Upload README.md

README.md

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+---
+base_model: LeoLM/leo-hessianai-70b-chat
+datasets:
+- LeoLM/OpenSchnabeltier
+- OpenAssistant/OASST-DE
+- FreedomIntelligence/alpaca-gpt4-deutsch
+- FreedomIntelligence/evol-instruct-deutsch
+- LeoLM/German_Poems
+- LeoLM/German_Songs
+inference: false
+language:
+- en
+- de
+library_name: transformers
+license: llama2
+model_creator: LAION LeoLM
+model_name: Leo Hessianai 70B Chat
+model_type: llama
+pipeline_tag: text-generation
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
+quantized_by: TheBloke
+---
+<!-- markdownlint-disable MD041 -->
+
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+
+# Leo Hessianai 70B Chat - AWQ
+- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
+- Original model: [Leo Hessianai 70B Chat](https://huggingface.co/LeoLM/leo-hessianai-70b-chat)
+
+<!-- description start -->
+## Description
+
+This repo contains AWQ model files for [LAION LeoLM's Leo Hessianai 70B Chat](https://huggingface.co/LeoLM/leo-hessianai-70b-chat).
+
+These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
+
+
+### About AWQ
+
+AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
+
+AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
+
+It is supported by:
+
+- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
+- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
+- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
+
+<!-- description end -->
+<!-- repositories-available start -->
+## Repositories available
+
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-70B-chat-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-70B-chat-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-70B-chat-GGUF)
+* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-70b-chat)
+<!-- repositories-available end -->
+
+<!-- prompt-template start -->
+## Prompt template: ChatML
+
+```
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+
+```
+
+<!-- prompt-template end -->
+
+
+<!-- README_AWQ.md-provided-files start -->
+## Provided files, and AWQ parameters
+
+I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
+
+Models are released as sharded safetensors files.
+
+| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
+| ------ | ---- | -- | ----------- | ------- | ---- |
+| [main](https://huggingface.co/TheBloke/leo-hessianai-70B-chat-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 8192 | 36.62 GB
+
+<!-- README_AWQ.md-provided-files end -->
+
+<!-- README_AWQ.md-text-generation-webui start -->
+## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
+
+Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+
+It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-70B-chat-AWQ`.
+3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say "Done".
+5. In the top left, click the refresh icon next to **Model**.
+6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-70B-chat-AWQ`
+7. Select **Loader: AutoAWQ**.
+8. Click Load, and the model will load and is now ready for use.
+9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
+10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
+<!-- README_AWQ.md-text-generation-webui end -->
+
+<!-- README_AWQ.md-use-from-vllm start -->
+## Multi-user inference server: vLLM
+
+Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
+
+- Please ensure you are using vLLM version 0.2 or later.
+- When using vLLM as a server, pass the `--quantization awq` parameter.
+
+For example:
+
+```shell
+python3 -m vllm.entrypoints.api_server --model TheBloke/leo-hessianai-70B-chat-AWQ --quantization awq --dtype auto
+```
+
+- When using vLLM from Python code, again set `quantization=awq`.
+
+For example:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Tell me about AI",
+    "Write a story about llamas",
+    "What is 291 - 150?",
+    "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
+]
+prompt_template=f'''<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+'''
+
+prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
+
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(model="TheBloke/leo-hessianai-70B-chat-AWQ", quantization="awq", dtype="auto")
+
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+<!-- README_AWQ.md-use-from-vllm start -->
+
+<!-- README_AWQ.md-use-from-tgi start -->
+## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
+
+Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
+
+Example Docker parameters:
+
+```shell
+--model-id TheBloke/leo-hessianai-70B-chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
+```
+
+Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
+
+```shell
+pip3 install huggingface-hub
+```
+
+```python
+from huggingface_hub import InferenceClient
+
+endpoint_url = "https://your-endpoint-url-here"
+
+prompt = "Tell me about AI"
+prompt_template=f'''<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+'''
+
+client = InferenceClient(endpoint_url)
+response = client.text_generation(prompt,
+                                  max_new_tokens=128,
+                                  do_sample=True,
+                                  temperature=0.7,
+                                  top_p=0.95,
+                                  top_k=40,
+                                  repetition_penalty=1.1)
+
+print(f"Model output: ", response)
+```
+<!-- README_AWQ.md-use-from-tgi end -->
+
+<!-- README_AWQ.md-use-from-python start -->
+## Inference from Python code using Transformers
+
+### Install the necessary packages
+
+- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
+- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
+
+```shell
+pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
+```
+
+Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
+
+If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
+
+```shell
+pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
+```
+
+If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
+
+```shell
+pip3 uninstall -y autoawq
+git clone https://github.com/casper-hansen/AutoAWQ
+cd AutoAWQ
+pip3 install .
