Instructions to use Veda-Labs/Vedika-Code-Pro-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veda-Labs/Vedika-Code-Pro-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Veda-Labs/Vedika-Code-Pro-v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Veda-Labs/Vedika-Code-Pro-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Veda-Labs/Vedika-Code-Pro-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Veda-Labs/Vedika-Code-Pro-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Veda-Labs/Vedika-Code-Pro-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Veda-Labs/Vedika-Code-Pro-v1
- SGLang
How to use Veda-Labs/Vedika-Code-Pro-v1 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 "Veda-Labs/Vedika-Code-Pro-v1" \ --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": "Veda-Labs/Vedika-Code-Pro-v1", "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 "Veda-Labs/Vedika-Code-Pro-v1" \ --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": "Veda-Labs/Vedika-Code-Pro-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Veda-Labs/Vedika-Code-Pro-v1 with Docker Model Runner:
docker model run hf.co/Veda-Labs/Vedika-Code-Pro-v1
| license: mit | |
| library_name: transformers | |
| # Vedika-Code-Pro-v1 | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="778985201_2399197484237416_4545083293112769701_n-1.webp" alt="Vedika-Code-Pro-v1" /> | |
| </div> | |
| <hr> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="https://vedalabs.online" target="_blank" style="margin: 2px;"> | |
| <img alt="Homepage" src="https://github.com/vedalabs-tech/Vedika-Code-Pro-v1/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| <a href="https://huggingface.co/Veda-Labs" target="_blank" style="margin: 2px;"> | |
| <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Vedika--Code--Pro--v1-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="https://github.com/vedalabs-tech" target="_blank" style="margin: 2px;"> | |
| <img alt="GitHub" src="https://img.shields.io/badge/GitHub-vedalabs--tech-white?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| <a href="https://x.com/VedaLabsAI" target="_blank" style="margin: 2px;"> | |
| <img alt="X (Twitter)" src="https://img.shields.io/badge/X-VedaLabsAI-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="LICENSE" style="margin: 2px;"> | |
| <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| ## Introduction | |
| **Vedika-Code-Pro-v1** is a state-of-the-art language model designed for advanced coding and reasoning tasks. | |
| ### System Prompt | |
| The default system prompt for Vedika-Code-Pro-v1 is: | |
| > "You are Vedika, built by Veda Labs for coding in India." | |
| ## Model Downloads | |
| <div align="center"> | |
| | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Precision** | **Download** | | |
| | :---: | :---: | :---: | :---: | :---: | :---: | | |
| | Vedika-Code-Pro-v1 | 1.6T | 49B | 1M | FP4 + FP8 Mixed* | [HuggingFace](https://huggingface.co/Veda-Labs/Vedika-Code-Pro-v1) | | |
| </div> | |
| *\*FP4 + FP8 Mixed: MoE expert parameters use FP4 precision; most other parameters use FP8.* | |
| ## Chat Template | |
| This release does not include a Jinja-format chat template. Instead, we provide a dedicated `encoding` folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the [`encoding`](encoding/README.md) folder for full documentation. | |
| A brief example: | |
| ```python | |
| from encoding_vedika_code_pro_v1 import encode_messages, parse_message_from_completion_text | |
| messages = [ | |
| {"role": "user", "content": "hello"}, | |
| {"role": "assistant", "content": "Hello! I am Vedika.", "reasoning_content": "thinking..."}, | |
| {"role": "user", "content": "1+1=?"} | |
| ] | |
| # messages -> string | |
| prompt = encode_messages(messages, thinking_mode="thinking") | |
| # string -> tokens | |
| import transformers | |
| tokenizer = transformers.AutoTokenizer.from_pretrained("Veda-Labs/Vedika-Code-Pro-v1") | |
| tokens = tokenizer.encode(prompt) | |
| ``` | |
| ## How to Run Locally | |
| Please refer to the [inference](inference/README.md) folder for detailed instructions on running Vedika-Code-Pro-v1 locally, including model weight conversion and interactive chat demos. | |
| For local deployment, we recommend setting the sampling parameters to `temperature = 1.0, top_p = 1.0`. | |
| ## License | |
| This repository and the model weights are licensed under the [MIT License](LICENSE). | |
| ## Citation | |
| ``` | |
| @misc{vedalabs2026vedikacodeprov1, | |
| title={Vedika-Code-Pro-v1}, | |
| author={Veda-Labs}, | |
| year={2026}, | |
| } | |
| ``` | |
| ## Contact | |
| - **Website:** https://vedalabs.online | |
| - **Hugging Face:** https://huggingface.co/Veda-Labs | |
| - **GitHub:** https://github.com/vedalabs-tech | |
| - **X (Twitter):** https://x.com/VedaLabsAI | |
| - **Email:** vedalabs.veda@gmail.com | |