Create README.md

README.md

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+---
+viewer: true
+license: other
+license_name: cms-manhattan-jirack-v1.2
+license_link: LICENSE
+language:
+- en
+tags:
+- llama
+
+pipeline_tag: text-generation
+---
+
+# 💎 JiRack Boooks 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 14–18 hours per epoch on NVIDIA BlackWell 96 Gb VRAM
+- Performance: JiRack Ternary Pro 10b  about 4-5 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](http://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.
+- Let's build your sovereign AI today. DM for inquiries.
+- 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