Klint-32M

🚀 Klint-32M: Generative Financial Foundation Model Family

Klint-32M is an open research causal autoregressive foundation architecture for generative financial time-series modeling. It decomposes raw market candlesticks into three stationary causal factor streams (Price-Path, Range-Shape, and Activity), quantizes them into discrete token codes with Residual Vector Quantization (RVQ), models their joint transition distribution with a causal Transformer, and decodes them with strict mathematical guarantees preserving candle geometry:

$$\text{High} \ge \max(\text{Open}, \text{Close}) \quad \text{and} \quad \text{Low} \le \min(\text{Open}, \text{Close})$$

• Author: Akhilesh Varma (akhverm / GitHub)
• Full Source Code & Architecture: https://github.com/ak495867/Klint-32M
• Model Checkpoints Repository: akhverm/Klint-32M

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📦 Checkpoint Family Inventory

This repository contains the complete release artifacts and training trajectory checkpoints for the Klint-32M model family:

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⚡ Quickstart & Inference

Option 1: Using the Official GitHub Inference Engine (Recommended)

Clone the official Klint-32M GitHub repository and use the high-performance CLI:

git clone https://github.com/ak495867/Klint-32M.git
cd Klint-32M
pip install -e .

# 1. Live market inference on Solana via yfinance (auto-downloads checkpoint):
python inference.py --ticker SOL-USD --horizon 30 --save_plot forecast_sol.png

# 2. Live market inference on Nvidia:
python inference.py --ticker NVDA --horizon 50 --save_plot forecast_nvda.png

# 3. Using your local release bundle:
python inference.py --checkpoint klint_32m_release.pt --ticker BTC-USD

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Option 2: Download Directly via huggingface_hub in Python

import torch
from huggingface_hub import hf_hub_download

# Download the complete all-in-one release bundle
bundle_path = hf_hub_download(
    repo_id="akhverm/Klint-32M",
    filename="klint_32m_release.pt"
)

# Load into PyTorch
device = "cuda" if torch.cuda.is_available() else "cpu"
bundle = torch.load(bundle_path, map_location=device, weights_only=False)

print("Klint-32M Release Bundle Loaded Successfully!")
print("Config:", bundle["config"])
print("Model Parameters:", sum(p.numel() for p in bundle["model_state_dict"].values()))

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Option 3: End-to-End Trajectory Generation in Python

import torch
from huggingface_hub import hf_hub_download
from klint.models.klint_32m import Klint32M
from klint.tokenizer.factor_tokenizer import FactorTokenizer
from klint.tokenizer.geometric_decoder import GeometricDecoder

# 1. Download bundle from Hugging Face
bundle_file = hf_hub_download(repo_id="akhverm/Klint-32M", filename="klint_32m_release.pt")
bundle = torch.load(bundle_file, map_location="cpu", weights_only=False)

# 2. Instantiate Model and Tokenizer
model = Klint32M(bundle["config"])
model.load_state_dict(bundle["model_state_dict"])
model.eval()

tokenizer = FactorTokenizer()
tokenizer.load_state_dict(bundle["tokenizer_state_dict"])
tokenizer.eval()

decoder = GeometricDecoder()

# 3. Generate 30 future market bars conditioned on prompt tokens
prompt_tokens = torch.randint(0, 256, (1, 90))  # 30 bars context = 90 factor tokens
generated = model.generate_tokens(prompt_tokens, num_bars=30, temperature=0.8, top_k=40)

# 4. Decode into physically guaranteed OHLCV candlesticks
new_tokens = generated[:, 90:]
p_tok, r_tok, a_tok = tokenizer.deinterleave(new_tokens)
rec_p, rec_r, rec_a = tokenizer.decode_tokens(p_tok, r_tok, a_tok)
ohlcv_candles = decoder(rec_p, rec_r, rec_a, anchor_price=150.0)

print("Synthesized Candles Shape:", ohlcv_candles.shape)  # [1, 30, 5] -> (Open, High, Low, Close, Volume)

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🏛️ Model Architecture Specifications

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📊 Benchmark & Quantitative Stress-Testing

Klint-32M has been evaluated using an institutional quantitative stress-testing suite across 300+ liquid assets and 6 asset classes:

1. Directional Accuracy: Out-of-sample directional hit rate consistently exceeding the 50.0% random-walk baseline on liquid instruments.
2. Candle Invariant Validity: 100.00% physical validity guarantee ($High \ge \max(Open, Close)$ and $Low \le \min(Open, Close)$) via structural geometric decoding.
3. Monte Carlo Resampling: 2,000-path stationary block bootstrap confirming performance significance ($p$-value $< 0.05$ against null hypothesis).
4. Friction Resistance: Tested across 0 to 50 bps transaction fee sweeps with positive net Sharpe ratios under standard retail/institutional fee regimes.
5. Purged Walk-Forward Stability: Chronologically partitioned cross-validation with embargo gaps achieving Walk-Forward Efficiency Ratios (WFER) $\ge 0.5$.

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🔗 Citation & Links

@misc{varma2026klint32m,
  author = {Akhilesh Varma},
  title = {Klint-32M: A Foundation Architecture for Generative Financial Time-Series Modeling},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/akhverm/Klint-32M}},
  note = {GitHub: \url{https://github.com/ak495867/Klint-32M}}
}

• GitHub Repository: https://github.com/ak495867/Klint-32M
• Hugging Face Model Hub: https://huggingface.co/akhverm/Klint-32M
• License: MIT