README.md

---
license: apache-2.0
language:
  - multilingual
tags:
  - tokenizer
  - bpe
  - byte-level-bpe
  - chatml
  - routing
  - moe
  - robotics
  - gemma
  - gemma4
  - jirack
  - google
---

# 💎 Gemma Robotics & Routing Tokenizer
- Gemma Pro Tokenizer from JiRack team  . Fully compatible with gemma4 models from Google
- Size {
 Vocab size: 262251
 pad_token_id: 0
 eos_token_id: 1
 }
- Compatible with gemma 4 • Optimized for Code . Use resize function without adaptation, see examples below
- It needs 100k example to fully adapt routing features for RAG Routing model . So check gemma modification tokenizer rules
- A gemma 4-based tokenizer enhanced with FIM markers from Microsoft datasets (
    <|fim_prefix|>,
    <|fim_middle|>,
    <f|im_suffix|>
  )
- Added Robotics & Embodiment tags (
   "<|action_start|>",
    "<|action_end|>",
    "<|trajectory_start|>",
    "<|trajectory_end|>",
    "<|joint_start|>",
    "<|joint_end|>",
    "<|sensor_start|>",
    "<|sensor_end|>",
    "<|command_start|>",
    "<|command_end|>",
    "<|state_start|>",
    "<|state_end|>",
    "<|pose|>",
    "<|velocity|>",
    "<|force|>",
    "<|torque|>",
    "<|gripper|>",
    "<|navigation|>",
    "<|obstacle|>",
    "<|task_start|>",
    "<|task_end|>",
    "<|plan_start|>",
    "<|plan_end|>",
    "<|behavior_start|>",
    "<|behavior_end|>",
    "<|skill_start|>",
    "<|skill_end|>",
    "<|motor|>",
    "<|servo|>",
    "<|imu|>",
    "<|lidar|>",
    "<|camera|>",
    "<|depth|>",
    "<|waypoint|>",
    "<|path|>",
    "<|collision|>",
    "<|grasp|>",
    "<|release|>",
    "<|homing|>",
    "<|emergency_stop|>",
    "<|calibration|>",
    "<|manipulation|>",
    "<|locomotion|>",
    "<|feedback|>",
    "<|control_loop|>",)

- Added Multi models support (
    "<|image|>",
    "<|video|>",
    "<|sound|>",
    "<|voice|>",
    "<|listening|>",
    "<|vision|>",)

- Added Human mood tags (
    "<|mood_happy|>",
    "<|mood_sad|>",
    "<|mood_angry|>",
    "<|mood_neutral|>",
  )

- Added RAG routing tags for RAG MoE Systems (
    "__SCIENCE__",
    "__CODING__",
    "__STOCK_EXCHANGE__",
    "__MEDICINE__",
    "__GOVERNMENT__",
    "__NEWS__",
    "__GENERAL__",
    "__MATERIAL_SCIENCE__",
    "__ELECTRONICS__",
    "__MICROELECTRONICS__",
    "__ENGINEERING__",
    "__ROBOTICS__",
    "__ENERGY__",
    "__AUTOMOTIVE__",
    "__AVIATION__",
    "__MATH__",
    "__PYTHON__",
    "__C__",
    "__CPP__",
    "__C_SHARP__",
    "__JAVA__",
    "__JAVASCRIPT__",
    "__TYPESCRIPT__",
    "__RUST__",
    "__GO__",
    "__RUBY__",
    "__PHP__",
    "__SWIFT__",
    "__KOTLIN__",
    "__BASH__",
    "__SQL__",
    "__ASSEMBLY__",
    "__PHILOSOPHY__",
    "__LITERATURE__",
    "__SOCIOLOGY__",
    "__PSYCHOLOGY__",
    "__POLITICAL_SCIENCE__",
    "__CULTURAL_STUDIES__",
    "__ETHNOGRAPHY__",
    "__HUMAN_RIGHTS__",
    "__COMPLIANCE__",
    "__MILITARY__",
    "__BANKING__",
    "__OIL_INDUSTRY__",
    "__LIGHT_INDUSTRY__",
    "__NATURE__",
    "__OCEAN__",
    "__SPORT__",
    "__CULINARY__",
    "__TRAVEL__",
    "__HOBBY__"
  )
    
- Fully compatible with Microsoft BigCode datasets including The Stack, StarCoder, and NextCoder.
- Enables efficient training on large-scale coding data for superior code generation and understanding.


**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**  JiRack Architecture: Build Sovereign Financial Models from Scratch
- Leveraging the JiRack Tokenizer and our Open Dataset, we enable financial institutions to develop secure, internal AI models from the ground up. This approach ensures maximum data privacy and model sovereignty for high-stakes banking operations.
- There is fix price for FinTech
- 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.


**JiRack Corp Tokenizer  solution**
- Use JiRack models with trusted, high-quality coding datasets while maintaining full control over your code and data privacy.
- Excellent fit for Banks, Fintech companies, and any organization that requires strict data confidentiality and security.
- Update JiRack model for corp privacy coding. 

**JiRack Tokenizer Subcription**
- All subscribed members will receive regular tokenizer updates optimized for the latest high-quality coding datasets.

### Open Robot platform 

- **Tiangong** : https://english.www.gov.cn/english.www.gov.cn/news/202411/13/content_WS673406e2c6d0868f4e8ece33.html
- **Unitree g1**  https://a.co/d/0e4A8YVc
- **LimX Oli**  https://www.limxdynamics.com/en/products/oli?channel=option_google_advertising__c-
- **ubtrobot** https://www.ubtrobot.com/en/
- **x-humanoid** https://www.x-humanoid.com/detail/hskw.html
  


### Key Features

- **Algorithm**: Byte-Level BPE
- **Vocabulary Size**: **128,000** tokens — excellent balance between precision and efficiency
- **Multilingual & Technical Strength**: Optimized for English, Russian, code, scientific literature, and technical documentation
- **Domain Specialization**: Strong performance on programming languages, engineering, robotics, and scientific texts

### Special Tokens Support

- Full **gemma4 compatible format** dialogue format 
- FIM (Fill-in-the-Middle) support for code generation
- Rich set of domain routing tokens (`__CODING__`, `__PYTHON__`, `__ROBOTICS__`, `__SCIENCE__`, etc.)
- Extended robotics and control tokens
  
### CMS Manhattan Service & Support 

- Jirack patent guards your technology for competitors 
- Redesign Llama , Qwen , Gemma to Ternary model
- Re-tain and replace embeddings for  Llama , Qwen , Gemma  to extend langeages to 347
- Accelerate inference via high compression 256K tokenizer and replace multiplication with sum operations via Ternary weights

**Install for Llamma compatible models in your chat script**

- from transformers import AutoModelForCausalLM

- model = AutoModelForCausalLM.from_pretrained(your model)

- # The Must !
- model.resize_token_embeddings(len(tokenizer.tokenizer))   # или просто len(tokenizer.tokenizer)

- print("New Embedding size for you chat script:", model.get_input_embeddings().weight.shape[0])

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

Vocab size: 262251
pad_token_id: 0
eos_token_id: 1


## 📧 Contact & Licensing

For joint ventures, hardware integration, or licensing inquiries:

- **Email:** grabko@cmsmanhattan.com
- **Phone:** +1 (516) 777-0945
- **Location:** New York, USA

## 📧 Copyright 2026  CMS Manhattan . All rights reserved