metadata
license: llama3.2
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
tags:
- gptqmodel
- modelcloud
- llama3.2
- instruct
- int4
This model has been quantized using GPTQModel.
- bits: 4
- dynamic: null
- group_size: 32
- desc_act: true
- static_groups: false
- sym: true
- lm_head: false
- true_sequential: true
- quant_method: "gptq"
- checkpoint_format: "gptq"
- meta：
- quantizer: gptqmodel:1.1.0
- uri: https://github.com/modelcloud/gptqmodel
- damp_percent: 0.05
- damp_auto_increment: 0.0015
Example:
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/Llama-3.2-1B-Instruct-gptqmodel-4bit-vortex-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPTQModel.from_quantized(model_name)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)