Text Generation
Transformers
PyTorch
Persian
English
ysnrfd
From_Scratch
Custom
YSNRFD
LLM
Persian_LLM
Instructions to use ysn-rfd/ysnrfd-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ysn-rfd/ysnrfd-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ysn-rfd/ysnrfd-base")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ysn-rfd/ysnrfd-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ysn-rfd/ysnrfd-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ysn-rfd/ysnrfd-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ysn-rfd/ysnrfd-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ysn-rfd/ysnrfd-base
- SGLang
How to use ysn-rfd/ysnrfd-base 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 "ysn-rfd/ysnrfd-base" \ --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": "ysn-rfd/ysnrfd-base", "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 "ysn-rfd/ysnrfd-base" \ --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": "ysn-rfd/ysnrfd-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ysn-rfd/ysnrfd-base with Docker Model Runner:
docker model run hf.co/ysn-rfd/ysnrfd-base
| library_name: transformers | |
| tags: | |
| - From_Scratch | |
| - Custom | |
| - YSNRFD | |
| - ysnrfd | |
| - LLM | |
| - ysnrfd | |
| - Persian_LLM | |
| datasets: | |
| - none | |
| language: | |
| - fa | |
| - en | |
| # REPORT ANY PROBLEMS IN MODEL LOADING AND INFERENCE | |
| ## Model Details | |
| **WARNINNGS:** This Model IS **Pre-Trained**, **in the future will be finetuned**. | |
| ### Model Description | |
| The First Persian LLM By YSNRFD, This Model support Only English text Inputs, In The Future I Want Add Persian Language Support. | |
| - **Developed by:** ysnrfd | |
| - **Funded by:** ysnrfd | |
| - **Shared by:** ysnrfd | |
| - **Model type:** LLM | |
| - **Language(s) (NLP):** English | |
| - **License:** ysnrfd LICENSE | |
| ### Training Data | |
| wikitext2 | |
| #### Training Hyperparameters | |
| - **Training regime:** fp32 mixed precision | |
| ## Evaluation | |
| Not Yet | |
| ### Testing Data, Factors & Metrics | |
| ysnrfd en testing data | |
| #### Testing Data | |
| Not Yet | |
| #### Summary | |
| The Fisrt Persian LLM Trained From Scratch (Size Like SLM) | |
| - **Hardware Type:** Nvidia Tesla T4 | |
| - **Hours used:** 11H | |
| - **Cloud Provider:** Google Colab | |
| ### Model Architecture and Objective | |
| YSNRFD Architecture | |
| #### Hardware | |
| Nvidia Tesla T4 | |
| #### Software | |
| Python Code, From Scratch, Pytorch |