id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TheBloke/SauerkrautLM-7B-HerO-AWQ | TheBloke | 2023-11-29T13:20:14Z | 3 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetune",
"chatml",
"augmentation",
"german",
"en",
"de",
"base_model:VAGOsolutions/SauerkrautLM-7b-HerO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T13:20:14Z | 2023-11-28T22:50:49.000Z | null | null | ---
base_model: VAGOsolutions/SauerkrautLM-7b-HerO
inference: false
language:
- en
- de
library_name: transformers
license: apache-2.0
model_creator: VAGO solutions
model_name: SauerkrautLM 7B HerO
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
tags:
- mistral
- finetune
- chatml
- augmentation
- german
---
<!-- 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 -->
# SauerkrautLM 7B HerO - AWQ
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- description start -->
## Description
This repo contains AWQ model files for [VAGO solutions's SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO).
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.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [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/SauerkrautLM-7B-HerO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- 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/SauerkrautLM-7B-HerO-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 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/SauerkrautLM-7B-HerO-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: `SauerkrautLM-7B-HerO-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/SauerkrautLM-7B-HerO-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/SauerkrautLM-7B-HerO-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/SauerkrautLM-7B-HerO-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/SauerkrautLM-7B-HerO-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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: VAGO solutions's SauerkrautLM 7B HerO
## VAGO solutions SauerkrautLM-7b-HerO
Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology!
Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.**
SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources.
This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
This merge has allowed us to combine the best features of both models, creating an unparalleled synergy.
Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
# Table of Contents
1. [Overview of all Her0 models](#all-hero-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Merge Procedure](#merge-procedure)
3. [Evaluation](#evaluation)
- [GPT4ALL](#gpt4all)
- [Language Model evaluation Harness](#language-model-evaluation-harness)
- [BigBench](#big-bench)
- [MMLU](#mmlu)
- [TruthfulQA](#truthfulqa)
- [MT-Bench (German)](#mt-bench-german)
- [MT-Bench (English)](#mt-bench-english)
- [Additional German Benchmark results](#additional-german-benchmark-results)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All HerO Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-7b-HerO | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-7b-HerO**
- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture
- **Language(s):** English, German
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
### Training Dataset:
SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Merge Procedure:
SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
We applied the gradient SLERP method.
### Prompt Template:
```
<|im_start|>system
Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<|im_end|>
<|im_start|>user
Wie geht es dir?<|im_end|>
<|im_start|>assistant
Mir geht es gut!<|im_end|>
<|im_start|>user
Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
<|im_start|>assistant
```
## Evaluation
### GPT4ALL:
*Compared to relevant German Closed and Open Source models*
### Language Model evaluation Harness:
*Compared to Aleph Alpha Luminous Models*
**performed with newest Language Model Evaluation Harness*
### Big Bench:
**performed with newest Language Model Evaluation Harness*
### MMLU:
*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
### TruthfulQA:
*Compared to OpenAI Models (GPT3.5,GPT4)*
### MT-Bench (German):
```
########## First turn ##########
score
model turn
SauerkrautLM-70b-v1 1 7.25000
SauerkrautLM-7b-HerO <--- 1 6.96875
SauerkrautLM-7b-v1-mistral 1 6.30625
leo-hessianai-13b-chat 1 6.18750
SauerkrautLM-13b-v1 1 6.16250
leo-mistral-hessianai-7b-chat 1 6.15625
Llama-2-70b-chat-hf 1 6.03750
vicuna-13b-v1.5 1 5.80000
SauerkrautLM-7b-v1 1 5.65000
leo-hessianai-7b-chat 1 5.52500
vicuna-7b-v1.5 1 5.42500
Mistral-7B-v0.1 1 5.37500
SauerkrautLM-3b-v1 1 3.17500
Llama-2-7b 1 1.28750
open_llama_3b_v2 1 1.68750
########## Second turn ##########
score
model turn
SauerkrautLM-70b-v1 2 6.83125
SauerkrautLM-7b-HerO <--- 2 6.30625
vicuna-13b-v1.5 2 5.63125
SauerkrautLM-13b-v1 2 5.34375
SauerkrautLM-7b-v1-mistral 2 5.26250
leo-mistral-hessianai-7b-chat 2 4.99375
SauerkrautLM-7b-v1 2 4.73750
leo-hessianai-13b-chat 2 4.71250
vicuna-7b-v1.5 2 4.67500
Llama-2-70b-chat-hf 2 4.66250
Mistral-7B-v0.1 2 4.53750
leo-hessianai-7b-chat 2 2.65000
SauerkrautLM-3b-v1 2 1.98750
open_llama_3b_v2 2 1.22500
Llama-2-7b 2 1.07500
########## Average ##########
score
model
SauerkrautLM-70b-v1 7.040625
SauerkrautLM-7b-HerO <--- 6.637500
SauerkrautLM-7b-v1-mistral 5.784375
SauerkrautLM-13b-v1 5.753125
vicuna-13b-v1.5 5.715625
leo-mistral-hessianai-7b-chat 5.575000
leo-hessianai-13b-chat 5.450000
Llama-2-70b-chat-hf 5.350000
SauerkrautLM-v1-7b 5.193750
vicuna-7b-v1.5 5.050000
Mistral-7B-v0.1 4.956250
leo-hessianai-7b-chat 4.087500
SauerkrautLM-3b-v1 2.581250
open_llama_3b_v2 1.456250
Llama-2-7b 1.181250
```
**performed with the newest FastChat Version*
### MT-Bench (English):
```
########## First turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 1 8.21875
SauerkrautLM-7b-HerO <--- 1 8.03125
Mistral-7B-OpenOrca 1 7.65625
neural-chat-7b-v3-1 1 7.22500
########## Second turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 2 7.1000
SauerkrautLM-7b-HerO <--- 2 6.7875
neural-chat-7b-v3-1 2 6.4000
Mistral-7B-OpenOrca 2 6.1750
########## Average ##########
score
model
OpenHermes-2.5-Mistral-7B 7.659375
SauerkrautLM-7b-HerO <--- 7.409375
Mistral-7B-OpenOrca 6.915625
neural-chat-7b-v3-1 6.812500
```
**performed with the newest FastChat Version*
### Additional German Benchmark results:
*performed with newest Language Model Evaluation Harness
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/SauerkrautLM-7B-HerO-AWQ | [
-0.5023208856582642,
-0.8825690746307373,
0.33672451972961426,
0.04314885288476944,
-0.1379418522119522,
-0.12513868510723114,
0.024971814826130867,
-0.4123072624206543,
0.07007180154323578,
0.4341903626918793,
-0.684611976146698,
-0.4968315660953522,
-0.31878021359443665,
0.00974367279559... |
athirdpath/alpaca-2-13b-english_full-model | athirdpath | 2023-11-29T02:17:53Z | 3 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T02:17:53Z | 2023-11-29T01:41:21.000Z | null | null | ---
license: llama2
---
This is the LORA from iamshnoo/alpaca-2-13b-english applied to TheBloke/Llama-2-13B-fp16. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | athirdpath/alpaca-2-13b-english_full-model | [
-0.16598506271839142,
-0.9685510993003845,
0.09997469931840897,
0.9461082816123962,
-0.6573584675788879,
0.010251426137983799,
0.2547394037246704,
-0.9815194606781006,
1.2888331413269043,
0.6758771538734436,
-1.0255281925201416,
-0.29512351751327515,
-0.8192272782325745,
-0.045079924166202... |
mmenendezg/vit_pneumonia_classifier | mmenendezg | 2023-11-29T01:51:04Z | 3 | 0 | null | [
"keras",
"region:us"
] | 2023-11-29T01:51:04Z | 2023-11-29T01:50:27.000Z | null | null | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Nadam |
| learning_rate | 1.374011446841905e-07 |
| decay | 0.004 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| training_precision | float32 |
| null | keras | null | null | null | null | null | null | null | null | null | null | mmenendezg/vit_pneumonia_classifier | [
-0.4440501928329468,
-0.5627879500389099,
0.21598543226718903,
0.14051343500614166,
-0.48826321959495544,
-0.5321449041366577,
0.06285009533166885,
-0.10833398252725601,
0.04252929985523224,
0.44448888301849365,
-0.5942078232765198,
-0.8607088327407837,
-0.625973641872406,
-0.0178098846226... |
kwplayground/learning-basics | kwplayground | 2023-11-29T02:09:32Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T02:09:32Z | 2023-11-29T02:00:17.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.89 +/- 26.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
import gymnasium
from huggingface_sb3 import load_from_hub, package_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
env = gym.make('LunarLander-v2')
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1000,
batch_size = 64,
n_epochs = 4,
gamma = 0.99,
verbose=1)
model.learn(total_timesteps=10000)
model_name = "ppo-LunarLander-v2"
model.save(model_name)
eval_env = Monitor(gym.make(
"LunarLander-v2"
))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | kwplayground/learning-basics | [
-0.04312475398182869,
-0.33997562527656555,
0.2845441401004791,
0.35907891392707825,
-0.08447656035423279,
-0.18305262923240662,
0.12611214816570282,
-0.11716067045927048,
0.1716822236776352,
0.6059551239013672,
-0.6259660720825195,
-0.41924193501472473,
-0.6151360869407654,
-0.09686260670... |
yukiarimo/yuna-vision | yukiarimo | 2023-11-29T02:40:49Z | 3 | 1 | null | [
"transformers",
"pytorch",
"blip",
"text2text-generation",
"image-captioning",
"image-to-text",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:40:49Z | 2023-11-29T02:39:02.000Z | null | null | ---
pipeline_tag: image-to-text
tags:
- image-captioning
languages:
- en
license: mit
---
# Yuna Vision
An AGI model for Yuna AI
| null | transformers | image-to-text | null | null | null | null | null | null | null | null | null | yukiarimo/yuna-vision | [
0.1464136689901352,
-0.14644089341163635,
0.23658211529254913,
0.328590452671051,
-0.17186681926250458,
0.005158138927072287,
0.9277133941650391,
-0.4173693060874939,
0.7076180577278137,
0.760338306427002,
-0.7028540372848511,
-0.5221015214920044,
-0.6539042592048645,
-0.2069896161556244,
... |
benayas/llama-2-7b-snips_v0 | benayas | 2023-11-29T18:08:07Z | 3 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:08:07Z | 2023-11-29T05:13:42.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | benayas/llama-2-7b-snips_v0 | [
-0.32276490330696106,
-0.22568461298942566,
0.862226128578186,
0.43461498618125916,
-0.5282989740371704,
0.7012966871261597,
0.7915717363357544,
0.07618622481822968,
0.7746026515960693,
0.25632232427597046,
-0.785281777381897,
-0.22573840618133545,
-0.9104479551315308,
0.5715670585632324,
... |
LangChain12/my_awesome_wnut_model | LangChain12 | 2023-11-29T05:49:21Z | 3 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:49:21Z | 2023-11-29T05:20:14.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.4704918032786885
