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---
library_name: transformers
license: llama3.2
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
# This model has been xMADified!
This repository contains [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) quantized from 16-bit floats to 4-bit integers, using xMAD.ai proprietary technology.
# How to Run Model
Loading the model checkpoint of this xMADified model requires less than 2 GiB of VRAM. Hence it can be efficiently run on most laptop GPUs.
**Package prerequisites**: Run the following commands to install the required packages.
```bash
pip install -q --upgrade transformers accelerate optimum
pip install -q --no-build-isolation auto-gptq
```
**Sample Inference Code**
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_id = "xmadai/Llama-3.2-1B-Instruct-xMADai-4bit"
prompt = [
{"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
{"role": "user", "content": "What's Deep Learning?"},
]
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer.apply_chat_template(
prompt,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
model = AutoGPTQForCausalLM.from_quantized(
model_id,
device_map='auto',
trust_remote_code=True,
)
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
For additional xMADified models, access to fine-tuning, and general questions, please contact us at [email protected] and join our waiting list. |