sarvam-eval

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Methodology

This model represents a fine-tuned version of the Sarvam-30B baseline, developed for the Resilient AI Challenge., developed for the Resilient AI Challenge.

The compression strategy utilized "Post-Training Quantization (PTQ) formatted via compressed-tensors to achieve a W4A16 precision balance.

The primary objective was to maximize energy efficiency while ensuring the model maintains at least 80% of the baseline Sarvam-30b performance.

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Model Details

• Base Model: sarvam-30b
• Compression Precision: W4A16
• License: Apache 2.0

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Inference Configuration

The model is optimized to run using the vLLM inference engine.

vllm_config.yaml

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

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Evaluation Metrics

The model has been evaluated against the challenge benchmarks:

• Technical Reasoning: Advanced Science and Mathematics problem-solving.
• Domain-Specific Expertise: Medical knowledge synthesis.
• Linguistic Creativity: Narrative generation in English and Indian languages.
• Analytical Logic: Complex logical reasoning and deductive tasks.
• Energy Monitoring: Power consumption was tracked using the NVIDIA Management Library (NVML) for GPU draw and TDP-relative estimation for CPU load.

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Usage

To serve this model for evaluation, use the following command:

vllm serve --config vllm_config.yaml