- name: Recall
type: recall
value: 0.2659870250231696
- name: F1
type: f1
value: 0.33984606275902896
- name: Accuracy
type: accuracy
value: 0.9393356419135565
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2839
- Precision: 0.4705
- Recall: 0.2660
- F1: 0.3398
- Accuracy: 0.9393
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2976 | 0.4098 | 0.1937 | 0.2631 | 0.9349 |
| No log | 2.0 | 426 | 0.2839 | 0.4705 | 0.2660 | 0.3398 | 0.9393 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | LangChain12/my_awesome_wnut_model | [
-0.46386247873306274,
-0.6263032555580139,
0.08493367582559586,
0.18392716348171234,
-0.3102344870567322,
-0.2807120084762573,
-0.17807920277118683,
-0.32243019342422485,
0.09528592973947525,
0.17771294713020325,
-0.585451066493988,
-0.7502728700637817,
-0.8346742987632751,
-0.108183816075... |
VinayHajare/a2c-PandaReachDense-v3 | VinayHajare | 2023-11-29T06:02:18Z | 3 | 0 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T06:02:18Z | 2023-11-29T05:57:50.000Z | null | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.20 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | VinayHajare/a2c-PandaReachDense-v3 | [
-0.32333609461784363,
-0.6825422644615173,
-0.02639593556523323,
0.6930292248725891,
0.028159521520137787,
-0.0857580229640007,
0.49541807174682617,
-0.3490144610404968,
0.4173520803451538,
0.6371415853500366,
-0.889333963394165,
-0.49527186155319214,
-0.4364127814769745,
-0.01470290310680... |
rika37/a2c-PandaReachDense-v3 | rika37 | 2023-11-29T06:06:34Z | 3 | 0 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T06:06:34Z | 2023-11-29T06:02:18.000Z | null | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -2.29 +/- 4.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | rika37/a2c-PandaReachDense-v3 | [
-0.32333624362945557,
-0.6825420260429382,
-0.026395972818136215,
0.6930292844772339,
0.02815961465239525,
-0.08575822412967682,
0.4954181909561157,
-0.34901487827301025,
0.4173519015312195,
0.6371418237686157,
-0.8893341422080994,
-0.49527209997177124,
-0.4364127516746521,
-0.014702910557... |
Noveled/test-500 | Noveled | 2023-11-29T06:19:04Z | 3 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | 2023-11-29T06:19:04Z | 2023-11-29T06:19:01.000Z | null | null | ---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | Noveled/test-500 | [
-0.5779396295547485,
-0.5580515265464783,
0.40497368574142456,
0.08317576348781586,
-0.253414124250412,
-0.27545133233070374,
0.06068450212478638,
-0.5384040474891663,
0.04877224564552307,
0.6135933995246887,
-0.7259423136711121,
-0.6298723816871643,
-0.5585345029830933,
-0.079713866114616... |
cottyard/ppo-LunarLander-v2 | cottyard | 2023-11-29T06:27:43Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T06:27:43Z | 2023-11-29T06:27:20.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.66 +/- 17.29
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | cottyard/ppo-LunarLander-v2 | [
-0.0031748120673000813,
-0.3944118618965149,
0.24817678332328796,
0.33905377984046936,
-0.08787564188241959,
0.04007992520928383,
0.5000529885292053,
-0.17607824504375458,
0.2888225317001343,
0.9444824457168579,
-0.6269252300262451,
-0.5120341181755066,
-0.49809563159942627,
-0.27938348054... |
VinayHajare/a2c-PandaPickAndPlace-v3 | VinayHajare | 2023-11-29T07:01:28Z | 3 | 0 | null | [
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T07:01:28Z | 2023-11-29T06:57:06.000Z | null | null | ---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | VinayHajare/a2c-PandaPickAndPlace-v3 | [
-0.2607196867465973,
-0.685429573059082,
-0.04876747354865074,
0.7317578792572021,
0.008595545776188374,
-0.014755365438759327,
0.3949442207813263,
-0.3592028319835663,
0.38726022839546204,
0.5498561263084412,
-0.6759288907051086,
-0.5502520203590393,
-0.4802432060241699,
-0.14786317944526... |
alexsh9999/distilbert-base-uncased-finetuned-emotions | alexsh9999 | 2023-11-29T07:13:11Z | 3 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:13:11Z | 2023-11-29T07:13:00.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotions
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9335
- name: F1
type: f1
value: 0.9339438957548638
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotions
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1589
- Accuracy: 0.9335
- F1: 0.9339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2153 | 1.0 | 250 | 0.1864 | 0.929 | 0.9298 |
| 0.1392 | 2.0 | 500 | 0.1589 | 0.9335 | 0.9339 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alexsh9999/distilbert-base-uncased-finetuned-emotions | [
-0.5621408820152283,
-0.5849125981330872,
0.19427061080932617,
0.3212299048900604,
-0.3853708803653717,
-0.28341642022132874,
-0.19440165162086487,
-0.11309335380792618,
0.1002812311053276,
0.12433741986751556,
-0.8328408002853394,
-0.7364280819892883,
-0.8517698645591736,
-0.0873840376734... |
hkit/gte-large-manual2 | hkit | 2023-11-29T07:28:35Z | 3 | 0 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:28:35Z | 2023-11-29T07:17:40.000Z | null | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5625 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 562,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | null | sentence-transformers | sentence-similarity | null | null | null | null | null | null | null | null | null | hkit/gte-large-manual2 | [
-0.3025243580341339,
-0.8149304389953613,
0.34086623787879944,
0.2927362024784088,
-0.21855893731117249,
-0.4530971646308899,
-0.20976898074150085,
0.08594929426908493,
0.1977926790714264,
0.4427514672279358,
-0.7215589284896851,
-0.695928692817688,
-0.5521104335784912,
-0.0382672175765037... |
learningML/grammarly-finetune | learningML | 2023-11-29T08:22:23Z | 3 | 0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:paws",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:22:23Z | 2023-11-29T07:21:18.000Z | null | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- paws
model-index:
- name: grammarly-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# grammarly-finetune
This model is a fine-tuned version of [learningML/grammarly-finetune](https://huggingface.co/learningML/grammarly-finetune) on the paws dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 0.9027 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | learningML/grammarly-finetune | [
-0.37468892335891724,
-0.7879582643508911,
0.29819223284721375,
0.23571984469890594,
-0.2569788992404938,
-0.4463074803352356,
-0.2289004623889923,
-0.21237510442733765,
0.04014468938112259,
0.2651120126247406,
-0.8401471376419067,
-0.5554085373878479,
-0.6358171701431274,
-0.0984105765819... |
sronger/ko-llm-llama-2-7b-chat2 | sronger | 2023-11-29T07:49:35Z | 3 | 0 | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T07:49:35Z | 2023-11-29T07:24:09.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | sronger/ko-llm-llama-2-7b-chat2 | [
-0.3227651119232178,
-0.22568456828594208,
0.8622261881828308,
0.43461447954177856,
-0.5282989740371704,
0.7012965083122253,
0.7915719747543335,
0.0761861652135849,
0.7746025323867798,
0.25632235407829285,
-0.7852817177772522,
-0.22573819756507874,
-0.9104477763175964,
0.5715669393539429,
... |
Ransaka/whisper-tiny-sinhala-20k-8k-steps | Ransaka | 2023-11-29T17:36:27Z | 3 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:36:27Z | 2023-11-29T07:48:42.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Ransaka/whisper-tiny-sinhala-20k-8k-steps | [
-0.322765052318573,
-0.22568443417549133,
0.862225353717804,
0.43461543321609497,
-0.5282990336418152,
0.7012964487075806,
0.7915717363357544,
0.07618646323680878,
0.7746022939682007,
0.25632232427597046,
-0.7852814197540283,
-0.2257380485534668,
-0.9104474782943726,
0.5715667009353638,
... |
SamJu3/haerin-model-lora40with-ssd_50 | SamJu3 | 2023-11-29T08:58:39Z | 3 | 0 | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:segmind/SSD-1B",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T08:58:39Z | 2023-11-29T07:53:19.000Z | null | null |
---
license: creativeml-openrail-m
base_model: segmind/SSD-1B
dataset: /home/cora3/vscode_project/SweetBrothers/kohya_ss/images/train/haerin
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - SamJu3/haerin-model-lora40with-ssd_50
These are LoRA adaption weights for segmind/SSD-1B. The weights were fine-tuned on the /home/cora3/vscode_project/SweetBrothers/kohya_ss/images/train/haerin dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | SamJu3/haerin-model-lora40with-ssd_50 | [
-0.11034772545099258,
-0.7675151228904724,
0.2560543417930603,
0.2878079116344452,
-0.6147946119308472,
-0.33871719241142273,
0.08138922601938248,
-0.35123559832572937,
0.604668140411377,
0.8201937675476074,
-0.720379650592804,
-0.49759915471076965,
-0.7273515462875366,
-0.1087142080068588... |
sciencejiho/save_trained_llama2 | sciencejiho | 2023-11-29T08:12:59Z | 3 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T08:12:59Z | 2023-11-29T08:10:55.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
| null | peft | null | null | null | null | null | null | null | null | null | null | sciencejiho/save_trained_llama2 | [
-0.6108308434486389,
-0.7736405730247498,
0.45678073167800903,
0.48175248503685,
-0.5534147024154663,
0.106610007584095,
0.159415140748024,
-0.18475507199764252,
-0.12979134917259216,
0.43808919191360474,
-0.591006875038147,
-0.11884382367134094,
-0.4750036299228668,
0.16232258081436157,
... |
sanjit23/ca | sanjit23 | 2023-11-29T08:39:57Z | 3 | 0 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:39:57Z | 2023-11-29T08:36:28.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | sanjit23/ca | [
-0.322765052318573,
-0.22568443417549133,
0.862225353717804,
0.43461543321609497,
-0.5282990336418152,
0.7012964487075806,
0.7915717363357544,
0.07618646323680878,
0.7746022939682007,
0.25632232427597046,
-0.7852814197540283,
-0.2257380485534668,
-0.9104474782943726,
0.5715667009353638,
... |
TheBloke/psyonic-cetacean-20B-AWQ | TheBloke | 2023-11-29T13:58:28Z | 3 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"base_model:jebcarter/psyonic-cetacean-20B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T13:58:28Z | 2023-11-29T09:06:45.000Z | null | null | ---
base_model: jebcarter/psyonic-cetacean-20B
inference: false
license: other
license_name: microsoft-research-license
model_creator: Jeb Carter
model_name: Psyonic Cetacean 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
<!-- 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 -->
# Psyonic Cetacean 20B - AWQ
- Model creator: [Jeb Carter](https://huggingface.co/jebcarter)
- Original model: [Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Jeb Carter's Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B).
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.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [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/psyonic-cetacean-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF)
