metadata

base_model: NX-AI/xLSTM-7b
library_name: peft
license: apache-2.0
datasets:
  - vicgalle/alpaca-gpt4
language:
  - en
pipeline_tag: text-generation

Model Card for FlowerTune-xLSTM-7b-NLP-PEFT

This PEFT adapter has been trained by using Flower, a friendly federated AI framework.

The adapter and benchmark results have been submitted to the FlowerTune LLM NLP Leaderboard.

Model Details

Please check the following GitHub project for model details and evaluation results:

https://github.com/mrs83/FlowerTune-xLSTM-7b-NLP

How to Get Started with the Model

First, install xlstm and mlstm_kernels packages:

pip install xlstm
pip install mlstm_kernels

For now, install the transformers repositiory fork from NX-AI (until it is merged):

pip install 'transformers @ git+ssh://[email protected]/NX-AI/transformers.git@integrate_xlstm'

Use this model as:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("NX-AI/xLSTM-7b")
model = PeftModel.from_pretrained(base_model, "mrs83/FlowerTune-xLSTM-7b-NLP-PEFT")

Evaluation Results (Accuracy)

STEM: 13.67 %
Social Sciences: 17.55 %
Humanities: 14.84 %
Average: 15.35 %

Communication Budget

60609.38 Megabytes

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: QuantizationMethod.BITS_AND_BYTES
_load_in_8bit: False
_load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: fp4
bnb_4bit_use_double_quant: False
bnb_4bit_compute_dtype: float32
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False

Framework versions

PEFT 0.14.0
Flower 1.13.0