Instructions to use xemha125/Rx_Codex_Tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xemha125/Rx_Codex_Tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xemha125/Rx_Codex_Tokenizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xemha125/Rx_Codex_Tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xemha125/Rx_Codex_Tokenizer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xemha125/Rx_Codex_Tokenizer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xemha125/Rx_Codex_Tokenizer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xemha125/Rx_Codex_Tokenizer
- SGLang
How to use xemha125/Rx_Codex_Tokenizer with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "xemha125/Rx_Codex_Tokenizer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xemha125/Rx_Codex_Tokenizer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "xemha125/Rx_Codex_Tokenizer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xemha125/Rx_Codex_Tokenizer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xemha125/Rx_Codex_Tokenizer with Docker Model Runner:
docker model run hf.co/xemha125/Rx_Codex_Tokenizer
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language:
- en
license: apache-2.0
library_name: transformers
tags:
- tokenizer
- rx-codex
- medical-ai
- code-tokenizer
- chat-ai
pipeline_tag: text-generation
---
<div align="center">
<img src="./rx_codex_logo.png" width="50%" alt="Rx-Codex-AI" />
</div>
<h3 align="center">
<b>
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Rx Codex Tokenizer: Professional Tokenizer for Modern AI
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</b>
</h3>
<br/>
<div align="center" style="line-height: 1;">
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<a href="https://huggingface.co/rxmha125" target="_blank">π€ HuggingFace</a>
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<a href="https://rxcodexai.com" target="_blank">π Website</a>
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<a href="mailto:contact@rxcodexai.com" target="_blank">π§ Contact</a>
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</div>
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## Overview
Rx Codex Tokenizer is a state-of-the-art BPE tokenizer designed for modern AI applications. With 128K vocabulary optimized for English, code, and medical text, it outperforms established tokenizers in comprehensive benchmarks.
**Developed by Rx Founder & CEO of Rx Codex AI**
## Benchmark Results
### Tokenizer Battle Royale - Final Scores
| Tokenizer | Final Score | Speed | Compression | Special Tokens | Chat Support |
|-----------|-------------|-------|-------------|----------------|--------------|
| π₯ **Rx Codex** | **84.51/100** | 24.84/25 | 35.0/35 | 16.67/20 | 15/15 |
| π₯ GPT-2 | 67.89/100 | 24.89/25 | 35.0/35 | 0.0/20 | 15/15 |
| π₯ DeepSeek | 67.77/100 | 24.77/25 | 35.0/35 | 0.0/20 | 15/15 |
### Final Scores Comparison

### Speed Analysis

### Compression Efficiency

### Multi-dimensional Analysis

### Token Count Efficiency

## Key Features
- **128K Vocabulary** - Optimal balance of coverage and efficiency
- **Byte-Level BPE** - No UNK tokens, handles any text
- **Medical Text Optimized** - Perfect for healthcare AI applications
- **Code-Aware** - Excellent programming language support
- **Chat-Ready Tokens** - Built-in support for conversation formats
## Technical Specifications
- **Vocabulary Size**: 128,256 tokens
- **Special Tokens**: 9 custom tokens
- **Model Type**: BPE with byte fallback
- **Training Data**: OpenOrca 5GB English dataset
- **Average Speed**: 0.63ms per tokenization
- **Compression Ratio**: 4.18 characters per token
## Use Cases
- **Chat AI Systems** - Built-in chat token support
- **Medical AI** - Optimized for healthcare terminology
- **Code Generation** - Excellent programming language handling
- **Academic Research** - Efficient with complex text
## License
Apache 2.0
## Author
**Rx Founder & CEO**
Rx Codex AI |