Instructions to use Khanjan21/krishna-vaani-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Khanjan21/krishna-vaani-merged with Transformers:

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

pipe = pipeline("text-generation", model="Khanjan21/krishna-vaani-merged")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Khanjan21/krishna-vaani-merged")
model = AutoModelForImageTextToText.from_pretrained("Khanjan21/krishna-vaani-merged")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Khanjan21/krishna-vaani-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Khanjan21/krishna-vaani-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Khanjan21/krishna-vaani-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Khanjan21/krishna-vaani-merged

SGLang

How to use Khanjan21/krishna-vaani-merged 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 "Khanjan21/krishna-vaani-merged" \
    --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": "Khanjan21/krishna-vaani-merged",
		"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 "Khanjan21/krishna-vaani-merged" \
        --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": "Khanjan21/krishna-vaani-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Khanjan21/krishna-vaani-merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Khanjan21/krishna-vaani-merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Khanjan21/krishna-vaani-merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Khanjan21/krishna-vaani-merged to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Khanjan21/krishna-vaani-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use Khanjan21/krishna-vaani-merged with Docker Model Runner:
```
docker model run hf.co/Khanjan21/krishna-vaani-merged
```

krishna-vaani-merged

File size: 1,688 Bytes

d55daa3

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  "audio_ms_per_token": 40,
  "audio_seq_length": 750,
  "feature_extractor": {
    "dither": 0.0,
    "feature_extractor_type": "Gemma4AudioFeatureExtractor",
    "feature_size": 128,
    "fft_length": 512,
    "fft_overdrive": false,
    "frame_length": 320,
    "hop_length": 160,
    "input_scale_factor": 1.0,
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    "return_attention_mask": true,
    "sampling_rate": 16000
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    "do_rescale": true,
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  },
  "image_seq_length": 280,
  "processor_class": "Gemma4Processor",
  "video_processor": {
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "do_sample_frames": true,
    "image_mean": [
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    ],
    "image_std": [
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    "resample": 3,
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    "return_metadata": false,
    "video_processor_type": "Gemma4VideoProcessor"
  }
}