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| Property | Value |
|---|---|
| Bits | 4 |
| Group Size | 128 |
| desc_act | False |
| Calibration | wikitext-2 (512 samples) |
1from gptqmodel import GPTQModel
2from transformers import AutoTokenizer
3
4model = GPTQModel.load("atrisaxena/llama3.1-8B-gptq-4bit", device_map="auto")
5tokenizer = AutoTokenizer.from_pretrained("atrisaxena/llama3.1-8B-gptq-4bit")
6
7messages = [{"role": "user", "content": "What is quantization?"}]
8prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
9inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
10
11output = model.generate(**inputs, max_new_tokens=100)
12print(tokenizer.decode(output[0], skip_special_tokens=True))