metadata
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
language:
- en
- fr
- es
- pt
pipeline_tag: text-generation
tags:
- causal-lm
- autoround
- auto-round
- intel-autoround
- woq
- autogptq
- auto-gptq
- gptq
- intel
- pytorch
- falcon3
model_name: Falcon3 1B Base
base_model:
- tiiuae/Falcon3-1B-Base
inference: false
library_name: transformers
model_creator: tiiuae
prompt_template: '{prompt} '
quantized_by: fbaldassarri
Model Information
Quantized version of tiiuae/Falcon3-1B-Base using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoGPTQ
Quantization framework: Intel AutoRound v0.4.4
Note: this INT4 version of Falcon3-1B-Base 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/v0.4.4.tar.gz
tar -xvzf v0.4.4.tar.gz
cd auto-round-0.4.4
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 = "tiiuae/Falcon3-1B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 4, 128, False, 'cpu', False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
autoround.quantize()
output_dir = "./AutoRound/tiiuae_Falcon3-1B-Base-autogptq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
License
Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.