+```
+
+### Transformers example code (requires Transformers 4.35.0 and later)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_name_or_path = "TheBloke/leo-hessianai-70B-chat-AWQ"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name_or_path,
+    low_cpu_mem_usage=True,
+    device_map="cuda:0"
+)
+
+# Using the text streamer to stream output one token at a time
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+prompt = "Tell me about AI"
+prompt_template=f'''<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+'''
+
+# Convert prompt to tokens
+tokens = tokenizer(
+    prompt_template,
+    return_tensors='pt'
+).input_ids.cuda()
+
+generation_params = {
+    "do_sample": True,
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "top_k": 40,
+    "max_new_tokens": 512,
+    "repetition_penalty": 1.1
+}
+
+# Generate streamed output, visible one token at a time
+generation_output = model.generate(
+    tokens,
+    streamer=streamer,
+    **generation_params
+)
+
+# Generation without a streamer, which will include the prompt in the output
+generation_output = model.generate(
+    tokens,
+    **generation_params
+)
+
+# Get the tokens from the output, decode them, print them
+token_output = generation_output[0]
+text_output = tokenizer.decode(token_output)
+print("model.generate output: ", text_output)
+
+# Inference is also possible via Transformers' pipeline
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    **generation_params
+)
+
+pipe_output = pipe(prompt_template)[0]['generated_text']
+print("pipeline output: ", pipe_output)
+
+```
+<!-- README_AWQ.md-use-from-python end -->
+
+<!-- README_AWQ.md-compatibility start -->
+## Compatibility
+
+The files provided are tested to work with:
+
+- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
+- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
+- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
+- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
+
+<!-- README_AWQ.md-compatibility end -->
+
+<!-- footer start -->
+<!-- 200823 -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Aemon Algiz.
+
+**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
+
+
+Thank you to all my generous patrons and donaters!
+
+And thank you again to a16z for their generous grant.
+
+<!-- footer end -->
+
+# Original model card: LAION LeoLM's Leo Hessianai 70B Chat
+
+# LAION LeoLM 70b Chat: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
+Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. 
+Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
+Thanks to a compute grant at HessianAI's new supercomputer **42**, we release a series foundation models trained with 8k context length
+under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt). Now, we're finally releasing the
+much anticipated `leo-hessianai-70b`, the largest model of this series based on `Llama-2-70b`.
+With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
+Read our [blog post](https://laion.ai/blog/leo-lm/) or our paper (preprint coming soon) for more details!
+
+
+*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
+
+## LeoLM Chat
+`LeoLM/leo-hessianai-70b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-70b` and finetuned on a selection of German instruction datasets.
+The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
+```
+{
+    "first_turn": 7.2375,
+    "second_turn": 6.5375,
+    "categories": {
+        "writing": 8.55,
+        "roleplay": 7.15,
+        "reasoning": 4.2,
+        "math": 4.85,
+        "coding": 4.85,
+        "extraction": 7.75,
+        "stem": 8.45,
+        "humanities": 9.3
+    },
+    "average": 6.8875
+}
+```
+Have a look at some examples [in this Google Doc](https://docs.google.com/document/d/1SAAikkPAF4oLoFISqE0P1mRL5OUk8l2pI90zZC4bP1E/edit?usp=sharing).
