Llama-2-7b-chat-hf_4bitnogs_hqq

This is an <a href="https://github.com/mobiusml/hqq/">HQQ</a> 4-bit quantized <a href="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"> Llama2-7B-chat model </a> <i>without grouping</i> using a low-rank adapter to improve the performance (referred to as <a href="https://mobiusml.github.io/1bit_blog/">HQQ+</a>).
This model doesn't use grouping to make it compatible with the fast <a href="https://github.com/IST-DASLab/marlin/tree/master/marlin">Marlin</a> inference kernel.

Running quantized models efficiently for inference requires using fused matrix-vector multiplications. The kernels available now have some constraints on the choice of the group-size and the axis along-which quantization is performed. This model doesn't use grouping to make it compatible with all the kernels that operate along axis=1.

Performance

Usage

First, install the latest version of <a href="https://github.com/mobiusml/hqq/">HQQ</a>:

pip install git+https://github.com/mobiusml/hqq.git

Then you can use the sample code below:

import torch
from transformers import AutoTokenizer
from hqq.models.hf.base import AutoHQQHFModel
from hqq.core.quantize import *
from hqq.utils.patching 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-2-7b-chat-hf_4bitnogs_hqq"
model     = AutoHQQHFModel.from_quantized(model_id, cache_dir=cache_dir, compute_dtype=compute_dtype, adapter='adapter_v0.1.lora', device=device).eval();
tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)

#Use optimized inference kernels
###################################################
prepare_for_inference(model, backend=backend) #It takes a while...

#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=None) #Slower generation but no warm-up 
gen = HFGenerator(model, tokenizer, max_new_tokens=1000, do_sample=True, compile="partial").warmup() #Faster generation, but 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)