Instructions to use madhurithika22/sarvam-30b-compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhurithika22/sarvam-30b-compressed with Transformers:

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

pipe = pipeline("text-generation", model="madhurithika22/sarvam-30b-compressed", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("madhurithika22/sarvam-30b-compressed", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use madhurithika22/sarvam-30b-compressed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "madhurithika22/sarvam-30b-compressed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhurithika22/sarvam-30b-compressed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/madhurithika22/sarvam-30b-compressed

SGLang

How to use madhurithika22/sarvam-30b-compressed 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 "madhurithika22/sarvam-30b-compressed" \
    --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": "madhurithika22/sarvam-30b-compressed",
		"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 "madhurithika22/sarvam-30b-compressed" \
        --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": "madhurithika22/sarvam-30b-compressed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use madhurithika22/sarvam-30b-compressed with Docker Model Runner:
```
docker model run hf.co/madhurithika22/sarvam-30b-compressed
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

1. Methodology & Executive Summary

This submission optimizes the Sarvam-30b baseline for high-performance reasoning within constrained energy envelopes.

Compression Strategy

We employed 4-bit Quantization via Compressed-Tensors to achieve W4A16 precision.

Efficiency Goal

The model aims to achieve the optimal balance between inference accuracy and total energy draw, adhering to the challenge threshold of >80% baseline performance.

Energy Transparency

Power efficiency was prioritized throughout the development cycle to minimize the carbon footprint of inference.

2. Technical Specifications

Base Model: Sarvam-30b
Architecture: SarvamMoEForCausalLM
Precision: W4A16
License: Apache 2.0
Default Context Length: 65,536 tokens

3. Inference & Deployment Configuration

The model is optimized to operate on NVIDIA A100 (80GB) hardware using the vLLM OpenAI API server format.

`vllm_config.yaml`

model: ./models/sarvam-30b-compressed-w4a16
quantization: compressed-tensors
kv-cache-dtype: auto
max-model-len: 8192
trust-remote-code: true

4. Evaluation Framework

This submission follows the rigorous evaluation protocol defined for the Resilient AI Challenge.

Quality Criteria

Performance was validated using LLM-as-a-judge across the following benchmark categories:

Technical Reasoning
Medical Knowledge Synthesis
Linguistic Creativity
Analytical Logic

Energy Monitoring

Our monitoring pipeline utilizes the NVIDIA Management Library (NVML) for real-time GPU power draw, complemented by TDP-relative estimates for CPU load.

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Safetensors

Model size

6B params

Tensor type

I64

I32

F16

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