* [Jeb Carter's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jebcarter/psyonic-cetacean-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Jeb Carter's Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B).
<!-- licensing 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/psyonic-cetacean-20B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.87 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/psyonic-cetacean-20B-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: `psyonic-cetacean-20B-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/psyonic-cetacean-20B-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/psyonic-cetacean-20B-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/psyonic-cetacean-20B-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
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/psyonic-cetacean-20B-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# 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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jeb Carter's Psyonic Cetacean 20B
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model.
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/psyonic-cetacean-20B-AWQ | [
-0.5609463453292847,
-0.7927747964859009,
0.4151010811328888,
0.20184136927127838,
-0.3523472845554352,
-0.08177893608808517,
-0.06596991419792175,
-0.5730018019676208,
0.14772896468639374,
0.5356282591819763,
-0.7093439102172852,
-0.5137709379196167,
-0.33251699805259705,
0.01212405972182... |
wataruew/bert-base-japanese-v3-jnli | wataruew | 2023-11-29T10:17:58Z | 3 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:17:58Z | 2023-11-29T09:20:29.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | wataruew/bert-base-japanese-v3-jnli | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
MadzM/ppo-LunarLander-v2 | MadzM | 2023-11-29T09:30:27Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T09:30:27Z | 2023-11-29T09:28:52.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.89 +/- 21.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | MadzM/ppo-LunarLander-v2 | [
-0.003174922661855817,
-0.39441171288490295,
0.24817690253257751,
0.3390539288520813,
-0.08787579834461212,
0.04008012264966965,
0.5000532865524292,
-0.176078662276268,
0.28882256150245667,
0.944482684135437,
-0.6269251704216003,
-0.5120342373847961,
-0.4980957806110382,
-0.279383569955825... |
wesley7137/OpenHermes-2.5-neural-chat-7b-v3-1-7B-sharded | wesley7137 | 2023-11-30T00:44:20Z | 3 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:44:20Z | 2023-11-29T09:36:19.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | wesley7137/OpenHermes-2.5-neural-chat-7b-v3-1-7B-sharded | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
sanjit23/testsdfg | sanjit23 | 2023-11-29T09:48:06Z | 3 | 0 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:48:06Z | 2023-11-29T09:40:06.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | sanjit23/testsdfg | [
-0.3227651119232178,
-0.22568456828594208,
0.8622261881828308,
0.43461447954177856,
-0.5282989740371704,
0.7012965083122253,
0.7915719747543335,
0.0761861652135849,
0.7746025323867798,
0.25632235407829285,
-0.7852817177772522,
-0.22573819756507874,
-0.9104477763175964,
0.5715669393539429,
... |
Nighter/QA_wiki_data_roberta_base_short_answer | Nighter | 2023-11-29T13:39:56Z | 3 | 0 | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:39:56Z | 2023-11-29T09:58:32.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: Nighter/QA_wiki_data_roberta_base_short_answer
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Nighter/QA_wiki_data_roberta_base_short_answer
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7150
- Validation Loss: 0.9199
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10434, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.2406 | 0.9512 | 0 |
| 0.8306 | 0.9199 | 1 |
| 0.7150 | 0.9199 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.13.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | Nighter/QA_wiki_data_roberta_base_short_answer | [
-0.5701431035995483,
-0.7258662581443787,
0.3762865960597992,
-0.030833285301923752,
-0.37471336126327515,
-0.3613339364528656,
-0.24028925597667694,
-0.2243942767381668,
0.09313834458589554,
0.2508624196052551,
-0.8138180375099182,
-0.6548275351524353,
-0.8330104947090149,
-0.213550016283... |
Puluming/AISquare-Instruct-llama2-koen-13b-v0.9.2 | Puluming | 2023-11-29T10:43:48Z | 3 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:43:48Z | 2023-11-29T10:22:52.000Z | null | null | ---
license: cc-by-nc-sa-4.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Puluming/AISquare-Instruct-llama2-koen-13b-v0.9.2 | [
-0.12853394448757172,
-0.1861671805381775,
0.6529130339622498,
0.49436283111572266,
-0.1931932270526886,
0.23607474565505981,
0.3607197403907776,
0.05056331306695938,
0.5793652534484863,
0.7400139570236206,
-0.6508102416992188,
-0.23783963918685913,
-0.7102248668670654,
-0.0478258728981018... |
tizayi/ppo-LunarLander-v2 | tizayi | 2023-11-29T11:15:35Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T11:15:35Z | 2023-11-29T11:15:15.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.38 +/- 20.48
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | tizayi/ppo-LunarLander-v2 | [
-0.003174568060785532,
-0.3944118022918701,
0.24817675352096558,
0.3390538692474365,
-0.08787596970796585,
0.04007981717586517,
0.500053346157074,
-0.17607858777046204,
0.28882235288619995,
0.944482684135437,
-0.6269252300262451,
-0.5120340585708618,
-0.49809592962265015,
-0.27938362956047... |
BenLearningRL/a2c-PandaReachDense-v3 | BenLearningRL | 2023-11-29T12:17:38Z | 3 | 0 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T12:17:38Z | 2023-11-29T12:13:10.000Z | null | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.14 +/- 0.06
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | BenLearningRL/a2c-PandaReachDense-v3 | [
-0.323336124420166,
-0.6825424432754517,
-0.026396198198199272,
0.6930294632911682,
0.028159767389297485,
-0.08575843274593353,
0.495417982339859,
-0.34901466965675354,
0.4173518121242523,
0.6371418833732605,
-0.8893340229988098,
-0.4952719807624817,
-0.43641260266304016,
-0.01470296271145... |
peldrak/segformer-finetuned-riviera2 | peldrak | 2023-11-29T13:05:53Z | 3 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:peldrak/segformer-finetuned-coasts-final",
"license:other",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:05:53Z | 2023-11-29T12:28:02.000Z | null | null | ---
license: other
base_model: peldrak/segformer-finetuned-coasts-final
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-finetuned-riviera2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-riviera2
This model is a fine-tuned version of [peldrak/segformer-finetuned-coasts-final](https://huggingface.co/peldrak/segformer-finetuned-coasts-final) on the peldrak/riviera_labeled_split2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2172
- Mean Iou: 0.5684
- Mean Accuracy: 0.7041
- Overall Accuracy: 0.9037
- Accuracy Water: 0.9782
- Accuracy Whitewater: 0.0031
- Accuracy Sand: 0.9694
- Accuracy Rocky Terrain: 0.8474
- Accuracy Agricultural: 0.8818
- Accuracy Vegetation: 0.9453
- Accuracy Road: 0.5085
- Accuracy Development: 0.7910
- Accuracy Other Natural Terrain: 0.4118
- Accuracy Unknown: nan
- Iou Water: 0.9541
- Iou Whitewater: 0.0031
- Iou Sand: 0.8472
- Iou Rocky Terrain: 0.7939
- Iou Agricultural: 0.7881
- Iou Vegetation: 0.8610
- Iou Road: 0.4506
- Iou Development: 0.6761
- Iou Other Natural Terrain: 0.3104
- Iou Unknown: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sand | Accuracy Rocky Terrain | Accuracy Agricultural | Accuracy Vegetation | Accuracy Road | Accuracy Development | Accuracy Other Natural Terrain | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sand | Iou Rocky Terrain | Iou Agricultural | Iou Vegetation | Iou Road | Iou Development | Iou Other Natural Terrain | Iou Unknown |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:-------------:|:--------------------:|:------------------------------:|:----------------:|:---------:|:--------------:|:--------:|:-----------------:|:----------------:|:--------------:|:--------:|:---------------:|:-------------------------:|:-----------:|
| 1.6151 | 0.24 | 20 | 1.4156 | 0.0850 | 0.1749 | 0.3514 | 0.9391 | 0.0113 | 0.0 | 0.1553 | 0.0248 | 0.1658 | 0.0035 | 0.1890 | 0.0857 | nan | 0.3854 | 0.0012 | 0.0 | 0.1345 | 0.0110 | 0.1615 | 0.0017 | 0.1034 | 0.0518 | 0.0 |
| 1.3149 | 0.49 | 40 | 1.0378 | 0.2239 | 0.3363 | 0.6043 | 0.9571 | 0.0318 | 0.2679 | 0.5472 | 0.0248 | 0.6780 | 0.0 | 0.4390 | 0.0811 | nan | 0.6123 | 0.0184 | 0.2520 | 0.5089 | 0.0141 | 0.5632 | 0.0 | 0.2247 | 0.0456 | 0.0 |
| 1.1554 | 0.73 | 60 | 0.7989 | 0.3289 | 0.4401 | 0.7722 | 0.9818 | 0.0 | 0.8295 | 0.6482 | 0.0581 | 0.9606 | 0.0008 | 0.4676 | 0.0146 | nan | 0.8261 | 0.0 | 0.7142 | 0.6253 | 0.0523 | 0.7241 | 0.0008 | 0.3334 | 0.0130 | 0.0 |
| 1.0181 | 0.98 | 80 | 0.6544 | 0.3592 | 0.4747 | 0.8043 | 0.9692 | 0.0 | 0.8957 | 0.7580 | 0.3678 | 0.9693 | 0.0001 | 0.3088 | 0.0032 | nan | 0.9121 | 0.0 | 0.6664 | 0.7171 | 0.2835 | 0.7605 | 0.0001 | 0.2489 | 0.0031 | 0.0 |
| 1.119 | 1.22 | 100 | 0.5360 | 0.3739 | 0.4880 | 0.8252 | 0.9764 | 0.0 | 0.9123 | 0.7932 | 0.5894 | 0.9776 | 0.0 | 0.1422 | 0.0008 | nan | 0.9300 | 0.0 | 0.7082 | 0.7339 | 0.4630 | 0.7700 | 0.0 | 0.1329 | 0.0008 | 0.0 |
| 0.8191 | 1.46 | 120 | 0.4732 | 0.4115 | 0.5270 | 0.8487 | 0.9860 | 0.0 | 0.8930 | 0.8134 | 0.7485 | 0.9750 | 0.0 | 0.3256 | 0.0019 | nan | 0.9360 | 0.0 | 0.7421 | 0.7490 | 0.6024 | 0.8003 | 0.0 | 0.2832 | 0.0018 | 0.0 |
| 0.7274 | 1.71 | 140 | 0.4744 | 0.4010 | 0.5172 | 0.8459 | 0.9847 | 0.0 | 0.8945 | 0.7845 | 0.7891 | 0.9754 | 0.0 | 0.2258 | 0.0011 | nan | 0.9485 | 0.0 | 0.7668 | 0.7512 | 0.5327 | 0.8076 | 0.0 | 0.2025 | 0.0011 | 0.0 |
| 0.3963 | 1.95 | 160 | 0.4212 | 0.4143 | 0.5289 | 0.8491 | 0.9835 | 0.0 | 0.9249 | 0.8669 | 0.6654 | 0.9796 | 0.0 | 0.3312 | 0.0084 | nan | 0.9514 | 0.0 | 0.7786 | 0.7736 | 0.5481 | 0.7861 | 0.0 | 0.2971 | 0.0082 | 0.0 |
| 0.8763 | 2.2 | 180 | 0.3832 | 0.4210 | 0.5390 | 0.8587 | 0.9798 | 0.0 | 0.9471 | 0.7914 | 0.9032 | 0.9688 | 0.0 | 0.2596 | 0.0012 | nan | 0.9483 | 0.0 | 0.8401 | 0.7517 | 0.6183 | 0.8151 | 0.0 | 0.2348 | 0.0012 | 0.0 |
| 0.868 | 2.44 | 200 | 0.3764 | 0.4061 | 0.5216 | 0.8472 | 0.9793 | 0.0 | 0.9666 | 0.8200 | 0.7151 | 0.9764 | 0.0 | 0.2267 | 0.0103 | nan | 0.9514 | 0.0 | 0.6926 | 0.7764 | 0.6398 | 0.7901 | 0.0 | 0.2007 | 0.0102 | 0.0 |
| 0.7492 | 2.68 | 220 | 0.3502 | 0.4267 | 0.5626 | 0.8629 | 0.9742 | 0.0 | 0.9717 | 0.8473 | 0.9721 | 0.9408 | 0.0 | 0.3376 | 0.0200 | nan | 0.9493 | 0.0 | 0.7071 | 0.7902 | 0.6701 | 0.8511 | 0.0 | 0.2796 | 0.0192 | 0.0 |
