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modelling/quant.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, AwqConfig
+from huggingface_hub import notebook_login, HfApi
+from peft import PeftModel, PeftConfig
+from optimum.gptq import GPTQQuantizer, load_quantized_model
+from accelerate import Accelerator
+import torch
+
+model_id = "mistralai/Mistral-Nemo-Instruct-2407"
+quant_dataset = "c4"
+
+gptq_repo = "Granther/Mistral-Nemo-Instruct-GPTQ"
+awq_repo = "" 
+
+# GPTQ
+gptq_dir = "gptq/"
+
+# AWQ
+awq_dir = "awq/"
+
+accelerator = Accelerator()
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, ignore_mismatched_sizes=True)
+
+model, tokenizer = accelerator.prepare(model, tokenizer)
+
+quantizer = GPTQQuantizer(bits=4, 
+                         dataset="c4", 
+                         group_size=64, # The size of groups to perform quant calcs on
+                         desc_act=True, # Perplexity is better, compute speed is worse
+                         sym=True, # Symetrical quant
+                         true_sequential=True, # 
+                         #block_name_to_quantize = "layers.0",
+                         tokenizer=tokenizer)
+
+print("Made it to quant_model")
+quantized_model = quantizer.quantize_model(model, tokenizer=tokenizer)
+
+tokenizer.save_pretrained(gptq_dir)
+#gptq_config.save_pretrained(gptq_dir)
+quantized_model.save_pretrained(gptq_dir)