🚀 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:

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

• 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:

File Name	Size	Type	Description
klint_32m_release.pt	112.5 MB	All-in-One Bundle	Complete deployment bundle containing Klint-32M weights, Factor Tokenizer codebooks, KlintConfig, and training metadata. Ready for one-line loading.
klint_32m_best.pt	111.9 MB	Best Checkpoint	Optimal validation loss checkpoint (~2.76 Cross-Entropy Loss) across training.
klint_32m_step_3000.pt	111.9 MB	Checkpoint	Completed step 3,000 checkpoint.
klint_32m_step_2500.pt	111.9 MB	Checkpoint	Intermediate training milestone at step 2,500.
klint_32m_step_2000.pt	111.9 MB	Checkpoint	Intermediate training milestone at step 2,000.
klint_32m_step_1505.pt	111.9 MB	Checkpoint	Checkpoint at step 1,505.
klint_32m_step_1500.pt	111.9 MB	Checkpoint	Resume checkpoint at step 1,500.
klint_32m_step_1000.pt	111.9 MB	Checkpoint	Training milestone at step 1,000.
klint_32m_step_500.pt	111.9 MB	Checkpoint	Early training milestone at step 500.
klint_32m_step_5.pt	111.9 MB	Checkpoint	Initial training calibration step.
tokenizer_best.pt	630 KB	Discrete Tokenizer	Standalone multi-stream Residual Vector Quantizer (RVQ) factor codebooks (512 Price, 256 Range, 256 Activity codes).
synthetic_market_trajectory.png	76 KB	Visualization	Generative candlestick trajectory sample showing autoregressive price path sampling.

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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

---

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()))

---

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

Parameter	Specification	Details
Model Type	Causal Autoregressive Foundation Transformer	Decoder-only sequence model with multi-stream interleaving
Trainable Parameters	28,642,560 (~32M analytically with embeddings)	Optimized for high-throughput single-GPU inference
Layers ($N_{\text{layers}}$)	10	Uniform causal transformer blocks
Hidden Dimension ($d_{\text{model}}$)	480	Balanced capacity-to-memory ratio
Attention Heads ($N_{\text{heads}}$)	10 (head dimension = 48)	Multi-head self-attention with RoPE
Feed-Forward Dimension ($d_{\text{ff}}$)	1,920 ($4 \times d_{\text{model}}$)	SwiGLU activation projection
Positional Embedding	Rotary Position Embeddings (RoPE)	Causal relative-distance preservation
Normalization	RMSNorm ($\epsilon = 10^{-5}$)	Pre-normalization architecture
Factor Codebooks	Multi-Stream Residual Vector Quantizer (RVQ)	$K_p = 512$ (Price), $K_r = 256$ (Range), $K_a = 256$ (Activity)
Factor Generation Order	Causal Decomposition Chain	$\text{Price} \to \text{Range} \to \text{Activity}$
Context Length	768 factor tokens (256 bars) up to 3,072 tokens	Causal intraday to multi-day dynamics

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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