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")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Veda-Labs/Vedika-Code-Pro-v1", 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
Vedika-Code-Pro-v1
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
| Model | #Total Params | #Activated Params | Context Length | Precision | Download |
|---|---|---|---|---|---|
| Vedika-Code-Pro-v1 | 1.6T | 49B | 1M | FP4 + FP8 Mixed* | HuggingFace |
*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 folder for full documentation.
A brief example:
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 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.
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
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