| 0.9957 | 2.93 | 240 | 0.3382 | 0.4572 | 0.5778 | 0.8688 | 0.9842 | 0.0 | 0.9534 | 0.8633 | 0.7888 | 0.9593 | 0.0 | 0.5910 | 0.0602 | nan | 0.9527 | 0.0 | 0.8231 | 0.8012 | 0.6499 | 0.8112 | 0.0 | 0.4790 | 0.0545 | 0.0 |
| 0.416 | 3.17 | 260 | 0.3426 | 0.4475 | 0.5700 | 0.8617 | 0.9725 | 0.0 | 0.9648 | 0.8810 | 0.7076 | 0.9640 | 0.0 | 0.5665 | 0.0738 | nan | 0.9499 | 0.0 | 0.7928 | 0.7900 | 0.6095 | 0.8113 | 0.0 | 0.4584 | 0.0628 | 0.0 |
| 0.3574 | 3.41 | 280 | 0.3294 | 0.4534 | 0.5701 | 0.8659 | 0.9835 | 0.0 | 0.9498 | 0.7931 | 0.7716 | 0.9674 | 0.0 | 0.6177 | 0.0481 | nan | 0.9441 | 0.0 | 0.8365 | 0.7578 | 0.6388 | 0.8089 | 0.0 | 0.5038 | 0.0438 | 0.0 |
| 0.2504 | 3.66 | 300 | 0.3045 | 0.4381 | 0.5590 | 0.8596 | 0.9787 | 0.0 | 0.9376 | 0.8223 | 0.8269 | 0.9575 | 0.0 | 0.4316 | 0.0765 | nan | 0.9492 | 0.0 | 0.7856 | 0.7767 | 0.6163 | 0.8214 | 0.0 | 0.3681 | 0.0637 | 0.0 |
| 0.3342 | 3.9 | 320 | 0.3037 | 0.4675 | 0.5986 | 0.8712 | 0.9712 | 0.0 | 0.9625 | 0.9100 | 0.7707 | 0.9492 | 0.0 | 0.7293 | 0.0942 | nan | 0.9492 | 0.0 | 0.8752 | 0.7814 | 0.6541 | 0.8159 | 0.0 | 0.5153 | 0.0836 | 0.0 |
| 0.7272 | 4.15 | 340 | 0.3025 | 0.4617 | 0.5795 | 0.8694 | 0.9742 | 0.0 | 0.9665 | 0.8396 | 0.7763 | 0.9678 | 0.0 | 0.6223 | 0.0689 | nan | 0.9486 | 0.0 | 0.8327 | 0.7980 | 0.6451 | 0.8157 | 0.0 | 0.5163 | 0.0605 | 0.0 |
| 0.452 | 4.39 | 360 | 0.2799 | 0.4589 | 0.5891 | 0.8688 | 0.9723 | 0.0 | 0.9796 | 0.8554 | 0.8382 | 0.9439 | 0.0 | 0.5294 | 0.1836 | nan | 0.9510 | 0.0 | 0.7637 | 0.7978 | 0.6793 | 0.8367 | 0.0 | 0.4202 | 0.1405 | 0.0 |
| 0.2372 | 4.63 | 380 | 0.2749 | 0.4642 | 0.5891 | 0.8765 | 0.9755 | 0.0 | 0.9684 | 0.8436 | 0.9190 | 0.9569 | 0.0 | 0.5282 | 0.1100 | nan | 0.9493 | 0.0 | 0.7809 | 0.7960 | 0.7737 | 0.8389 | 0.0 | 0.4060 | 0.0969 | 0.0 |
| 1.3141 | 4.88 | 400 | 0.2875 | 0.4723 | 0.6004 | 0.8726 | 0.9700 | 0.0 | 0.9573 | 0.8714 | 0.7878 | 0.9529 | 0.0 | 0.7587 | 0.1054 | nan | 0.9449 | 0.0 | 0.8715 | 0.7964 | 0.6752 | 0.8177 | 0.0 | 0.5243 | 0.0929 | 0.0 |
| 0.605 | 5.12 | 420 | 0.2752 | 0.4653 | 0.6031 | 0.8722 | 0.9752 | 0.0 | 0.9356 | 0.8359 | 0.9082 | 0.9332 | 0.0010 | 0.7404 | 0.0986 | nan | 0.9462 | 0.0 | 0.8710 | 0.7879 | 0.6188 | 0.8482 | 0.0010 | 0.4905 | 0.0892 | 0.0 |
| 0.3456 | 5.37 | 440 | 0.2907 | 0.4721 | 0.5955 | 0.8717 | 0.9761 | 0.0 | 0.9655 | 0.8499 | 0.7721 | 0.9535 | 0.0 | 0.6785 | 0.1641 | nan | 0.9531 | 0.0 | 0.8718 | 0.7986 | 0.6161 | 0.8207 | 0.0 | 0.5316 | 0.1291 | 0.0 |
| 0.4065 | 5.61 | 460 | 0.2588 | 0.4707 | 0.6035 | 0.8751 | 0.9735 | 0.0 | 0.9728 | 0.8768 | 0.8025 | 0.9485 | 0.0 | 0.5470 | 0.3100 | nan | 0.9528 | 0.0 | 0.7724 | 0.8075 | 0.6647 | 0.8490 | 0.0 | 0.4260 | 0.2346 | 0.0 |
| 0.5515 | 5.85 | 480 | 0.2509 | 0.4817 | 0.6075 | 0.8803 | 0.9607 | 0.0 | 0.9654 | 0.8643 | 0.8910 | 0.9597 | 0.0000 | 0.6962 | 0.1303 | nan | 0.9444 | 0.0 | 0.8502 | 0.7854 | 0.7400 | 0.8362 | 0.0000 | 0.5515 | 0.1096 | 0.0 |
| 0.7913 | 6.1 | 500 | 0.2392 | 0.4835 | 0.6074 | 0.8854 | 0.9772 | 0.0 | 0.9578 | 0.8821 | 0.9384 | 0.9594 | 0.0244 | 0.6102 | 0.1169 | nan | 0.9511 | 0.0 | 0.8279 | 0.8049 | 0.7936 | 0.8365 | 0.0244 | 0.4897 | 0.1072 | 0.0 |
| 0.3186 | 6.34 | 520 | 0.2556 | 0.4736 | 0.6009 | 0.8775 | 0.9769 | 0.0 | 0.9765 | 0.8601 | 0.9007 | 0.9478 | 0.0386 | 0.5438 | 0.1636 | nan | 0.9521 | 0.0 | 0.7805 | 0.8029 | 0.7483 | 0.8328 | 0.0385 | 0.4392 | 0.1420 | 0.0 |
| 0.2549 | 6.59 | 540 | 0.2342 | 0.5138 | 0.6555 | 0.8909 | 0.9748 | 0.0 | 0.9533 | 0.8514 | 0.8781 | 0.9276 | 0.0280 | 0.7772 | 0.5094 | nan | 0.9512 | 0.0 | 0.8886 | 0.7891 | 0.7547 | 0.8588 | 0.0279 | 0.5303 | 0.3371 | 0.0 |
| 0.3034 | 6.83 | 560 | 0.2574 | 0.4892 | 0.6058 | 0.8794 | 0.9819 | 0.0 | 0.9563 | 0.7993 | 0.7777 | 0.9666 | 0.0356 | 0.7305 | 0.2041 | nan | 0.9505 | 0.0 | 0.8925 | 0.7697 | 0.6993 | 0.8226 | 0.0355 | 0.5656 | 0.1561 | 0.0 |
| 0.2759 | 7.07 | 580 | 0.2417 | 0.5055 | 0.6355 | 0.8899 | 0.9735 | 0.0 | 0.9789 | 0.8397 | 0.9129 | 0.9483 | 0.0845 | 0.6354 | 0.3465 | nan | 0.9530 | 0.0 | 0.8171 | 0.7897 | 0.7572 | 0.8530 | 0.0840 | 0.5311 | 0.2698 | 0.0 |
| 0.4661 | 7.32 | 600 | 0.2272 | 0.5198 | 0.6647 | 0.8944 | 0.9787 | 0.0 | 0.9780 | 0.8505 | 0.8440 | 0.9286 | 0.0697 | 0.6809 | 0.6515 | nan | 0.9552 | 0.0 | 0.8088 | 0.7960 | 0.7438 | 0.8682 | 0.0691 | 0.5247 | 0.4328 | 0.0 |
| 0.1629 | 7.56 | 620 | 0.2331 | 0.5248 | 0.6639 | 0.8916 | 0.9782 | 0.0 | 0.9591 | 0.8574 | 0.8627 | 0.9262 | 0.1268 | 0.7364 | 0.5286 | nan | 0.9539 | 0.0 | 0.8723 | 0.8011 | 0.6613 | 0.8493 | 0.1252 | 0.5734 | 0.4116 | 0.0 |
| 0.1212 | 7.8 | 640 | 0.2431 | 0.5138 | 0.6604 | 0.8841 | 0.9727 | 0.0 | 0.9811 | 0.8664 | 0.7576 | 0.9201 | 0.1052 | 0.7484 | 0.5919 | nan | 0.9543 | 0.0 | 0.8160 | 0.7970 | 0.6137 | 0.8406 | 0.1039 | 0.5809 | 0.4314 | 0.0 |
| 0.4444 | 8.05 | 660 | 0.2277 | 0.5174 | 0.6470 | 0.8938 | 0.9807 | 0.0 | 0.9616 | 0.8141 | 0.9088 | 0.9461 | 0.0907 | 0.7961 | 0.3247 | nan | 0.9518 | 0.0 | 0.8687 | 0.7832 | 0.7780 | 0.8490 | 0.0901 | 0.5943 | 0.2586 | 0.0 |
| 0.2176 | 8.29 | 680 | 0.2123 | 0.5327 | 0.6734 | 0.9001 | 0.9774 | 0.0 | 0.9701 | 0.8768 | 0.8724 | 0.9365 | 0.1111 | 0.8156 | 0.5004 | nan | 0.9541 | 0.0 | 0.8617 | 0.8040 | 0.7823 | 0.8657 | 0.1092 | 0.5785 | 0.3715 | 0.0 |
| 0.4515 | 8.54 | 700 | 0.2545 | 0.5198 | 0.6586 | 0.8886 | 0.9852 | 0.0 | 0.9652 | 0.8208 | 0.9443 | 0.9214 | 0.2593 | 0.7064 | 0.3252 | nan | 0.9517 | 0.0 | 0.8370 | 0.7896 | 0.6680 | 0.8531 | 0.2441 | 0.5716 | 0.2833 | 0.0 |
| 0.2276 | 8.78 | 720 | 0.2427 | 0.5161 | 0.6488 | 0.8869 | 0.9730 | 0.0 | 0.9757 | 0.8662 | 0.8668 | 0.9466 | 0.2834 | 0.7261 | 0.2011 | nan | 0.9521 | 0.0 | 0.8300 | 0.8106 | 0.7177 | 0.8432 | 0.2632 | 0.5765 | 0.1681 | 0.0 |
| 0.1664 | 9.02 | 740 | 0.2403 | 0.5174 | 0.6477 | 0.8924 | 0.9731 | 0.0 | 0.9722 | 0.8425 | 0.8760 | 0.9551 | 0.1686 | 0.7817 | 0.2597 | nan | 0.9516 | 0.0 | 0.8776 | 0.7910 | 0.7536 | 0.8527 | 0.1648 | 0.5767 | 0.2062 | 0.0 |
| 0.1256 | 9.27 | 760 | 0.2232 | 0.5293 | 0.6719 | 0.8913 | 0.9753 | 0.0 | 0.9694 | 0.8536 | 0.8344 | 0.9224 | 0.1563 | 0.7175 | 0.6187 | nan | 0.9520 | 0.0 | 0.8567 | 0.7925 | 0.7405 | 0.8617 | 0.1530 | 0.5946 | 0.3420 | 0.0 |
| 0.336 | 9.51 | 780 | 0.2125 | 0.5451 | 0.6873 | 0.9017 | 0.9794 | 0.0 | 0.9741 | 0.8796 | 0.8795 | 0.9323 | 0.2422 | 0.7836 | 0.5153 | nan | 0.9522 | 0.0 | 0.8579 | 0.7945 | 0.7699 | 0.8705 | 0.2256 | 0.5934 | 0.3870 | 0.0 |
| 0.2018 | 9.76 | 800 | 0.2224 | 0.5404 | 0.6681 | 0.8984 | 0.9822 | 0.0 | 0.9652 | 0.8555 | 0.8950 | 0.9519 | 0.3185 | 0.7221 | 0.3220 | nan | 0.9531 | 0.0 | 0.8535 | 0.8067 | 0.7567 | 0.8584 | 0.2949 | 0.6183 | 0.2621 | 0.0 |
| 0.1682 | 10.0 | 820 | 0.2187 | 0.5320 | 0.6820 | 0.8924 | 0.9718 | 0.0 | 0.9814 | 0.8696 | 0.8334 | 0.9177 | 0.1967 | 0.8444 | 0.5228 | nan | 0.9521 | 0.0 | 0.8742 | 0.7917 | 0.7417 | 0.8515 | 0.1852 | 0.5529 | 0.3707 | 0.0 |
| 0.176 | 10.24 | 840 | 0.2228 | 0.5335 | 0.6621 | 0.8964 | 0.9811 | 0.0 | 0.9741 | 0.8623 | 0.8578 | 0.9513 | 0.2482 | 0.7291 | 0.3547 | nan | 0.9553 | 0.0 | 0.8520 | 0.8082 | 0.7474 | 0.8587 | 0.2369 | 0.6102 | 0.2662 | 0.0 |
| 0.4021 | 10.49 | 860 | 0.2221 | 0.5370 | 0.6793 | 0.8973 | 0.9742 | 0.0 | 0.9634 | 0.8676 | 0.9133 | 0.9356 | 0.2970 | 0.7645 | 0.3982 | nan | 0.9533 | 0.0 | 0.8641 | 0.7969 | 0.7135 | 0.8725 | 0.2745 | 0.5827 | 0.3125 | 0.0 |
| 0.2189 | 10.73 | 880 | 0.2594 | 0.5157 | 0.6489 | 0.8857 | 0.9846 | 0.0 | 0.9684 | 0.8699 | 0.7198 | 0.9467 | 0.2024 | 0.8028 | 0.3458 | nan | 0.9562 | 0.0 | 0.8654 | 0.8160 | 0.6462 | 0.8367 | 0.1928 | 0.5791 | 0.2648 | 0.0 |
| 0.218 | 10.98 | 900 | 0.2445 | 0.5208 | 0.6623 | 0.8853 | 0.9734 | 0.0 | 0.9834 | 0.8650 | 0.7271 | 0.9380 | 0.2457 | 0.7985 | 0.4296 | nan | 0.9561 | 0.0 | 0.8233 | 0.8043 | 0.6560 | 0.8406 | 0.2276 | 0.5827 | 0.3177 | 0.0 |
| 0.3402 | 11.22 | 920 | 0.2789 | 0.5181 | 0.6438 | 0.8821 | 0.9728 | 0.0 | 0.9665 | 0.8725 | 0.6840 | 0.9599 | 0.2502 | 0.7863 | 0.3024 | nan | 0.9507 | 0.0 | 0.8598 | 0.8023 | 0.6413 | 0.8265 | 0.2387 | 0.6396 | 0.2225 | 0.0 |
| 0.2232 | 11.46 | 940 | 0.2219 | 0.5387 | 0.6810 | 0.8920 | 0.9705 | 0.0 | 0.9498 | 0.9055 | 0.7311 | 0.9404 | 0.2678 | 0.7632 | 0.6004 | nan | 0.9490 | 0.0 | 0.8674 | 0.7808 | 0.6552 | 0.8561 | 0.2553 | 0.6268 | 0.3965 | 0.0 |
| 0.1727 | 11.71 | 960 | 0.2741 | 0.5328 | 0.6593 | 0.8880 | 0.9812 | 0.0 | 0.9682 | 0.8166 | 0.7600 | 0.9648 | 0.4642 | 0.7672 | 0.2112 | nan | 0.9554 | 0.0 | 0.8701 | 0.7751 | 0.6763 | 0.8300 | 0.4070 | 0.6312 | 0.1824 | 0.0 |
| 0.3027 | 11.95 | 980 | 0.2126 | 0.5477 | 0.6784 | 0.9005 | 0.9774 | 0.0 | 0.9702 | 0.8705 | 0.8597 | 0.9570 | 0.3690 | 0.7887 | 0.3127 | nan | 0.9542 | 0.0 | 0.8645 | 0.8041 | 0.7646 | 0.8582 | 0.3354 | 0.6361 | 0.2600 | 0.0 |
| 0.2245 | 12.2 | 1000 | 0.2490 | 0.5254 | 0.6482 | 0.8899 | 0.9833 | 0.0 | 0.9638 | 0.8135 | 0.7925 | 0.9626 | 0.2752 | 0.8146 | 0.2286 | nan | 0.9548 | 0.0 | 0.8727 | 0.7852 | 0.7239 | 0.8352 | 0.2619 | 0.6371 | 0.1834 | 0.0 |
| 0.1551 | 12.44 | 1020 | 0.2332 | 0.5364 | 0.6875 | 0.8924 | 0.9766 | 0.0 | 0.9856 | 0.8760 | 0.8783 | 0.9118 | 0.3322 | 0.6270 | 0.5995 | nan | 0.9571 | 0.0 | 0.7598 | 0.8028 | 0.7604 | 0.8562 | 0.2982 | 0.5043 | 0.4247 | 0.0 |
| 0.3828 | 12.68 | 1040 | 0.2138 | 0.5437 | 0.6753 | 0.8979 | 0.9799 | 0.0 | 0.9477 | 0.8984 | 0.8723 | 0.9574 | 0.4473 | 0.6534 | 0.3209 | nan | 0.9542 | 0.0 | 0.8187 | 0.8072 | 0.7887 | 0.8580 | 0.3793 | 0.5674 | 0.2640 | 0.0 |
| 0.0929 | 12.93 | 1060 | 0.2544 | 0.5186 | 0.6501 | 0.8855 | 0.9783 | 0.0 | 0.9709 | 0.8634 | 0.7491 | 0.9542 | 0.3067 | 0.6526 | 0.3757 | nan | 0.9549 | 0.0 | 0.8048 | 0.8069 | 0.6771 | 0.8418 | 0.2878 | 0.5185 | 0.2948 | 0.0 |
| 0.2362 | 13.17 | 1080 | 0.2353 | 0.5278 | 0.6661 | 0.8877 | 0.9763 | 0.0 | 0.9807 | 0.8577 | 0.8281 | 0.9374 | 0.3786 | 0.6547 | 0.3816 | nan | 0.9531 | 0.0 | 0.7907 | 0.8045 | 0.7260 | 0.8447 | 0.3395 | 0.5196 | 0.3001 | 0.0 |
| 0.1954 | 13.41 | 1100 | 0.2073 | 0.5580 | 0.7009 | 0.9019 | 0.9762 | 0.0 | 0.9681 | 0.8902 | 0.8184 | 0.9348 | 0.3367 | 0.7731 | 0.6111 | nan | 0.9548 | 0.0 | 0.8642 | 0.8120 | 0.7373 | 0.8679 | 0.3120 | 0.6404 | 0.3918 | 0.0 |
| 0.2412 | 13.66 | 1120 | 0.2144 | 0.5520 | 0.6946 | 0.9022 | 0.9808 | 0.0 | 0.9764 | 0.8460 | 0.8418 | 0.9352 | 0.3048 | 0.7065 | 0.6605 | nan | 0.9554 | 0.0 | 0.8265 | 0.7963 | 0.7536 | 0.8773 | 0.2868 | 0.6078 | 0.4162 | 0.0 |
| 0.2167 | 13.9 | 1140 | 0.2111 | 0.5577 | 0.6947 | 0.9005 | 0.9753 | 0.0 | 0.9744 | 0.8242 | 0.8303 | 0.9442 | 0.3808 | 0.7631 | 0.5597 | nan | 0.9516 | 0.0 | 0.8487 | 0.7825 | 0.7616 | 0.8716 | 0.3440 | 0.6640 | 0.3534 | 0.0 |
| 0.0952 | 14.15 | 1160 | 0.2637 | 0.5279 | 0.6724 | 0.8863 | 0.9740 | 0.0 | 0.9860 | 0.8767 | 0.7933 | 0.9334 | 0.4219 | 0.5904 | 0.4754 | nan | 0.9531 | 0.0 | 0.7480 | 0.8056 | 0.7181 | 0.8535 | 0.3615 | 0.4894 | 0.3497 | 0.0 |
| 0.1032 | 14.39 | 1180 | 0.2484 | 0.5403 | 0.6730 | 0.8921 | 0.9769 | 0.0 | 0.9724 | 0.8537 | 0.7452 | 0.9591 | 0.4251 | 0.8313 | 0.2934 | nan | 0.9553 | 0.0 | 0.8613 | 0.7993 | 0.6934 | 0.8405 | 0.3786 | 0.6417 | 0.2331 | 0.0 |
