Instructions to use daksh-neo/sarvam-30b-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daksh-neo/sarvam-30b-quantized with Transformers:

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

pipe = pipeline("text-generation", model="daksh-neo/sarvam-30b-quantized", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("daksh-neo/sarvam-30b-quantized", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use daksh-neo/sarvam-30b-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daksh-neo/sarvam-30b-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daksh-neo/sarvam-30b-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daksh-neo/sarvam-30b-quantized

SGLang

How to use daksh-neo/sarvam-30b-quantized 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 "daksh-neo/sarvam-30b-quantized" \
    --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": "daksh-neo/sarvam-30b-quantized",
		"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 "daksh-neo/sarvam-30b-quantized" \
        --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": "daksh-neo/sarvam-30b-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use daksh-neo/sarvam-30b-quantized with Docker Model Runner:
```
docker model run hf.co/daksh-neo/sarvam-30b-quantized
```

sarvam-30b-quantized / quantization_metadata.json

daksh-neo

Upload Sarvam-30B ultra-quantized model (1-bit HQQ, 4.34GB)

7890a94 verified 5 months ago

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	{
	"model_id": "sarvamai/sarvam-30b",
	"quantization": "Ultra 1-bit (group=256, 8-bit scales)",
	"original_size_gb": 128.61,
	"quantized_size_gb": 4.65,
	"compression_ratio": 27.6,
	"weights_quantized": 7027,
	"total_weights": 7122
	}