File size: 3,355 Bytes
034ffc5 085fd2d 034ffc5 085fd2d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
library_name: transformers
license: llama3.1
base_model:
- meta-llama/Llama-3.1-70B-Instruct
---
# This model has been xMADified!
This repository contains [`meta-llama/Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) quantized from 16-bit floats to 4-bit integers, using xMAD.ai proprietary technology.
# Why should I use this model?
1. **Accuracy:** This xMADified model is the **best** quantized version of the `meta-llama/Llama-3.1-70B-Instruct` model (40 GB only). See _Table 1_ below for model quality benchmarks.
2. **Memory-efficiency:** The full-precision model is around 140 GB, while this xMADified model is only around 40 GB, making it feasible to run on one 48 GB GPU.
3. **Fine-tuning**: These models are fine-tunable over the same reduced (48 GB GPUs) hardware in mere 3-clicks. Watch our product demo [here](https://www.youtube.com/watch?v=S0wX32kT90s&list=TLGGL9fvmJ-d4xsxODEwMjAyNA)
## Table 1: xMAD vs. NeuralMagic
| Model | LAMBADA Standard | LAMBADA OpenAI | MMLU | PIQA | WinoGrande |
|---|---|---|---|---|---|
| [xmadai/Llama-3.1-70B-Instruct-xMADai-INT4](https://huggingface.co/xmadai/Llama-3.1-70B-Instruct-xMADai-INT4) (this model) | **72.70** | **76.07** | **81.75** | **83.41** | **78.53** |
| [neuralmagic/Meta-Llama-3.1-70B-Instruct-quantized.w4a16](https://huggingface.co/neuralmagic/Meta-Llama-3.1-70B-Instruct-quantized.w4a16) | 71.51 | 75.24 | 81.71 | 82.43 | 77.82 |
# How to Run Model
Loading the model checkpoint of this xMADified model requires around 40 GB of VRAM. Hence it can be efficiently run on a single 48 GB GPU.
**Package prerequisites**:
1. Run the following *commands to install the required packages.
```bash
pip install torch==2.4.0 # Run following if you have CUDA version 11.8: pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install transformers accelerate optimum
pip install -vvv --no-build-isolation "git+https://github.com/PanQiWei/AutoGPTQ.git@v0.7.1"
```
**Sample Inference Code**
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_id = "xmadai/Llama-3.1-70B-Instruct-xMADai-INT4"
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, use_fast=False)
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=1024)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
# Citation
If you found this model useful, please cite our research paper.
```
@article{zhang2024leanquant,
title={LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid},
author={Zhang, Tianyi and Shrivastava, Anshumali},
journal={arXiv preprint arXiv:2407.10032},
year={2024},
url={https://arxiv.org/abs/2407.10032},
}
```
# Contact Us
For additional xMADified models, access to fine-tuning, and general questions, please contact us at support@xmad.ai and join our waiting list.
|