| 0.2301 | 14.63 | 1200 | 0.2167 | 0.5450 | 0.6843 | 0.8969 | 0.9752 | 0.0 | 0.9785 | 0.8525 | 0.9088 | 0.9402 | 0.4511 | 0.6742 | 0.3782 | nan | 0.9534 | 0.0 | 0.8015 | 0.7952 | 0.7726 | 0.8610 | 0.3911 | 0.5705 | 0.3044 | 0.0 |
| 0.3602 | 14.88 | 1220 | 0.2154 | 0.5522 | 0.7015 | 0.8960 | 0.9738 | 0.0 | 0.9770 | 0.8642 | 0.7977 | 0.9202 | 0.3336 | 0.7938 | 0.6533 | nan | 0.9495 | 0.0 | 0.8433 | 0.7947 | 0.7288 | 0.8638 | 0.3085 | 0.6520 | 0.3817 | 0.0 |
| 0.1081 | 15.12 | 1240 | 0.2512 | 0.5321 | 0.6634 | 0.8872 | 0.9717 | 0.0 | 0.9707 | 0.8631 | 0.7590 | 0.9627 | 0.4839 | 0.6768 | 0.2828 | nan | 0.9508 | 0.0 | 0.8181 | 0.7973 | 0.6834 | 0.8401 | 0.4158 | 0.5872 | 0.2285 | 0.0 |
| 0.138 | 15.37 | 1260 | 0.1995 | 0.5660 | 0.7144 | 0.9028 | 0.9747 | 0.0 | 0.9687 | 0.8627 | 0.8566 | 0.9240 | 0.3978 | 0.8400 | 0.6048 | nan | 0.9511 | 0.0 | 0.8700 | 0.7965 | 0.7700 | 0.8698 | 0.3606 | 0.6542 | 0.3876 | 0.0 |
| 0.1254 | 15.61 | 1280 | 0.2302 | 0.5517 | 0.6866 | 0.8994 | 0.9652 | 0.0 | 0.9730 | 0.8691 | 0.9000 | 0.9510 | 0.4196 | 0.7253 | 0.3762 | nan | 0.9483 | 0.0 | 0.8413 | 0.7915 | 0.7688 | 0.8603 | 0.3770 | 0.6323 | 0.2976 | 0.0 |
| 0.0939 | 15.85 | 1300 | 0.2252 | 0.5506 | 0.6884 | 0.8930 | 0.9853 | 0.0 | 0.9585 | 0.8488 | 0.7720 | 0.9348 | 0.4332 | 0.7397 | 0.5235 | nan | 0.9494 | 0.0 | 0.8510 | 0.8047 | 0.6791 | 0.8478 | 0.3833 | 0.6327 | 0.3582 | 0.0 |
| 0.079 | 16.1 | 1320 | 0.2439 | 0.5381 | 0.6686 | 0.8888 | 0.9764 | 0.0 | 0.9686 | 0.8422 | 0.7464 | 0.9577 | 0.4522 | 0.6807 | 0.3930 | nan | 0.9544 | 0.0 | 0.8271 | 0.7930 | 0.6805 | 0.8475 | 0.3958 | 0.6125 | 0.2706 | 0.0 |
| 0.1409 | 16.34 | 1340 | 0.2244 | 0.5567 | 0.7105 | 0.8928 | 0.9660 | 0.0 | 0.9787 | 0.8779 | 0.7602 | 0.9191 | 0.4474 | 0.7951 | 0.6504 | nan | 0.9501 | 0.0 | 0.8409 | 0.7902 | 0.6957 | 0.8539 | 0.3926 | 0.6588 | 0.3851 | 0.0 |
| 0.3714 | 16.59 | 1360 | 0.2480 | 0.5451 | 0.6732 | 0.8965 | 0.9827 | 0.0 | 0.9692 | 0.8453 | 0.9122 | 0.9530 | 0.4942 | 0.7125 | 0.1900 | nan | 0.9553 | 0.0 | 0.8439 | 0.8058 | 0.7744 | 0.8417 | 0.4262 | 0.6336 | 0.1704 | 0.0 |
| 0.2288 | 16.83 | 1380 | 0.2100 | 0.5634 | 0.7029 | 0.9011 | 0.9767 | 0.0 | 0.9704 | 0.8758 | 0.8995 | 0.9382 | 0.5412 | 0.7601 | 0.3645 | nan | 0.9520 | 0.0 | 0.8497 | 0.8046 | 0.7688 | 0.8513 | 0.4448 | 0.6575 | 0.3057 | 0.0 |
| 0.2297 | 17.07 | 1400 | 0.2083 | 0.5629 | 0.7053 | 0.9023 | 0.9720 | 0.0 | 0.9721 | 0.8785 | 0.9438 | 0.9305 | 0.4819 | 0.7221 | 0.4466 | nan | 0.9514 | 0.0 | 0.8451 | 0.7942 | 0.7728 | 0.8596 | 0.4149 | 0.6409 | 0.3503 | 0.0 |
| 0.1961 | 17.32 | 1420 | 0.2102 | 0.5588 | 0.6962 | 0.9023 | 0.9768 | 0.0 | 0.9704 | 0.8353 | 0.8819 | 0.9435 | 0.4188 | 0.7559 | 0.4831 | nan | 0.9528 | 0.0 | 0.8311 | 0.7837 | 0.7888 | 0.8673 | 0.3779 | 0.6372 | 0.3493 | 0.0 |
| 0.1938 | 17.56 | 1440 | 0.2151 | 0.5554 | 0.6917 | 0.8999 | 0.9775 | 0.0 | 0.9638 | 0.8859 | 0.8447 | 0.9448 | 0.4199 | 0.8143 | 0.3742 | nan | 0.9525 | 0.0 | 0.8517 | 0.8075 | 0.7688 | 0.8574 | 0.3760 | 0.6574 | 0.2831 | 0.0 |
| 0.283 | 17.8 | 1460 | 0.2285 | 0.5502 | 0.6886 | 0.8971 | 0.9752 | 0.0 | 0.9658 | 0.8285 | 0.7796 | 0.9470 | 0.3540 | 0.8388 | 0.5087 | nan | 0.9553 | 0.0 | 0.8671 | 0.7779 | 0.7223 | 0.8636 | 0.3317 | 0.6615 | 0.3227 | 0.0 |
| 0.2744 | 18.05 | 1480 | 0.2301 | 0.5532 | 0.6929 | 0.8974 | 0.9741 | 0.0 | 0.9831 | 0.8644 | 0.7817 | 0.9473 | 0.4503 | 0.7601 | 0.4751 | nan | 0.9550 | 0.0 | 0.8186 | 0.7977 | 0.7268 | 0.8598 | 0.3918 | 0.6399 | 0.3421 | 0.0 |
| 0.0956 | 18.29 | 1500 | 0.2140 | 0.5637 | 0.7149 | 0.9026 | 0.9717 | 0.0 | 0.9813 | 0.8585 | 0.9085 | 0.9223 | 0.4672 | 0.7189 | 0.6055 | nan | 0.9535 | 0.0 | 0.8169 | 0.7947 | 0.7597 | 0.8727 | 0.3985 | 0.6146 | 0.4264 | 0.0 |
| 0.0477 | 18.54 | 1520 | 0.2209 | 0.5551 | 0.6878 | 0.8975 | 0.9692 | 0.0 | 0.9696 | 0.8742 | 0.8338 | 0.9573 | 0.5019 | 0.7648 | 0.3190 | nan | 0.9495 | 0.0 | 0.8549 | 0.7934 | 0.7518 | 0.8579 | 0.4426 | 0.6592 | 0.2416 | 0.0 |
| 0.3598 | 18.78 | 1540 | 0.2430 | 0.5330 | 0.6704 | 0.8890 | 0.9762 | 0.0 | 0.9813 | 0.8502 | 0.8685 | 0.9383 | 0.4623 | 0.5791 | 0.3782 | nan | 0.9527 | 0.0 | 0.7785 | 0.7953 | 0.7300 | 0.8520 | 0.4042 | 0.5312 | 0.2861 | 0.0 |
| 0.1007 | 19.02 | 1560 | 0.2182 | 0.5626 | 0.7106 | 0.8983 | 0.9772 | 0.0 | 0.9776 | 0.8621 | 0.8359 | 0.9232 | 0.4950 | 0.7717 | 0.5527 | nan | 0.9560 | 0.0 | 0.8457 | 0.7943 | 0.7189 | 0.8602 | 0.4321 | 0.6575 | 0.3613 | 0.0 |
| 0.1631 | 19.27 | 1580 | 0.2335 | 0.5543 | 0.6887 | 0.8983 | 0.9767 | 0.0 | 0.9741 | 0.8444 | 0.8350 | 0.9533 | 0.5018 | 0.6999 | 0.4135 | nan | 0.9555 | 0.0 | 0.8328 | 0.7923 | 0.7482 | 0.8629 | 0.4291 | 0.6299 | 0.2924 | 0.0 |
| 0.4344 | 19.51 | 1600 | 0.2087 | 0.5602 | 0.6987 | 0.9025 | 0.9797 | 0.0 | 0.9657 | 0.8446 | 0.8997 | 0.9404 | 0.4632 | 0.7297 | 0.4654 | nan | 0.9533 | 0.0 | 0.8205 | 0.7975 | 0.7871 | 0.8685 | 0.4160 | 0.6058 | 0.3531 | 0.0 |
| 0.0956 | 19.76 | 1620 | 0.2287 | 0.5574 | 0.6894 | 0.9007 | 0.9774 | 0.0 | 0.9667 | 0.8465 | 0.8975 | 0.9511 | 0.4834 | 0.8058 | 0.2760 | nan | 0.9546 | 0.0 | 0.8582 | 0.7967 | 0.7856 | 0.8517 | 0.4333 | 0.6646 | 0.2291 | 0.0 |
| 0.1634 | 20.0 | 1640 | 0.2126 | 0.5576 | 0.6997 | 0.9003 | 0.9725 | 0.0 | 0.9783 | 0.8538 | 0.8832 | 0.9374 | 0.4600 | 0.7096 | 0.5020 | nan | 0.9523 | 0.0 | 0.8042 | 0.7903 | 0.7842 | 0.8664 | 0.4073 | 0.5994 | 0.3719 | 0.0 |
| 0.1048 | 20.24 | 1660 | 0.2254 | 0.5588 | 0.6831 | 0.9017 | 0.9784 | 0.0 | 0.9590 | 0.8543 | 0.8564 | 0.9646 | 0.4542 | 0.7924 | 0.2885 | nan | 0.9534 | 0.0 | 0.8616 | 0.7995 | 0.7886 | 0.8552 | 0.4168 | 0.6842 | 0.2289 | 0.0 |
| 0.1636 | 20.49 | 1680 | 0.2063 | 0.5713 | 0.7097 | 0.9046 | 0.9746 | 0.0 | 0.9741 | 0.8597 | 0.8250 | 0.9483 | 0.5023 | 0.7629 | 0.5409 | nan | 0.9537 | 0.0 | 0.8468 | 0.7972 | 0.7660 | 0.8728 | 0.4351 | 0.6817 | 0.3597 | 0.0 |
| 0.0753 | 20.73 | 1700 | 0.2005 | 0.5738 | 0.7122 | 0.9053 | 0.9742 | 0.0 | 0.9762 | 0.8687 | 0.8586 | 0.9470 | 0.5485 | 0.7920 | 0.4442 | nan | 0.9536 | 0.0 | 0.8456 | 0.8049 | 0.7854 | 0.8662 | 0.4710 | 0.6828 | 0.3289 | 0.0 |
| 0.1346 | 20.98 | 1720 | 0.1977 | 0.5762 | 0.7246 | 0.9065 | 0.9759 | 0.0 | 0.9775 | 0.8651 | 0.8993 | 0.9239 | 0.5007 | 0.7995 | 0.5794 | nan | 0.9548 | 0.0 | 0.8379 | 0.7961 | 0.7913 | 0.8679 | 0.4392 | 0.6660 | 0.4089 | 0.0 |
| 0.1527 | 21.22 | 1740 | 0.2123 | 0.5662 | 0.7087 | 0.9025 | 0.9759 | 0.0002 | 0.9621 | 0.8524 | 0.9264 | 0.9353 | 0.5505 | 0.7677 | 0.4078 | nan | 0.9542 | 0.0002 | 0.8533 | 0.7906 | 0.7497 | 0.8610 | 0.4724 | 0.6514 | 0.3290 | 0.0 |
| 0.1149 | 21.46 | 1760 | 0.2262 | 0.5623 | 0.6926 | 0.9003 | 0.9734 | 0.0 | 0.9632 | 0.8380 | 0.8228 | 0.9623 | 0.5167 | 0.7898 | 0.3669 | nan | 0.9523 | 0.0 | 0.8536 | 0.7847 | 0.7638 | 0.8602 | 0.4552 | 0.6804 | 0.2725 | 0.0 |
| 0.3336 | 21.71 | 1780 | 0.2176 | 0.5647 | 0.7053 | 0.9023 | 0.9713 | 0.0003 | 0.9803 | 0.8546 | 0.8731 | 0.9426 | 0.4990 | 0.7902 | 0.4361 | nan | 0.9543 | 0.0003 | 0.8273 | 0.7953 | 0.7841 | 0.8632 | 0.4397 | 0.6565 | 0.3267 | 0.0 |
| 0.0924 | 21.95 | 1800 | 0.2271 | 0.5608 | 0.6933 | 0.9011 | 0.9834 | 0.0 | 0.9709 | 0.8338 | 0.8657 | 0.9472 | 0.4902 | 0.7430 | 0.4059 | nan | 0.9577 | 0.0 | 0.8354 | 0.7946 | 0.7788 | 0.8591 | 0.4359 | 0.6520 | 0.2945 | 0.0 |
| 0.256 | 22.2 | 1820 | 0.2140 | 0.5659 | 0.7010 | 0.9038 | 0.9820 | 0.0 | 0.9697 | 0.8401 | 0.9119 | 0.9435 | 0.5189 | 0.7867 | 0.3560 | nan | 0.9560 | 0.0 | 0.8462 | 0.7987 | 0.8078 | 0.8582 | 0.4515 | 0.6594 | 0.2817 | 0.0 |
| 0.202 | 22.44 | 1840 | 0.2358 | 0.5589 | 0.6907 | 0.9001 | 0.9766 | 0.0 | 0.9760 | 0.8359 | 0.8945 | 0.9526 | 0.5406 | 0.7420 | 0.2981 | nan | 0.9553 | 0.0 | 0.8391 | 0.7915 | 0.7924 | 0.8522 | 0.4572 | 0.6613 | 0.2397 | 0.0 |
| 0.1456 | 22.68 | 1860 | 0.2115 | 0.5669 | 0.7125 | 0.9016 | 0.9762 | 0.0 | 0.9801 | 0.8582 | 0.8871 | 0.9267 | 0.5130 | 0.8054 | 0.4657 | nan | 0.9556 | 0.0 | 0.8310 | 0.8008 | 0.7886 | 0.8585 | 0.4425 | 0.6588 | 0.3335 | 0.0 |
| 0.1198 | 22.93 | 1880 | 0.2233 | 0.5631 | 0.6997 | 0.9020 | 0.9745 | 0.0 | 0.9731 | 0.8563 | 0.9047 | 0.9484 | 0.5580 | 0.7858 | 0.2967 | nan | 0.9529 | 0.0 | 0.8405 | 0.8011 | 0.7914 | 0.8561 | 0.4715 | 0.6652 | 0.2520 | 0.0 |
| 0.0804 | 23.17 | 1900 | 0.2075 | 0.5719 | 0.7090 | 0.9047 | 0.9791 | 0.0 | 0.9686 | 0.8542 | 0.8664 | 0.9434 | 0.5174 | 0.7723 | 0.4800 | nan | 0.9552 | 0.0 | 0.8456 | 0.7995 | 0.7842 | 0.8636 | 0.4512 | 0.6765 | 0.3429 | 0.0 |
| 0.0779 | 23.41 | 1920 | 0.2217 | 0.5630 | 0.7025 | 0.9007 | 0.9736 | 0.0001 | 0.9774 | 0.8596 | 0.8837 | 0.9422 | 0.5470 | 0.7558 | 0.3832 | nan | 0.9544 | 0.0001 | 0.8258 | 0.7991 | 0.7705 | 0.8559 | 0.4602 | 0.6611 | 0.3032 | 0.0 |
| 0.1159 | 23.66 | 1940 | 0.2122 | 0.5690 | 0.7134 | 0.9030 | 0.9772 | 0.0002 | 0.9767 | 0.8574 | 0.8913 | 0.9275 | 0.4955 | 0.7959 | 0.4990 | nan | 0.9538 | 0.0002 | 0.8307 | 0.8004 | 0.7766 | 0.8612 | 0.4343 | 0.6650 | 0.3683 | 0.0 |
| 0.1 | 23.9 | 1960 | 0.1988 | 0.5762 | 0.7116 | 0.9078 | 0.9834 | 0.0 | 0.9523 | 0.8439 | 0.8919 | 0.9463 | 0.5231 | 0.8031 | 0.4608 | nan | 0.9543 | 0.0 | 0.8620 | 0.7983 | 0.7991 | 0.8662 | 0.4611 | 0.6719 | 0.3492 | 0.0 |
| 0.1052 | 24.15 | 1980 | 0.2147 | 0.5672 | 0.7012 | 0.9048 | 0.9778 | 0.0 | 0.9704 | 0.8441 | 0.8915 | 0.9499 | 0.5004 | 0.7622 | 0.4146 | nan | 0.9550 | 0.0 | 0.8396 | 0.7916 | 0.8009 | 0.8624 | 0.4403 | 0.6608 | 0.3219 | 0.0 |
| 0.1478 | 24.39 | 2000 | 0.2206 | 0.5638 | 0.6950 | 0.9040 | 0.9815 | 0.0 | 0.9657 | 0.8313 | 0.8904 | 0.9530 | 0.4869 | 0.7536 | 0.3929 | nan | 0.9557 | 0.0 | 0.8433 | 0.7884 | 0.7959 | 0.8606 | 0.4347 | 0.6515 | 0.3081 | 0.0 |
| 0.1752 | 24.63 | 2020 | 0.2048 | 0.5700 | 0.7085 | 0.9058 | 0.9793 | 0.0 | 0.9668 | 0.8566 | 0.9095 | 0.9430 | 0.5319 | 0.7519 | 0.4372 | nan | 0.9535 | 0.0 | 0.8458 | 0.7987 | 0.7801 | 0.8670 | 0.4597 | 0.6460 | 0.3492 | 0.0 |
| 0.0451 | 24.88 | 2040 | 0.2171 | 0.5666 | 0.7053 | 0.9025 | 0.9799 | 0.0 | 0.9748 | 0.8552 | 0.8761 | 0.9405 | 0.5371 | 0.7711 | 0.4131 | nan | 0.9545 | 0.0 | 0.8309 | 0.8008 | 0.7878 | 0.8586 | 0.4534 | 0.6644 | 0.3154 | 0.0 |
| 0.4243 | 25.12 | 2060 | 0.2341 | 0.5595 | 0.6916 | 0.8999 | 0.9746 | 0.0 | 0.9764 | 0.8444 | 0.8887 | 0.9491 | 0.4996 | 0.7703 | 0.3211 | nan | 0.9515 | 0.0 | 0.8327 | 0.7956 | 0.7984 | 0.8537 | 0.4397 | 0.6689 | 0.2545 | 0.0 |
| 0.0585 | 25.37 | 2080 | 0.2213 | 0.5660 | 0.7007 | 0.9033 | 0.9820 | 0.0 | 0.9652 | 0.8551 | 0.9015 | 0.9435 | 0.5187 | 0.7915 | 0.3485 | nan | 0.9542 | 0.0 | 0.8466 | 0.8024 | 0.7984 | 0.8564 | 0.4574 | 0.6699 | 0.2748 | 0.0 |
| 0.2001 | 25.61 | 2100 | 0.2246 | 0.5644 | 0.7026 | 0.9009 | 0.9783 | 0.0 | 0.9724 | 0.8691 | 0.8753 | 0.9397 | 0.5349 | 0.7871 | 0.3667 | nan | 0.9539 | 0.0 | 0.8358 | 0.8023 | 0.7863 | 0.8541 | 0.4617 | 0.6681 | 0.2817 | 0.0 |
| 0.0681 | 25.85 | 2120 | 0.2243 | 0.5653 | 0.7001 | 0.9033 | 0.9774 | 0.0071 | 0.9602 | 0.8607 | 0.8942 | 0.9492 | 0.5104 | 0.8085 | 0.3328 | nan | 0.9538 | 0.0071 | 0.8526 | 0.7975 | 0.7950 | 0.8573 | 0.4517 | 0.6706 | 0.2679 | 0.0 |
