Instructions to use Veda-Labs/Vedika-4.1-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Veda-Labs/Vedika-4.1-flash with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Veda-Labs/Vedika-4.1-flash", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Veda-Labs/Vedika-4.1-flash", dtype="auto", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Veda-Labs/Vedika-4.1-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Veda-Labs/Vedika-4.1-flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Veda-Labs/Vedika-4.1-flash

SGLang

How to use Veda-Labs/Vedika-4.1-flash 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-4.1-flash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-4.1-flash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-4.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Veda-Labs/Vedika-4.1-flash with Docker Model Runner:
```
docker model run hf.co/Veda-Labs/Vedika-4.1-flash
```

✨ Vedika 4.1 Flash

🚀 Next-Generation AI by Veda Labs

1M Token Context | Ultra-Low Latency | Built for Long-Horizon Coding | 100% Free

🌟 Introduction

Welcome to Vedika 4.1 Flash, a cutting-edge large language model developed by Veda Labs under the leadership of Divy Patel. Engineered for developers, researchers, and AI enthusiasts, Vedika represents a new paradigm in accessible, high-performance AI.

🔥 Breakthrough Capabilities

💻 Built for Long-Horizon Coding

Vedika is optimized for complex software development workflows:

Multi-file code generation with coherent architecture across entire projects
Full-stack application design from database schemas to frontend components
Advanced multi-script debugging with contextual understanding of interdependent modules

📚 1 Million (1M) Token Context Window

Unprecedented context capacity enables:

Ingestion of massive datasets in a single pass
Analysis of entire code repositories without chunking
Complete API documentation comprehension simultaneously
Long-form document processing and cross-referencing

⚡ Ultra-Low Latency

Engineered for real-time performance:

Blazing-fast inference optimized for production environments
Perfect for real-time AI agents and interactive applications
Sub-second response times for optimal user experience

🆓 100% Free Access

Committed to the developer community:

Completely free with no paywalls or usage limits
Open access for research, commercial, and educational purposes
No hidden costs or premium tiers

🚀 Quick Start / API Access

Get started with Vedika 4.1 Flash using our simple REST API:

cURL Example

curl -X POST "https://vedalabs-vedika-advanced-ai-4-1-flash.hf.space/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vedika-4.1-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms."
      }
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'

Python Example

import requests

url = "https://vedalabs-vedika-advanced-ai-4-1-flash.hf.space/v1/chat/completions"

payload = {
    "model": "vedika-4.1-flash",
    "messages": [
        {"role": "user", "content": "Write a Python function to sort a list."}
    ],
    "temperature": 0.7,
    "max_tokens": 512
}

response = requests.post(url, json=payload)
print(response.json()["choices"][0]["message"]["content"])

📋 Technical Specifications

Specification	Details
Model Name	Vedika 4.1 Flash
Developer	Divy Patel
Organization	Veda Labs
Context Window	1,000,000 tokens
Architecture	Transformer with Flash Attention
Optimization	Long-horizon coding, multi-file generation
Latency	Ultra-low, real-time optimized
License	Apache 2.0
Access	100% Free

🎯 Use Cases

Software Development: Generate, debug, and refactor code across multiple files
Documentation Analysis: Process entire API docs and technical manuals
Research: Analyze large corpora and cross-reference academic papers
AI Agents: Power real-time conversational and task-oriented agents
Education: Free access for students and educators worldwide

⚠️ Limitations

Beta Release: As a newly developed model, edge cases may exist
Specialized Focus: Optimized for coding and long-context tasks; general knowledge may vary
API Dependency: Requires internet connectivity for cloud-based inference

✨ A Product of Veda Labs

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