Upload README.md

README.md

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+---
+base_model: LeoLM/leo-mistral-hessianai-7b-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: apache-2.0
+model_creator: LAION LeoLM
+model_name: Leo Mistral Hessianai 7B Chat
+model_type: mistral
+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 Mistral Hessianai 7B Chat - AWQ
+- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM)
+- Original model: [Leo Mistral Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b-chat)
+
+<!-- description start -->
+## Description
+
+This repo contains AWQ model files for [LAION LeoLM's Leo Mistral Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b-chat).
+
+
+### 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.
+
+It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of Llama AWQ models for high-throughput concurrent inference in multi-user server scenarios.
+
+As of September 25th 2023, preliminary Llama-only AWQ support has also been added to [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference).
+
+Note that, at the time of writing, overall throughput is still lower than running vLLM or TGI with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
+<!-- description end -->
+<!-- repositories-available start -->
+## Repositories available
+
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Leo-Mistral-Hessianai-7B-Chat-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Leo-Mistral-Hessianai-7B-Chat-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Leo-Mistral-Hessianai-7B-Chat-GGUF)
+* [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b-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
+
+For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
+
+Models are released as sharded safetensors files.
+
+| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
+| ------ | ---- | -- | ----------- | ------- | ---- |
+| [main](https://huggingface.co/TheBloke/Leo-Mistral-Hessianai-7B-Chat-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 4096 | 4.15 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-Mistral-Hessianai-7B-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-Mistral-Hessianai-7B-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 -->
+## Serving this model from 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.
+- At the time of writing, vLLM AWQ does not support loading models in bfloat16, so to ensure compatibility with all models, also pass `--dtype float16`.
+
+For example:
+
+```shell
+python3 python -m vllm.entrypoints.api_server --model TheBloke/Leo-Mistral-Hessianai-7B-Chat-AWQ --quantization awq --dtype float16
+```
+
+- When using vLLM from Python code, again pass the `quantization=awq` and `dtype=float16` parameters.
+
+For example:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(model="TheBloke/Leo-Mistral-Hessianai-7B-Chat-AWQ", quantization="awq", dtype="float16")
+
+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 -->
+## Serving this model from 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-Mistral-Hessianai-7B-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 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 -->
+## How to use this AWQ model from Python code
+
+### Install the necessary packages
+
+Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.1 or later
+
+```shell
+pip3 install autoawq
+```
+
+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 .
+```
+
+### You can then try the following example code
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer
+
+model_name_or_path = "TheBloke/Leo-Mistral-Hessianai-7B-Chat-AWQ"
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
+                                          trust_remote_code=False, safetensors=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
+
+prompt = "Tell me about AI"
+prompt_template=f'''<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+'''
+
+print("\n\n*** Generate:")
+
+tokens = tokenizer(
+    prompt_template,
+    return_tensors='pt'
+).input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(
+    tokens,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    max_new_tokens=512
+)
+
+print("Output: ", tokenizer.decode(generation_output[0]))
+
+"""
+# Inference should be possible with transformers pipeline as well in future
+# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
+from transformers import pipeline
+
+print("*** Pipeline:")
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.1
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
+"""
+```
+<!-- 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)
+- [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
+
+<!-- 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
+
+
+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 Mistral Hessianai 7B Chat
+
+# LAION LeoLM: **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 and Mistral. 
+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 three foundation models trained with 8k context length.
+[`LeoLM/leo-mistral-hessianai-7b`](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) under Apache 2.0 and [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
+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-mistral-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-mistral-hessianai-7b` 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": 6.1,
+  "second_turn": 4.7,
+  "categories": {
+      "writing": 6.8,
+      "roleplay": 6.35,
+      "reasoning": 3.3,
+      "math": 2.75,
+      "coding": 4.4,
+      "extraction": 4.5,
+      "stem": 6.85,
+      "humanities": 8.25
+  },
+  "average": 5.4
+}
+```
+
+## Model Details
+
+- **Finetuned from:** [LeoLM/leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** English and German
+- **Demo:** [Web Demo coming soon !]()
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected])
+
+
+## Use in 🤗Transformers
+First install direct dependencies:
+```
+pip install transformers torch sentencepiece
+```
+If you want faster inference using flash-attention2, you need to install these dependencies:
+```bash
+pip install packaging ninja
+pip install flash-attn
+```
+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-mistral-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, use_flash_attention_2=True) # True for flash-attn2 else False
+print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
+```
+
+"*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.*
+
+*In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*"
+
+## 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-mistral-hessianai-7b-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-mistral-hessianai-7b-chat`, developers should
+perform safety testing and tuning tailored to their specific applications of the model.
+
+Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
+
+## Finetuning Details
+
+| Hyperparameter  | Value  |
+|---|---|
+| Num epochs | 4 |
+| Examples per epoch  | 131214  |
+| Global batch size | 256 |
+| Learning rate  |  1e-5 |
+| Warmup steps  |  100 |
+| LR scheduler  |  Cosine |
+| Adam betas  | (0.9, 0.95)  |
+
+
+## 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
+-----------------
+```