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---
license: cc-by-sa-3.0
language:
- de
---
# xLSTM Model trained on German Wikipedia
Research & development of an xLSTM model trained on German Wikipedia.
The Flair team is currently working on the integration of xLSTM (both LM training and fine-tuning models for downstream tasks).
For pretraining this xLSTM model, we this [fork](https://github.com/HallerPatrick/helibrunna) (from [Patrick Haller](https://huggingface.co/PatrickHaller)) of the awesome [Helibrunna](https://github.com/AI-Guru/helibrunna) library from [Tristan](https://huggingface.co/TristanBehrens).
Initially, we integrated xLSTM model training into Flair - for more information about this, please refer to the archived [flair-old](https://huggingface.co/stefan-it/xlstm-german-wikipedia/blob/flair-old/README.md) branch of this repository.
# Changelog
- 29.08.2024: Uploaded re-trained model for 1 epoch over complete German Wikipedia corpus. Training was done with gradient clipping (0.25).
- 28.08.2024: Model training is now done with [Helibrunna](https://github.com/AI-Guru/helibrunna) fork - find it [here](https://github.com/HallerPatrick/helibrunna).
- 10.06.2024: Initial version. xLSTM was trained with Flair library, see this [old](https://huggingface.co/stefan-it/xlstm-german-wikipedia/blob/flair-old/README.md) branch.
# Training
The current model was trained with commit `a1b3772` from the [`main` branch](https://github.com/HallerPatrick/helibrunna) of the forked Helibrunna repo.
The `xlstm` [library](https://github.com/NX-AI/xlstm) needs to be installed manually - also check that `pip3 install Ninja` is installed.
The German Wikipedia dump from [this repository](https://huggingface.co/datasets/gwlms/dewiki-20230701-flair-corpus) is used.
The following training configuration is used:
```yaml
description: "Train a wikipedia xLSTM"
training:
model_name: "german_wikipedia"
batch_size: 10
lr: 6e-4
lr_warmup_steps: 4584
lr_decay_until_steps: "auto"
lr_decay_factor: 0.001
weight_decay: 0.1
amp_precision: bfloat16
weight_precision: float32
enable_mixed_precision: true
num_epochs: 1
output_dir: "./output"
save_every_step: 2000
log_every_step: 10
generate_every_step: 5000
wandb_project: "xlstm"
max_grad_norm: 0.25
# wandb_project: "lovecraftxlstm"
model:
num_blocks: 24
embedding_dim: 768
mlstm_block:
mlstm:
num_heads: 4
slstm_block: {}
slstm_at: []
context_length: 512
dataset:
output_path: "./output/german-wikipedia-dataset"
hugging_face_id: ["stefan-it/dewiki-20230701"]
split: "train" # Also subsetting is possible: "train[:100000]"
shuffle: False
seed: 42
tokenizer:
type: "pretrained"
pretrained_class: "LlamaTokenizer"
pretrained_id: "meta-llama/Llama-2-7b-hf"
```
The training loss curve can be seen here:
The uploaded model checkpoint is from 458,431 steps (1 epoch over corpus). Training took 1d 3h 17m 58s on a single RTX 4090.
# Usage
It is possible to use the model to generate some text:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name_or_path = "stefan-it/xlstm-german-wikipedia"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
input_ids = tokenizer.encode("Heute ist schönes Wetter in", return_tensors="pt")
output = model.generate(input_ids, max_length=100, temperature=0.7, do_sample=True)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```
# Caveats
Notice: this model integration is heavily under development. And in the process of finding good hyper-parameters.
Also downstream experiments are coming very soon.
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