| 0.3011 | 26.1 | 2140 | 0.2142 | 0.5695 | 0.7099 | 0.9029 | 0.9755 | 0.0022 | 0.9730 | 0.8624 | 0.8863 | 0.9383 | 0.5417 | 0.7982 | 0.4115 | nan | 0.9541 | 0.0021 | 0.8424 | 0.8006 | 0.7913 | 0.8590 | 0.4633 | 0.6755 | 0.3067 | 0.0 |
| 0.1124 | 26.34 | 2160 | 0.2134 | 0.5678 | 0.7069 | 0.9014 | 0.9740 | 0.0097 | 0.9692 | 0.8475 | 0.8710 | 0.9399 | 0.5086 | 0.8119 | 0.4309 | nan | 0.9526 | 0.0097 | 0.8497 | 0.7935 | 0.7773 | 0.8598 | 0.4480 | 0.6745 | 0.3125 | 0.0 |
| 0.2631 | 26.59 | 2180 | 0.2171 | 0.5685 | 0.7043 | 0.9042 | 0.9764 | 0.0165 | 0.9626 | 0.8486 | 0.8985 | 0.9481 | 0.5077 | 0.8124 | 0.3682 | nan | 0.9540 | 0.0164 | 0.8554 | 0.7937 | 0.7880 | 0.8606 | 0.4499 | 0.6734 | 0.2935 | 0.0 |
| 0.0783 | 26.83 | 2200 | 0.2094 | 0.5699 | 0.7120 | 0.9031 | 0.9788 | 0.0 | 0.9649 | 0.8464 | 0.9089 | 0.9335 | 0.5525 | 0.8184 | 0.4045 | nan | 0.9525 | 0.0 | 0.8521 | 0.7966 | 0.7837 | 0.8604 | 0.4736 | 0.6675 | 0.3123 | 0.0 |
| 0.0412 | 27.07 | 2220 | 0.2115 | 0.5688 | 0.7096 | 0.9041 | 0.9772 | 0.0057 | 0.9679 | 0.8448 | 0.8925 | 0.9363 | 0.4727 | 0.8228 | 0.4667 | nan | 0.9545 | 0.0057 | 0.8544 | 0.7909 | 0.7903 | 0.8633 | 0.4230 | 0.6677 | 0.3380 | 0.0 |
| 0.096 | 27.32 | 2240 | 0.2224 | 0.5626 | 0.6939 | 0.9029 | 0.9788 | 0.0 | 0.9699 | 0.8440 | 0.8945 | 0.9502 | 0.4758 | 0.7877 | 0.3443 | nan | 0.9551 | 0.0 | 0.8461 | 0.7956 | 0.7958 | 0.8576 | 0.4303 | 0.6747 | 0.2704 | 0.0 |
| 0.1542 | 27.56 | 2260 | 0.2251 | 0.5646 | 0.6958 | 0.9027 | 0.9755 | 0.0040 | 0.9674 | 0.8465 | 0.9004 | 0.9548 | 0.5310 | 0.7762 | 0.3064 | nan | 0.9531 | 0.0040 | 0.8489 | 0.7935 | 0.7997 | 0.8566 | 0.4623 | 0.6790 | 0.2493 | 0.0 |
| 0.2322 | 27.8 | 2280 | 0.2243 | 0.5641 | 0.6943 | 0.9029 | 0.9739 | 0.0024 | 0.9733 | 0.8549 | 0.8847 | 0.9570 | 0.5096 | 0.7569 | 0.3360 | nan | 0.9533 | 0.0024 | 0.8427 | 0.7961 | 0.7998 | 0.8584 | 0.4475 | 0.6741 | 0.2668 | 0.0 |
| 0.1025 | 28.05 | 2300 | 0.2217 | 0.5665 | 0.7022 | 0.9034 | 0.9726 | 0.0077 | 0.9750 | 0.8541 | 0.9116 | 0.9503 | 0.5514 | 0.7723 | 0.3243 | nan | 0.9519 | 0.0077 | 0.8415 | 0.7960 | 0.7986 | 0.8598 | 0.4700 | 0.6711 | 0.2689 | 0.0 |
| 0.1405 | 28.29 | 2320 | 0.2244 | 0.5680 | 0.7009 | 0.9042 | 0.9761 | 0.0087 | 0.9650 | 0.8600 | 0.8864 | 0.9543 | 0.5317 | 0.7668 | 0.3590 | nan | 0.9539 | 0.0087 | 0.8512 | 0.7963 | 0.7881 | 0.8603 | 0.4623 | 0.6746 | 0.2846 | 0.0 |
| 0.1002 | 28.54 | 2340 | 0.2165 | 0.5682 | 0.7088 | 0.9031 | 0.9742 | 0.0035 | 0.9769 | 0.8598 | 0.8865 | 0.9408 | 0.5402 | 0.7832 | 0.4139 | nan | 0.9535 | 0.0035 | 0.8365 | 0.7943 | 0.7853 | 0.8610 | 0.4612 | 0.6691 | 0.3179 | 0.0 |
| 0.0803 | 28.78 | 2360 | 0.2233 | 0.5655 | 0.7000 | 0.9019 | 0.9759 | 0.0059 | 0.9706 | 0.8529 | 0.8577 | 0.9497 | 0.5112 | 0.7560 | 0.4199 | nan | 0.9541 | 0.0059 | 0.8452 | 0.7923 | 0.7727 | 0.8604 | 0.4468 | 0.6716 | 0.3062 | 0.0 |
| 0.1149 | 29.02 | 2380 | 0.2215 | 0.5663 | 0.7008 | 0.9027 | 0.9749 | 0.0078 | 0.9712 | 0.8512 | 0.8789 | 0.9503 | 0.5218 | 0.7721 | 0.3787 | nan | 0.9534 | 0.0078 | 0.8440 | 0.7935 | 0.7823 | 0.8596 | 0.4542 | 0.6740 | 0.2941 | 0.0 |
| 0.0773 | 29.27 | 2400 | 0.2228 | 0.5675 | 0.7068 | 0.9027 | 0.9762 | 0.0070 | 0.9763 | 0.8517 | 0.8750 | 0.9405 | 0.5117 | 0.7937 | 0.4288 | nan | 0.9547 | 0.0070 | 0.8381 | 0.7930 | 0.7838 | 0.8606 | 0.4480 | 0.6735 | 0.3166 | 0.0 |
| 0.1085 | 29.51 | 2420 | 0.2225 | 0.5650 | 0.6974 | 0.9033 | 0.9811 | 0.0 | 0.9693 | 0.8404 | 0.8858 | 0.9493 | 0.5061 | 0.7632 | 0.3816 | nan | 0.9550 | 0.0 | 0.8423 | 0.7940 | 0.7873 | 0.8597 | 0.4464 | 0.6708 | 0.2944 | 0.0 |
| 0.0874 | 29.76 | 2440 | 0.2147 | 0.5680 | 0.7047 | 0.9033 | 0.9776 | 0.0012 | 0.9709 | 0.8514 | 0.8785 | 0.9432 | 0.5078 | 0.7886 | 0.4235 | nan | 0.9536 | 0.0012 | 0.8450 | 0.7948 | 0.7865 | 0.8610 | 0.4482 | 0.6751 | 0.3149 | 0.0 |
| 0.1308 | 30.0 | 2460 | 0.2172 | 0.5684 | 0.7041 | 0.9037 | 0.9782 | 0.0031 | 0.9694 | 0.8474 | 0.8818 | 0.9453 | 0.5085 | 0.7910 | 0.4118 | nan | 0.9541 | 0.0031 | 0.8472 | 0.7939 | 0.7881 | 0.8610 | 0.4506 | 0.6761 | 0.3104 | 0.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| null | transformers | image-segmentation | null | null | null | null | null | null | null | null | null | peldrak/segformer-finetuned-riviera2 | [
-0.7395023107528687,
-0.6726686358451843,
0.3141826093196869,
0.1410563886165619,
-0.09272684901952744,
0.073300801217556,
-0.0018665496027097106,
0.10341692715883255,
0.7608120441436768,
0.49231088161468506,
-0.6011878848075867,
-0.6964059472084045,
-0.8577781915664673,
-0.095406420528888... |
TheBloke/deepseek-llm-67b-chat-AWQ | TheBloke | 2023-11-29T16:30:51Z | 3 | 1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:deepseek-ai/deepseek-llm-67b-chat",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T16:30:51Z | 2023-11-29T13:56:33.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-67b-chat
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek Llm 67B Chat
model_type: deepseek
prompt_template: 'User: {prompt}
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 -->
# Deepseek Llm 67B Chat - AWQ
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- description start -->
## Description
This repo contains AWQ model files for [DeepSeek's Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-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.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [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/deepseek-llm-67b-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: DeepSeek-LLM
```
User: {prompt}
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/deepseek-llm-67b-chat-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 37.52 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/deepseek-llm-67b-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: `deepseek-llm-67b-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/deepseek-llm-67b-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'''User: {prompt}
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/deepseek-llm-67b-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/deepseek-llm-67b-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'''User: {prompt}
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/deepseek-llm-67b-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'''User: {prompt}
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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: DeepSeek's Deepseek Llm 67B Chat
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/deepseek-llm-67b-chat-AWQ | [
-0.5781465172767639,
-0.8941106200218201,
0.46662604808807373,
0.19719628989696503,
-0.20196929574012756,
-0.1298975944519043,
-0.005038973409682512,
-0.45420581102371216,
-0.013235070742666721,
0.35727375745773315,
-0.7672370076179504,
-0.5975210070610046,
-0.41848865151405334,
-0.1253305... |
LoneStriker/psyonic-cetacean-20B-4.65bpw-h6-exl2 | LoneStriker | 2023-11-29T15:36:54Z | 3 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:36:54Z | 2023-11-29T14:08:48.000Z | null | null | ---
license: other
license_name: microsoft-research-license
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/psyonic-cetacean-20B-4.65bpw-h6-exl2 | [
-0.40348950028419495,
-0.47778305411338806,
0.3416680097579956,
0.31183406710624695,
-0.48241081833839417,
0.15165606141090393,
0.044734615832567215,
-0.8787826895713806,
0.3258506953716278,
0.7322417497634888,
-0.6376093626022339,
-0.3487909138202667,
-0.6281625628471375,
-0.0670375972986... |
Hammad2910/t5_agent | Hammad2910 | 2023-11-29T14:23:12Z | 3 | 0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:23:12Z | 2023-11-29T14:16:09.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Hammad2910/t5_agent | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
Union-AI-OSS/1B-zeryx-example-adapter | Union-AI-OSS | 2023-11-29T14:44:53Z | 3 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:EleutherAI/pythia-1b",
"region:us"
] | 2023-11-29T14:44:53Z | 2023-11-29T14:37:44.000Z | null | null | ---
library_name: peft
base_model: EleutherAI/pythia-1b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | Union-AI-OSS/1B-zeryx-example-adapter | [
-0.5839648842811584,
-0.5444982647895813,
0.4422629177570343,
0.10047031193971634,
-0.21837837994098663,
-0.29282113909721375,
0.1171051636338234,
-0.560427188873291,
0.08466268330812454,
0.6898143887519836,
-0.749681830406189,
-0.6463689804077148,
-0.5569517612457275,
-0.12423540651798248... |
hotamago/ZAIC-2023-Model-MetaMath-7B-Short | hotamago | 2023-11-29T16:37:58Z | 3 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:37:58Z | 2023-11-29T16:13:35.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | hotamago/ZAIC-2023-Model-MetaMath-7B-Short | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
abolton99/orchestration | abolton99 | 2023-11-29T16:58:35Z | 3 | 0 | null | [
"sentence-transformers",
"safetensors",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-11-29T16:58:35Z | 2023-11-29T16:57:40.000Z | null | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/gr/47hycvx13rd_q25kzttvfx6h0000gn/T/tmp2wo1zuan/abolton99/orchestration
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/gr/47hycvx13rd_q25kzttvfx6h0000gn/T/tmp2wo1zuan/abolton99/orchestration")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| null | sentence-transformers | text-classification | null | null | null | null | null | null | null | null | null | abolton99/orchestration | [
-0.12510207295417786,
-0.7811993360519409,
0.34196293354034424,
-0.028716308996081352,
-0.04815997555851936,
-0.2458374947309494,
-0.27094393968582153,
-0.11382313817739487,
-0.08214525133371353,
0.48265743255615234,
-0.6603320240974426,
-0.26752495765686035,
-0.5052258968353271,
0.1931207... |
sotossta/ppo-LunarLander-v2 | sotossta | 2023-11-29T17:41:37Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T17:41:37Z | 2023-11-29T17:36:31.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 233.73 +/- 29.37
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | sotossta/ppo-LunarLander-v2 | [
-0.0031748120673000813,
-0.3944118618965149,
0.24817678332328796,
0.33905377984046936,
-0.08787564188241959,
0.04007992520928383,
0.5000529885292053,
-0.17607824504375458,
0.2888225317001343,
0.9444824457168579,
-0.6269252300262451,
-0.5120341181755066,
-0.49809563159942627,
-0.27938348054... |
naga-jay/bloom_prompt_tuning_1701280707.463308 | naga-jay | 2023-11-29T17:58:28Z | 3 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloomz-560m",
"region:us"
] | 2023-11-29T17:58:28Z | 2023-11-29T17:58:27.000Z | null | null | ---
library_name: peft
base_model: bigscience/bloomz-560m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | naga-jay/bloom_prompt_tuning_1701280707.463308 | [
-0.5982630252838135,
-0.5568305253982544,
0.4399724304676056,
0.10511720925569534,
-0.21972714364528656,
-0.30806827545166016,
0.12281565368175507,
-0.5639218091964722,
0.07784994691610336,
0.6828545928001404,
-0.7460102438926697,
-0.6527181267738342,
-0.5489066243171692,
-0.13548845052719... |
