1 style)	Qwen2.5-0.5B-Instruct	0.5B	Feb 13
Qwen.5B-GSM8K	Math finetune (GSM8K emphasis)	Qwen2.5-0.5B-Instruct	0.5B	Feb 12
GRPOtuned / GRPOtuned2	GRPO experiments on 0.5B LLMs	Qwen2.5-0.5B-Instruct	0.5B	Feb 6-9

Featured Datasets

Dataset	Description	Size	Updated
dakota-bilingual-qa	Dakota-English bilingual Q&A pairs	2.45k rows	5 days ago
Stoney10kRL	Stoney Nakoda RL training data	-	13 days ago
synthetic_stoney_data	Synthetic Stoney Nakoda language data	68.8k rows	Apr 28
StoneyNakoda45k	Stoney Nakoda language corpus	-	Apr 5
StoneyNakoda	Stoney Nakoda language dataset	14.5k rows	Jan 22
StoneyCIL	Stoney Nakoda CIL dataset	-	Jan 4

Direct links:

Models: Qwen3-0.6B-Dakota-Grammar-RL, nanochat-AquaRat, nanochat561, Qwen.5B-OpenR1Math, Qwen.5B-GSM8K, GRPOtuned, GRPOtuned2
Datasets: dakota-bilingual-qa, Stoney10kRL, synthetic_stoney_data, StoneyNakoda45k, StoneyNakoda, StoneyCIL
Spaces: Dakota Grammar RL Demo, deepsitecoder, Stoney-1, Ask About Stoney

Quickstart (try a model in 5 lines):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
mdl = "HarleyCooper/Qwen3-0.6B-Dakota-Grammar-RL"  # or any model ID above
tok = AutoTokenizer.from_pretrained(mdl)
model = AutoModelForCausalLM.from_pretrained(mdl, torch_dtype=torch.float16, device_map="auto")
print(tok.decode(model.generate(**tok("Translate to Dakota:", return_tensors="pt").to(model.device), max_new_tokens=64)[0], skip_special_tokens=True))