Quant code used - R1-Distill-Llama-8B - 4 bit:
4 bit quantization
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
from optimum.gptq import GPTQQuantizer
from auto_round import AutoRound
import torch
Load model and tokenizer
model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
Optimized quantization settings
bits = 4
group_size = 64 # Lower group size can improve speed
sym = True
AutoRound for fast inference
autoround = AutoRound(
model, tokenizer,
nsamples=128,
iters=100,
seqlen=2048,
batch_size=4,
bits=bits,
group_size=group_size,
sym=sym,
faster_kernels=True
)
Perform quantization
autoround.quantize()
Save quantized model
output_dir = "./AutoRound/GPTQ-sym/"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(output_dir, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(output_dir, torch_dtype=torch.float16, device_map="auto")
model.to("cuda") # Move the model to the GPU
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