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")
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", 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

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Commit History

Delete Ternary-Bonsai-27B-mmproj-BF16.gguf f848054 verified

Upload Ternary-Bonsai-27B-mmproj-BF16.gguf d59715b verified

Clean deployment with comprehensive LFS tracking 192ac9f

Delete Ternary-Bonsai-27B-mmproj-BF16.gguf

f848054
verified

Upload Ternary-Bonsai-27B-mmproj-BF16.gguf

d59715b
verified

Clean deployment with comprehensive LFS tracking

192ac9f