+
+
+## Model Details
+
+- **Finetuned from:** [LeoLM/leo-hessianai-70b](https://huggingface.co/LeoLM/leo-hessianai-70b)
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** English and German
+- **Demo:** [Web Demo]()
+- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
+- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
+
+
+## Use in 🤗Transformers
+First install direct dependencies:
+```
+pip install transformers torch
+```
+Then load the model in transformers:
+```python
+from transformers import pipeline
+import torch
+
+system_prompt = """<|im_start|>system
+Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
+Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
+
+"""
+prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
+
+generator = pipeline(model="LeoLM/leo-hessianai-70b-chat", device="cuda", torch_dtype=torch.float16)
+print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
+```
+
+"*Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren.*"
+
+## Prompting / Prompt Template
+
+Prompt dialogue template (ChatML format):
+
+```
+"""
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+"""
+```
+
+The model input can contain multiple conversation turns between user and assistant, e.g.
+```
+<|im_start|>user
+{prompt 1}<|im_end|>
+<|im_start|>assistant
+{reply 1}<|im_end|>
+<|im_start|>user
+{prompt 2}<|im_end|>
+<|im_start|>assistant
+(...)
+```
+
+## Ethical Considerations and Limitations
+
+LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. 
+For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-70b-chat` cannot be predicted
+in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
+to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-70b-chat`, developers should
+perform safety testing and tuning tailored to their specific applications of the model.
+
+We are aware of the model refusing to answer more often than desired. This will be adressed in future versions. For now, the training
+dataset is equal to that used for our smaller chat variants.
+
+Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
+
+## Finetuning Details
+
+| Hyperparameter  | Value  |
+|---|---|
+| Num epochs | 3 |
+| Examples per epoch  | 131214  |
+| Global batch size | 256 |
+| Learning rate  |  1.5e-5 |
+| Warmup steps  |  15 |
+| LR scheduler  |  Cosine |
+| Adam betas  | (0.9, 0.95)  |
+| Weight Decay | 0.01  |
+
+## Dataset Details
+```
+## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
+-----------------
+  Accepted: 3534/3534 (100.0%)
+  Accepted tokens: 2259302
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 29
+  Max tokens per sample: 2484
+  Avg tokens per sample: 639.3044708545557
+-----------------
+
+## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
+-----------------
+  Accepted: 57841/57841 (100.0%)
+  Accepted tokens: 42958192
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 33
+  Max tokens per sample: 5507
+  Avg tokens per sample: 742.6944900675991
+-----------------
+
+## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
+-----------------
+  Accepted: 48969/48969 (100.0%)
+  Accepted tokens: 13372005
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 19
+  Max tokens per sample: 1359
+  Avg tokens per sample: 273.07082031489307
+-----------------
+
+## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
+-----------------
+  Accepted: 21314/21314 (100.0%)
+  Accepted tokens: 8134690
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 25
+  Max tokens per sample: 1202
+  Avg tokens per sample: 381.65947264708643
+-----------------
+
+## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
+-----------------
+  Accepted: 490/490 (100.0%)
+  Accepted tokens: 618642
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 747
+  Max tokens per sample: 1678
+  Avg tokens per sample: 1262.534693877551
+-----------------
+
+## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
+-----------------
+  Accepted: 392/392 (100.0%)
+  Accepted tokens: 187897
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 231
+  Max tokens per sample: 826
+  Avg tokens per sample: 479.3290816326531
+-----------------
+
+## Stats for 'total' (132540 samples (100.0%))
+-----------------
+  Accepted: 132540/132540 (100.0%)
+  Accepted tokens: 67530728
+  Skipped: 0 (0.0%)
+  Min tokens per sample: 19
+  Max tokens per sample: 5507
+  Avg tokens per sample: 509.51205673758864
+-----------------
+```