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---
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](https://github.com/ModelCloud/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**: 0.1
- **damp_auto_increment**: 0.0015
## Example:
```python
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/Llama-3.2-1B-Instruct-gptqmodel-4bit-vortex-v2.5"
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)
```
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