---
license: llama3.1
train: false
inference: false
pipeline_tag: text-generation
---
This is an HQQ all 4-bit (group-size=64) quantized Llama3.1-8B-Instruct model.
We provide two versions:
* Calibration-free version: https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq/
* Calibrated version: https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib/
## Model Size
| Models | fp16| HQQ 4-bit/gs-64 | AWQ 4-bit | GPTQ 4-bit |
|:-------------------:|:--------:|:----------------:|:----------------:|:----------------:|
| Bitrate (Linear layers) | 16 | 4.5 | 4.25 | 4.25 |
| VRAM (GB) | 15.7 | 6.1 | 6.3 | 5.7 |
## Model Decoding Speed
| Models | fp16| HQQ 4-bit/gs-64| AWQ 4-bit | GPTQ 4-bit |
|:-------------------:|:--------:|:----------------:|:----------------:|:----------------:|
| Decoding* - short seq (tokens/sec)| 53 | 125 | 67 | 3.7 |
| Decoding* - long seq (tokens/sec)| 50 | 97 | 65 | 21 |
*: RTX 3090
## Performance
| Models | fp16 | HQQ 4-bit/gs-64 (no calib) | HQQ 4-bit/gs-64 (calib) | AWQ 4-bit | GPTQ 4-bit |
|:-------------------:|:--------:|:----------------:|:----------------:|:----------------:|:----------------:|
| ARC (25-shot) | 60.49 | 60.32 | 60.92 | 57.85 | 61.18 |
| HellaSwag (10-shot)| 80.16 | 79.21 | 79.52 | 79.28 | 77.82 |
| MMLU (5-shot) | 68.98 | 67.07 | 67.74 | 67.14 | 67.93 |
| TruthfulQA-MC2 | 54.03 | 53.89 | 54.11 | 51.87 | 53.58 |
| Winogrande (5-shot)| 77.98 | 76.24 | 76.48 | 76.4 | 76.64 |
| GSM8K (5-shot) | 75.44 | 71.27 | 75.36 | 73.47 | 72.25 |
| Average | 69.51 | 68.00 | 69.02 | 67.67 | 68.23 |
| Relative performance | 100% | 97.83% | 99.3% | 97.35% | 98.16% |
You can reproduce the results above via `pip install lm-eval==0.4.3`
## Usage
First, install the dependecies:
```
pip install git+https://github.com/mobiusml/hqq.git #master branch fix
pip install bitblas #if you use the bitblas backend
```
Also, make sure you use at least torch `2.4.0` or the nightly build with at least CUDA 12.1.
Then you can use the sample code below:
``` Python
import torch
from transformers import AutoTokenizer
from hqq.models.hf.base import AutoHQQHFModel
from hqq.utils.patching import *
from hqq.core.quantize import *
from hqq.utils.generation_hf import HFGenerator
#Settings
###################################################
backend = "torchao_int4" #'torchao_int4' #"torchao_int4" (4-bit only) or "bitblas" (4-bit + 2-bit) or "gemlite" (8-bit, 4-bit, 2-bit, 1-bit)
compute_dtype = torch.bfloat16 if backend=="torchao_int4" else torch.float16
device = 'cuda:0'
cache_dir = '.'
#Load the model
###################################################
#model_id = 'mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq' #no calib version
model_id = 'mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib' #calibrated version
model = AutoHQQHFModel.from_quantized(model_id, cache_dir=cache_dir, compute_dtype=compute_dtype, device=device).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)
#Use optimized inference kernels
###################################################
prepare_for_inference(model, backend=backend)
#Generate
###################################################
#For longer context, make sure to allocate enough cache via the cache_size= parameter
gen = HFGenerator(model, tokenizer, max_new_tokens=1000, do_sample=True, compile="partial").warmup() #Warm-up takes a while
gen.generate("Write an essay about large language models", print_tokens=True)
gen.generate("Tell me a funny joke!", print_tokens=True)
gen.generate("How to make a yummy chocolate cake?", print_tokens=True)
```