kgrabko/JiRackBaseDataset

💎 JiRack Base dataset for 1.5B model

Dataset: the dataset formated for JiRack tokenizer . I recommend initializing the model with a 4K context window for initial stability, followed by scaling to 8K context using specialized JiRack 8K datasets. This two-stage approach ensures robust positional encoding before extending the model's long-range dependency.

Time: JiRack 1.5B: High-Efficiency Financial Modeling

• We are training a compact 1.5B parameter model on an extensive 11 billion token corpus. By training on a token-to-parameter ratio of nearly 7:1, we achieve exceptional knowledge density and reasoning capabilities in a lightweight architecture.
• Performance: JiRack Ternary Pro 1.5b about 28–36 hours per epoch on NVIDIA BlackWell 96 Gb VRAM
• Performance: JiRack Ternary Pro 10b about 7-9 days per epoch on NVIDIA BlackWell 96 Gb VRAM
• Optimization: Optimized for secure, low-latency banking applications.

Inventor: Konstantin Vladimirovich Grabko
Organization: CMS Manhattan JiRack Technology
Official Site: www.cmsmanhattan.com

Designed for Banking and Fintech Institutions

Banks and Fintech Build secure, internal models tailored for the banking sector. We provide end-to-end solutions to pre-train models for fraud prevention, spam filtering, risk assessment, and Anti-Money Laundering (AML) detectio

• This is the base checkpoint, evaluated prior to fine-tuning on domain-specific datasets. The primary objective is to validate RoPE (Rotary Positional Embeddings) stability and coherence following the initial pre-training phase.

⚠️ IMPORTANT NOTICE — PROPRIETARY TECHNOLOGY

Allowed:

• Personal and non-commercial research use only

Strictly Prohibited without a written commercial license:

• Any commercial use (SaaS, mobile apps, edge devices, paid services, etc.)
• Creating and distributing derivative models for profit
• Removing or modifying any copyright or legal notices
• Patenting any part of this technology

Commercial users must obtain a signed license and pay 5% royalty on net revenue.

Any unauthorized commercial use will be pursued legally under New York law.

Contact for commercial license: grabko@cmsmanhattan.com There is fix price for FinTech

⚠️ Finch tech AL solution

Custom AI Solutions with JiRack

• Deploy your own secure, high-performance model from scratch. I specialize in delivering the JiRack modern architecture on NVIDIA Clusters, fully optimized for your private datasets.

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• Please contact to CMS Manhttan for the solution

• Tesr Tokenizer size !

(venv_ji) root@jirack2:# python -c ' from transformers import AutoTokenizer tok = AutoTokenizer.from_pretrained("./jirack_code_tokenizer_fixed") print("Vocab size:", len(tok)) print("pad_token_id:", tok.pad_token_id) print("eos_token_id:", tok.eos_token_id) '

• Vocab size: 128259
• pad_token_id: 128001
• eos_token_id: 128001