sarvam-30b-4bit

Sarvam-30B 4-Bit (BitsAndBytes)

This repository provides a 4-bit NF4 quantized version of the base model sarvamai/sarvam-30b using bitsandbytes. The quantization significantly reduces GPU memory usage while preserving strong inference performance.

Base model sarvamai/sarvam-30b

Architecture SarvamMoEForCausalLM

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Quantization Details

Quantization method: BitsAndBytes 4-bit (NF4)

Configuration used:

• load_in_4bit = True
• bnb_4bit_quant_type = nf4
• bnb_4bit_compute_dtype = float16
• bnb_4bit_use_double_quant = True

Approximate GPU memory usage:

This version is recommended for most users who want to run the model with reduced hardware requirements.

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Installation

Install the required libraries.

pip install transformers accelerate bitsandbytes torch safetensors

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Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "neuralnets/sarvam-30b-4bit",
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "neuralnets/sarvam-30b-4bit",
    trust_remote_code=True
)

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Example Inference

prompt = "Explain mixture of experts in simple terms."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Hardware Requirements

Recommended GPUs:

• A100 40GB or 80GB
• RTX 4090
• RTX 3090 (with offloading)

CPU RAM recommendation:

• 32 GB or higher

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Notes

• This model uses bitsandbytes quantization integrated into Hugging Face Transformers.
• The Sarvam architecture requires trust_remote_code=True.
• Designed primarily for inference workloads.

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Base Model

Original model:

sarvamai/sarvam-30b

Please refer to the base repository for model training details and benchmarks.

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License

This repository distributes a quantized derivative of the original model.

Users must follow the license of the upstream model:

sarvamai/sarvam-30b