alfredo-wh/ppo-Pacman-v5 | alfredo-wh | 2023-11-29T19:02:17Z | 3 | 0 | null | [
"stable-baselines3",
"ALE/Pacman-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T19:02:17Z | 2023-11-29T19:01:54.000Z | null | null | ---
library_name: stable-baselines3
tags:
- ALE/Pacman-v5
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/Pacman-v5
type: ALE/Pacman-v5
metrics:
- type: mean_reward
value: 43.30 +/- 17.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **ALE/Pacman-v5**
This is a trained model of a **PPO** agent playing **ALE/Pacman-v5**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env ALE/Pacman-v5 -orga alfredo-wh -f logs/
python -m rl_zoo3.enjoy --algo ppo --env ALE/Pacman-v5 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env ALE/Pacman-v5 -orga alfredo-wh -f logs/
python -m rl_zoo3.enjoy --algo ppo --env ALE/Pacman-v5 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env ALE/Pacman-v5 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env ALE/Pacman-v5 -f logs/ -orga alfredo-wh
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 500000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | alfredo-wh/ppo-Pacman-v5 | [
-0.6672477722167969,
-0.5242368578910828,
0.1864340603351593,
0.24183419346809387,
-0.4232673645019531,
-0.20614148676395416,
0.14668594300746918,
-0.40192192792892456,
0.018137726932764053,
0.4242507815361023,
-0.7461875081062317,
-0.48676323890686035,
-0.5794888138771057,
0.1582506746053... |
harshaan3497/mistral_b_finance_finetuned_test_harshaan_1000_Instruct | harshaan3497 | 2023-11-29T19:52:44Z | 3 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:sanchit-gandhi/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2023-11-29T19:52:44Z | 2023-11-29T19:52:35.000Z | null | null | ---
library_name: peft
base_model: sanchit-gandhi/Mistral-7B-Instruct-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | harshaan3497/mistral_b_finance_finetuned_test_harshaan_1000_Instruct | [
-0.5779396295547485,
-0.5580515265464783,
0.40497368574142456,
0.08317576348781586,
-0.253414124250412,
-0.27545133233070374,
0.06068450212478638,
-0.5384040474891663,
0.04877224564552307,
0.6135933995246887,
-0.7259423136711121,
-0.6298723816871643,
-0.5585345029830933,
-0.079713866114616... |
benayas/llama-2-7b-snips_v5 | benayas | 2023-11-29T20:09:19Z | 3 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:09:19Z | 2023-11-29T20:00:09.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | benayas/llama-2-7b-snips_v5 | [
-0.3227648437023163,
-0.2256842851638794,
0.8622258305549622,
0.4346150755882263,
-0.5282991528511047,
0.7012966275215149,
0.7915719151496887,
0.07618607580661774,
0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
Tatvajsh/dpo_AHS_OPS_WPCS_v3.0 | Tatvajsh | 2023-11-29T23:06:14Z | 3 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
] | 2023-11-29T23:06:14Z | 2023-11-29T20:17:43.000Z | null | null | ---
library_name: peft
base_model: openlm-research/open_llama_3b_v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | Tatvajsh/dpo_AHS_OPS_WPCS_v3.0 | [
-0.5717411637306213,
-0.5540269017219543,
0.40148475766181946,
0.0774766355752945,
-0.2556554973125458,
-0.2793441116809845,
0.0574457086622715,
-0.5368510484695435,
0.05009448900818825,
0.6143900752067566,
-0.7264446020126343,
-0.6263335347175598,
-0.5605001449584961,
-0.08549568057060242... |
KuriT/HF_DRL | KuriT | 2023-11-29T20:29:18Z | 3 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T20:29:18Z | 2023-11-29T20:28:58.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.67 +/- 19.25
name: mean_reward
verified: false
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | KuriT/HF_DRL | [
-0.0031747242901474237,
-0.3944118320941925,
0.24817679822444916,
0.3390541076660156,
-0.08787582069635391,
0.04007984697818756,
0.5000530481338501,
-0.1760784089565277,
0.28882232308387756,
0.9444825649261475,
-0.6269250512123108,
-0.5120341181755066,
-0.4980955719947815,
-0.2793834805488... |
th-nuernberg/gbert-large-german-counseling-gecco | th-nuernberg | 2023-11-29T23:05:35Z | 3 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"ger",
"base_model:deepset/gbert-large",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:05:35Z | 2023-11-29T20:59:16.000Z | null | null | ---
language:
- ger
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: th-nuernberg/gbert-large-german-counseling-gecco
results: []
widget:
- text: "Was haben Sie bisher unternommen, um ihr Problem zu lösen?"
- text: "Hallo Peter, wie kann ich helfen?"
- text: "Ich bin hier, um zuzuhören. Wenn du mir erzählen möchtest, wie es dir geht, bin ich bereit."
- text: "Fällt es dir leicht, mit anderen Menschen in Kontakt zu treten?"
- text: "Welche Hobbys oder Freizeitaktivitäten würdest du gerne in der Zukunft ausprobieren?"
- text: "Haben Sie finanzielle Unterstützung von Ihrem Mann?"
- text: "Könnten Sie bitte genauer beschreiben, welche Schwierigkeiten durch diese technischen Probleme entstehen?"
- text: "Gibt es denn keine Hobbys, die du mit deinen Freunden gemeinsam machen kannst?"
- text: "Wo geht ihr Sohn zur Schule?"
- text: "Haben sie gemeinsame Hobbies mit Ihren Freunden?"
---
# th-nuernberg/gbert-large-german-counseling-gecco
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
trained with the German E-Counseling Conversation Dataset,
created at the Technische Hochschule Nürnberg (see [github.com/th-nuernberg/gecco-dataset](https://github.com/th-nuernberg/gecco-dataset)).
It achieves the following results on the evaluation set: Accuracy 0.78, F1 0.66.
Contact:
- [Prof. Dr. Jens Albrecht](https://www.th-nuernberg.de/person/albrecht-jens/)
- [Prof. Dr. Robert Lehmann](https://www.th-nuernberg.de/person/lehmann-robert/)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.3924 | 1.0 | 20 | 2.9410 | 0.2032 | 0.0418 |
| 2.7028 | 2.0 | 40 | 2.2499 | 0.4806 | 0.2366 |
| 2.0665 | 3.0 | 60 | 1.7404 | 0.6129 | 0.3537 |
| 1.5 | 4.0 | 80 | 1.3602 | 0.6839 | 0.4109 |
| 1.0794 | 5.0 | 100 | 1.1377 | 0.7355 | 0.4971 |
| 0.7965 | 6.0 | 120 | 1.0123 | 0.7548 | 0.5518 |
| 0.6438 | 7.0 | 140 | 0.9806 | 0.7613 | 0.5547 |
| 0.5039 | 8.0 | 160 | 0.9452 | 0.7742 | 0.6019 |
| 0.4058 | 9.0 | 180 | 0.9218 | 0.7774 | 0.5907 |
| 0.3363 | 10.0 | 200 | 0.9373 | 0.7710 | 0.6157 |
| 0.2451 | 11.0 | 220 | 0.9751 | 0.7548 | 0.5955 |
| 0.1997 | 12.0 | 240 | 0.9197 | 0.7839 | 0.6526 |
| 0.1765 | 13.0 | 260 | 0.9187 | 0.7806 | 0.6425 |
| 0.1453 | 14.0 | 280 | 0.9431 | 0.7742 | 0.6357 |
| 0.1216 | 15.0 | 300 | 0.9388 | 0.7839 | 0.6534 |
| 0.1097 | 16.0 | 320 | 0.9290 | 0.7839 | 0.6645 |
### Framework versions
- Transformers 4.35.1
- Pytorch 1.10.1+cu111
- Datasets 2.14.7
- Tokenizers 0.14.1
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | th-nuernberg/gbert-large-german-counseling-gecco | [
-0.733215868473053,
-0.6899682283401489,
0.23595434427261353,
0.06722069531679153,
-0.08152984082698822,
-0.3232395648956299,
-0.2906947731971741,
-0.23949897289276123,
0.30821195244789124,
0.21554474532604218,
-0.8177466988563538,
-0.8970632553100586,
-0.769409716129303,
-0.22811110317707... |
zhangpn/bert-emotion | zhangpn | 2023-11-29T23:00:26Z | 2 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:00:26Z | 2022-11-30T21:32:54.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7412691902027423
- name: Recall
type: recall
value: 0.7200253439873575
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2007
- Precision: 0.7413
- Recall: 0.7200
- Fscore: 0.7268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8416 | 1.0 | 815 | 0.7683 | 0.7000 | 0.7141 | 0.7062 |
| 0.5465 | 2.0 | 1630 | 0.8561 | 0.7640 | 0.6735 | 0.6979 |
| 0.2747 | 3.0 | 2445 | 1.2007 | 0.7413 | 0.7200 | 0.7268 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | zhangpn/bert-emotion | [
-0.4828285276889801,
-0.666907548904419,
0.23210909962654114,
0.34824517369270325,
-0.4257555603981018,
-0.22810722887516022,
-0.2594827115535736,
-0.1496116816997528,
0.21306072175502777,
-0.021752746775746346,
-0.8830209374427795,
-0.7797757983207703,
-0.869249165058136,
-0.2712508738040... |
shunsso/A6 | shunsso | 2023-11-29T21:32:52Z | 2 | 1 | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:32:52Z | 2023-07-20T19:22:52.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | shunsso/A6 | [
-0.3227651119232178,
-0.22568456828594208,
0.8622261881828308,
0.43461447954177856,
-0.5282989740371704,
0.7012965083122253,
0.7915719747543335,
0.0761861652135849,
0.7746025323867798,
0.25632235407829285,
-0.7852817177772522,
-0.22573819756507874,
-0.9104477763175964,
0.5715669393539429,
... |
roymgabriel/trial-model | roymgabriel | 2023-11-29T06:32:18Z | 2 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:32:18Z | 2023-09-15T00:40:43.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: trial-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trial-model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0843
- F1: 0.2899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | roymgabriel/trial-model | [
-0.28088483214378357,
-0.6539552807807922,
0.37739741802215576,
0.17812688648700714,
-0.4088381230831146,
-0.47831276059150696,
-0.2832884192466736,
-0.16332858800888062,
0.09643492102622986,
0.42468687891960144,
-0.7946215867996216,
-0.6306886076927185,
-0.8723334074020386,
-0.03905382752... |
Nonzerophilip/testThesisSmallfiftyTESTsynone | Nonzerophilip | 2023-11-29T17:27:03Z | 2 | 0 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:KBLab/bert-base-swedish-cased-ner",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:27:03Z | 2023-10-03T14:48:31.000Z | null | null | ---
base_model: KBLab/bert-base-swedish-cased-ner
tags:
- generated_from_trainer
model-index:
- name: testThesisSmallfiftyTESTsynone
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# testThesisSmallfiftyTESTsynone
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co/KBLab/bert-base-swedish-cased-ner) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 6
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | Nonzerophilip/testThesisSmallfiftyTESTsynone | [
-0.47125524282455444,
-0.6514748930931091,
0.1892034411430359,
0.22539274394512177,
-0.48456743359565735,
-0.3875795900821686,
-0.04674103483557701,
-0.13991595804691315,
0.20123061537742615,
0.400586873292923,
-0.8276896476745605,
-0.5377370119094849,
-0.40826794505119324,
-0.062457621097... |
arjunssat/mistral_7B_sharded_finetuned_rfp | arjunssat | 2023-11-29T03:31:19Z | 2 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"pretrained",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T03:31:19Z | 2023-11-01T06:14:15.000Z | null | null | ---
license: apache-2.0
pipeline_tag: text-generation
tags:
- pretrained
inference:
parameters:
temperature: 0.7
---
# Note: Sharded Version of the Original "Mistral 7B" Model
This is just a version of https://huggingface.co/mistralai/Mistral-7B-v0.1 which is sharded to 2GB maximum parts in order to reduce the RAM required when loading.
