metadata
language:
- en
license: llama3.1
library_name: transformers
tags:
- autoround
- auto-round
- intel-autoround
- intel
- woq
- meta
- pytorch
- llama
- llama-3
model_name: Llama-3.1 Tulu 3 8B DPO
base_model:
- meta-llama/Llama-3.1-8B
datasets:
- allenai/tulu-3-DPO-mixture
inference: false
model_creator: allenai
pipeline_tag: text-generation
prompt_template: '{prompt} '
quantized_by: fbaldassarri
Model Information
Quantized version of allenai/Llama-3.1-Tulu-3-8B-DPO using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 64
- Symmetrical Quantization
- Method WoQ (AutoRound format)
Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64)
Quantization framework: Intel AutoRound v0.4.6
Note: this INT4 version of Llama-3.1-Tulu-3-8B-DPO has been quantized to run inference through CPU.
Replication Recipe
Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.6.tar.gz
tar -xvzf v0.4.6.tar.gz
cd auto-round-0.4.6
pip install -r requirements-cpu.txt --upgrade
Step 2 Build Intel AutoRound wheel from sources
pip install -vvv --no-build-isolation -e .[cpu]
Step 3 Script for Quantization
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "allenai/Llama-3.1-Tulu-3-8B-DPO"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device = 4, 64, True, 'cpu'
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device)
autoround.quantize()
output_dir = "./AutoRound/allenai_Llama-3.1-Tulu-3-8B-DPO-autoround-int4-gs64-sym"
autoround.save_quantized(output_dir, format='auto_round', inplace=True)
License
Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.