# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q2_k-gguf

# Run inference
./main -m sarvam-m-24b-Q2_K.gguf -p "Your prompt here"

With Python (llama-cpp-python)

python

from llama_cpp import Llama

# Load the model
llm = Llama(
    model_path="./sarvam-m-24b-Q2_K.gguf",
    n_ctx=2048,  # Context length
    n_gpu_layers=35,  # Adjust based on your GPU
    verbose=False
)

# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])

With Transformers + AutoGGUF

python

from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM

model_name = "tifin-india/sarvam-m-24b-q2_k-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)

Performance Characteristics

Aspect	Rating
Speed	⭐⭐⭐⭐⭐
Quality	⭐
Memory	⭐⭐⭐⭐⭐

Original Model

This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.

Quantization Details

This model was quantized using llama.cpp's quantization tools. The Q2_K format provides a good balance of model size, inference speed, and output quality for most use cases.

License

This model follows the same license as the original model (Apache 2.0).

Citation

If you use this model, please cite the original model authors and acknowledge the quantization.

tifin-india/sarvam-m-24b-q2-k-gguf

sarvam-m-24b - Q2_K GGUF

Model Details

Usage

With llama.cpp