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kgrabko/JiRackBaseDataset

๐Ÿ’Ž 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 11โ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/kgrabko/JiRackBaseDataset.

sourceHugging Faceotherupdated 5mo agoView on Hugging Face
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๐Ÿ’Ž 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

  • โ€”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.
  • โ€”Performance: JiRack Ternary Pro 1.5b about 28โ€“36 hours per epoch on NVIDIA BlackWell 96 Gb VRAM
  • โ€”Performance: JiRack Ternary Pro 10b about 7-9 days per epoch on NVIDIA BlackWell 96 Gb VRAM
  • โ€”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

  • โ€”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.

โš ๏ธ IMPORTANT NOTICE โ€” PROPRIETARY TECHNOLOGY

Allowed:

  • โ€”Personal and non-commercial research use only

Strictly Prohibited without a written commercial license:

  • โ€”Any commercial use (SaaS, mobile apps, edge devices, paid services, etc.)
  • โ€”Creating and distributing derivative models for profit
  • โ€”Removing or modifying any copyright or legal notices
  • โ€”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

โš ๏ธ Finch tech AL solution

Custom AI Solutions with JiRack

  • โ€”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.
  • โ€”Let's build your sovereign AI today. DM for inquiries.
  • โ€”Please contact to CMS Manhttan for the solution
  • โ€”# Tesr Tokenizer size ! (venvji) root@jirack2:# python -c ' from transformers import AutoTokenizer tok = AutoTokenizer.frompretrained("./jirackcodetokenizerfixed") print("Vocab size:", len(tok)) print("padtokenid:", tok.padtokenid) print("eostokenid:", tok.eostoken_id) '
  • โ€”Vocab size: 128259
  • โ€”padtokenid: 128001
  • โ€”eostokenid: 128001