|
--- |
|
base_model: LeoLM/leo-hessianai-13b-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 13B 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 |
|
--- |
|
|
|
<!-- 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 13B Chat - AWQ |
|
- Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) |
|
- Original model: [Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat) |
|
|
|
<!-- description start --> |
|
## Description |
|
|
|
This repo contains AWQ model files for [LAION LeoLM's Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-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-hessianai-13B-chat-AWQ) |
|
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ) |
|
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-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-13b-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-hessianai-13B-chat-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.25 GB |
|
|
|
<!-- README_AWQ.md-provided-files 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/). |
|
|
|
- When using vLLM as a server, pass the `--quantization awq` parameter, for example: |
|
|
|
```shell |
|
python3 python -m vllm.entrypoints.api_server --model TheBloke/leo-hessianai-13B-chat-AWQ --quantization awq --dtype half |
|
``` |
|
|
|
Note: at the time of writing, vLLM has not yet done a new release with support for the `quantization` parameter. |
|
|
|
If you try the code below and get an error about `quantization` being unrecognised, please install vLLM from Github source. |
|
|
|
When using vLLM from Python code, pass the `quantization=awq` parameter, 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-hessianai-13B-chat-AWQ", quantization="awq", dtype="half") |
|
|
|
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-python start --> |
|
## Serving this model from TGI |
|
|
|
TGI merged support for AWQ on September 25th, 2023. At the time of writing you need to use the `:latest` Docker container: `ghcr.io/huggingface/text-generation-inference:latest` |
|
|
|
Add the parameter `--quantize awq` for AWQ support. |
|
|
|
Example parameters: |
|
```shell |
|
--model-id TheBloke/leo-hessianai-13B-chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 |
|
``` |
|
|
|
## How to use this AWQ model from Python code |
|
|
|
### Install the necessary packages |
|
|
|
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 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-hessianai-13B-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: |
|
|
|
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) |
|
- [vLLM](https://github.com/vllm-project/vllm) |
|
- [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) |
|
|
|
TGI merged AWQ support on September 25th, 2023: [TGI PR #1054](https://github.com/huggingface/text-generation-inference/pull/1054). Use the `:latest` Docker container until the next TGI release is made. |
|
|
|
<!-- 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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov |
|
|
|
|
|
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 13B 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. |
|
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 two foundation models trained with 8k context length, |
|
[`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]() 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-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` 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": 6.525, |
|
"second_turn": 5.15, |
|
"categories": { |
|
"writing": 6.925, |
|
"roleplay": 6.7, |
|
"reasoning": 4.55, |
|
"math": 3.25, |
|
"coding": 3.45, |
|
"extraction": 5.4, |
|
"stem": 7.55, |
|
"humanities": 8.875 |
|
}, |
|
"average": 5.8375 |
|
} |
|
``` |
|
|
|
## Model Details |
|
|
|
- **Finetuned from:** [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b) |
|
- **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:[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==v2.1.1 --no-build-isolation |
|
pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary |
|
``` |
|
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-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False |
|
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-13b-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-13b-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 | 3 | |
|
| Examples per epoch | 131214 | |
|
| Global batch size | 256 | |
|
| Learning rate | 3e-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 |
|
----------------- |
|
``` |
|
|
