quant_stage:
      quant_modifiers:
        QuantizationModifier:
          ignore: [lm_head]
          config_groups:
            group_0:
              weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
              targets: [Linear]

Evaluation Results

The following table shows the evaluation results on various benchmarks compared to the baseline (non-quantized) model.

Task	Baseline Metric (10.0% Threshold)	Quantized Metric	Metric Type
winogrande	0.7119	0.6606	acc,none

How to Use

You can load the quantized model and tokenizer using the transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "NoorNizar/Llama-3.2-3B-Instruct-WINT4"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Example usage (replace with your specific task)
prompt = "Hello, world!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Disclaimer

This model was quantized automatically using a script. Performance and behavior might differ slightly from the original base model.

Compumacy/Llama-3.2-3B-Instruct-WINT4

Llama-3.2-3B-Instruct-WINT4

Quantization Details

Evaluation Results

How to Use

Disclaimer

Compumacy/
Llama-3.2-3B-Instruct-WINT4