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
| 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> | |
| <span>βββββββββββββββββββββββββββββββββββββββββ</span> | |
| <br/> | |
| Rx Codex Tokenizer: Professional Tokenizer for Modern AI | |
| <br/> | |
| <span>βββββββββββββββββββββββββββββββββββββββββ</span> | |
| <br/> | |
| </b> | |
| </h3> | |
| <br/> | |
| <div align="center" style="line-height: 1;"> | |
| | | |
| <a href="https://huggingface.co/rxmha125" target="_blank">π€ HuggingFace</a> | |
| | | |
| <a href="https://rxcodexai.com" target="_blank">π Website</a> | |
| | | |
| <a href="mailto:contact@rxcodexai.com" target="_blank">π§ Contact</a> | |
| | | |
| <br/> | |
| </div> | |
| <br/> | |
| ## 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 |