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | arjunssat/mistral_7B_sharded_finetuned_rfp | [
-0.47116583585739136,
-0.7728816866874695,
0.26557496190071106,
0.5549750328063965,
-0.47428977489471436,
-0.3636222779750824,
0.09896134585142136,
-0.45373764634132385,
-0.0365036204457283,
1.0634750127792358,
-0.2681238055229187,
-0.29458481073379517,
-0.5276066064834595,
-0.161673590540... |
mehedihasanbijoy/wav2vec2-large-xls-r-300m-finnish-colab | mehedihasanbijoy | 2023-11-29T22:24:29Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:facebook/wav2vec2-lv-60-espeak-cv-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:24:29Z | 2023-11-10T08:56:55.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-lv-60-espeak-cv-ft
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: wav2vec2-large-xls-r-300m-finnish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finnish-colab
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | mehedihasanbijoy/wav2vec2-large-xls-r-300m-finnish-colab | [
-0.44298622012138367,
-0.8939919471740723,
0.07716070115566254,
0.14240103960037231,
-0.3321656584739685,
-0.4371594786643982,
-0.29223960638046265,
-0.32513996958732605,
0.19987282156944275,
0.4070971608161926,
-0.75893634557724,
-0.6440300941467285,
-0.5459964275360107,
-0.19199874997138... |
cmtn/test_model | cmtn | 2023-11-29T10:26:10Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:26:10Z | 2023-11-16T09:35:55.000Z | null | null | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: test_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6095
- Rouge1: 0.2222
- Rouge2: 0.1274
- Rougel: 0.2168
- Rougelsum: 0.2156
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 3 | 0.6897 | 0.2112 | 0.1226 | 0.2085 | 0.2072 | 19.0 |
| No log | 2.0 | 6 | 0.6454 | 0.2127 | 0.1245 | 0.2107 | 0.2099 | 19.0 |
| No log | 3.0 | 9 | 0.6195 | 0.2152 | 0.1245 | 0.2136 | 0.2121 | 19.0 |
| No log | 4.0 | 12 | 0.6095 | 0.2222 | 0.1274 | 0.2168 | 0.2156 | 19.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | cmtn/test_model | [
-0.48459169268608093,
-0.6110828518867493,
0.25930750370025635,
0.12257272750139236,
-0.28952980041503906,
-0.36777615547180176,
-0.09881287813186646,
-0.2377365231513977,
0.17838244140148163,
0.33458587527275085,
-0.8076631426811218,
-0.7581069469451904,
-0.7545192837715149,
-0.0485897883... |
ladoza03/distilbert-base-uncased-finetuned-emotion | ladoza03 | 2023-11-29T17:32:44Z | 2 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:32:44Z | 2023-11-19T14:40:10.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2214
- Accuracy: 0.924
- F1: 0.9238
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8627 | 1.0 | 250 | 0.3391 | 0.901 | 0.8991 |
| 0.2621 | 2.0 | 500 | 0.2214 | 0.924 | 0.9238 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.14.1
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | ladoza03/distilbert-base-uncased-finetuned-emotion | [
-0.5469841361045837,
-0.6144117116928101,
0.26068374514579773,
0.3643665611743927,
-0.4054870009422302,
-0.28525692224502563,
-0.19842205941677094,
-0.10243890434503555,
0.12528561055660248,
0.11381614208221436,
-0.8075280785560608,
-0.7196639776229858,
-0.8912513852119446,
-0.115344226360... |
nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant | nm-testing | 2023-11-29T06:38:19Z | 2 | 0 | null | [
"transformers",
"onnx",
"llama",
"text-generation",
"deepsparse",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:38:19Z | 2023-11-20T19:10:02.000Z | null | null | ---
tags:
- deepsparse
---
## Usage
```python
from deepsparse import TextGeneration
prompt = "How to make banana bread?"
formatted_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
model = TextGeneration(model="hf:nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant")
print(model(formatted_prompt, max_new_tokens=500).generations[0].text)
"""
Banana bread is a delicious and easy-to-make recipe that is sure to please. Here is a recipe for making banana bread:
Ingredients:
For the Banana Bread:
- 1 cup of sugar
- 1 cup of flour
- 1/2 cup of mashed bananas
- 1/4 cup of milk
- 1/2 cup of melted butter
- 1/4 cup of baking powder
- 1/4 cup of baking soda
- 1/4 cup of eggs
- 1/4 cup of milk
- 1/4 cup of sugar
Instructions:
1. Preheat the oven to 325°F (160°C).
2. In a large bowl, combine the sugar and flour.
3. In a separate bow, combine the mashed bananas, milk, butter, baking powder, baking soda, milk, sugar.
4. Add the bananas and milk into the flour-sugar mixture.
5. Pour the milk into the bowl of the flour-sugar mixture.
6. Pour the baking powder into the bowl of the flour-sugar mixture.
7. Pour the mashed bananas into the bowl of the flour-sugar mixture.
8. Add the eggs into the bowl of the flour-sugar mixture.
9. Stir the mixture until it becomes a dough.
10. Grease a 9-inch (23 cm) square pan.
11. Pour the mixture into the pan.
12. Bake the banana bread in the oven for 40 minutes.
13. Remove the banana bread from the oven and cool it.
14. Cut the bread into 16 pieces.
15. Make the glaze:
16. Sprinkle the sugar over the bread.
17. Bake the bread in the oven for 30 minutes.
"""
```
```python
from deepsparse import TextGeneration
prompt = "How to get in a good university?"
formatted_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
model = TextGeneration(model="hf:nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant")
print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
"""
There are many factors to consider when choosing a university. Here are some tips for getting into a good university:
1. Research your options: Consider the schools in your area and the ones in your desired location. Research their reputation, tuition, and academic programs.
2. Apply to multiple universities: Apply to multiple universities, ensuring that you are applying to the best option for you.
3. Get a job: If you are applying to a university, you will need to find a job to support your studies. This will help you budget and manage your time.
4. Get involved with your community: Your university will likely have a community of students and faculty. Engage with this community by volunteering, participating in clubs, and engaging with others in your community.
5. Get involved with extracurricular activities: Universities often have many extracurricular activities, which can help you meet new people
"""
```
## One-shot and Export
```bash
git clone https://github.com/neuralmagic/sparseml
pip install -e "sparseml[transformers]"
wget https://huggingface.co/nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant/raw/main/recipe.yaml # download recipe
python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py TinyLlama/TinyLlama-1.1B-Chat-v0.4 open_platypus --recipe recipe.yaml --save True
python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment
cp deployment/model.onnx deployment/model-orig.onnx
wget https://huggingface.co/nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant/raw/main/onnx_kv_inject.py # kv_cache file
python onnx_kv_inject.py --input-file deployment/model-orig.onnx --output-file deployment/model.onnx
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | nm-testing/TinyLlama-1.1B-Chat-v0.4-pruned50-quant | [
-0.3436565697193146,
-0.8886404633522034,
0.5947067737579346,
0.1646522879600525,
0.07875189930200577,
0.13024470210075378,
-0.2579364478588104,
-0.10490192472934723,
0.2201770395040512,
0.42430761456489563,
-0.5714378952980042,
-0.38491302728652954,
-0.4337599277496338,
-0.120367988944053... |
Yova/baseline | Yova | 2023-11-29T13:50:28Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:50:28Z | 2023-11-21T13:16:54.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: baseline
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# baseline
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9254
- Exact Match: 0.702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 400
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 4000
- training_steps: 20000
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|
| 2.8524 | 16.0 | 400 | 1.7375 | 0.059 |
| 1.422 | 32.0 | 800 | 1.6708 | 0.11 |
| 1.0862 | 48.0 | 1200 | 1.7149 | 0.094 |
| 0.9374 | 64.0 | 1600 | 1.6508 | 0.159 |
| 0.8704 | 80.0 | 2000 | 1.6920 | 0.112 |
| 0.8356 | 96.0 | 2400 | 1.5605 | 0.16 |
| 0.8157 | 112.0 | 2800 | 1.5249 | 0.188 |
| 0.8029 | 128.0 | 3200 | 1.3993 | 0.25 |
| 0.7917 | 144.0 | 3600 | 1.2768 | 0.312 |
| 0.7821 | 160.0 | 4000 | 1.2213 | 0.397 |
| 0.7719 | 176.0 | 4400 | 1.1216 | 0.432 |
| 0.7635 | 192.0 | 4800 | 1.1076 | 0.458 |
| 0.7584 | 208.0 | 5200 | 1.0275 | 0.567 |
| 0.7556 | 224.0 | 5600 | 1.0464 | 0.552 |
| 0.7525 | 240.0 | 6000 | 1.0442 | 0.56 |
| 0.7496 | 256.0 | 6400 | 1.0108 | 0.581 |
| 0.7487 | 272.0 | 6800 | 0.9721 | 0.61 |
| 0.7467 | 288.0 | 7200 | 1.0326 | 0.567 |
| 0.7466 | 304.0 | 7600 | 0.9900 | 0.572 |
| 0.7449 | 320.0 | 8000 | 1.0150 | 0.604 |
| 0.7445 | 336.0 | 8400 | 0.9755 | 0.603 |
| 0.7433 | 352.0 | 8800 | 0.9705 | 0.645 |
| 0.7432 | 368.0 | 9200 | 0.9567 | 0.663 |
| 0.7432 | 384.0 | 9600 | 0.9733 | 0.68 |
| 0.7425 | 400.0 | 10000 | 0.9262 | 0.67 |
| 0.7417 | 416.0 | 10400 | 0.9216 | 0.673 |
| 0.7409 | 432.0 | 10800 | 0.9411 | 0.681 |
| 0.7404 | 448.0 | 11200 | 0.9312 | 0.674 |
| 0.7405 | 464.0 | 11600 | 0.9777 | 0.585 |
| 0.7406 | 480.0 | 12000 | 0.9191 | 0.683 |
| 0.7395 | 496.0 | 12400 | 0.9216 | 0.643 |
| 0.7396 | 512.0 | 12800 | 0.9764 | 0.645 |
| 0.7394 | 528.0 | 13200 | 0.9361 | 0.644 |
| 0.7392 | 544.0 | 13600 | 0.9210 | 0.67 |
| 0.739 | 560.0 | 14000 | 0.9387 | 0.688 |
| 0.7389 | 576.0 | 14400 | 0.9385 | 0.67 |
| 0.7383 | 592.0 | 14800 | 0.9500 | 0.655 |
| 0.7386 | 608.0 | 15200 | 0.9405 | 0.67 |
| 0.7383 | 624.0 | 15600 | 0.9335 | 0.691 |
| 0.738 | 640.0 | 16000 | 0.9079 | 0.708 |
| 0.7379 | 656.0 | 16400 | 0.9027 | 0.714 |
| 0.7376 | 672.0 | 16800 | 0.8969 | 0.703 |
| 0.7372 | 688.0 | 17200 | 0.9169 | 0.685 |
| 0.7375 | 704.0 | 17600 | 0.8895 | 0.738 |
| 0.7376 | 720.0 | 18000 | 0.8951 | 0.734 |
| 0.7371 | 736.0 | 18400 | 0.9408 | 0.673 |
| 0.737 | 752.0 | 18800 | 0.9270 | 0.693 |
| 0.7371 | 768.0 | 19200 | 0.9063 | 0.71 |
| 0.7369 | 784.0 | 19600 | 0.9253 | 0.678 |
| 0.7367 | 800.0 | 20000 | 0.9254 | 0.702 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Yova/baseline | [
-0.7347301244735718,
-0.7206717729568481,
0.1845901757478714,
0.05849192664027214,
-0.028257597237825394,
-0.08429481089115143,
0.06437454372644424,
-0.06822402775287628,
0.6617901921272278,
0.4663733243942261,
-0.7265311479568481,
-0.7568690180778503,
-0.7127177119255066,
-0.2183742523193... |
IvanaLie/hf-repo | IvanaLie | 2023-11-29T03:24:57Z | 2 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:24:57Z | 2023-11-22T15:11:54.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | IvanaLie/hf-repo | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
OMazzuzi90/Ita2Sql | OMazzuzi90 | 2023-11-29T06:36:06Z | 2 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:36:06Z | 2023-11-25T11:56:48.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | OMazzuzi90/Ita2Sql | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
Panchovix/goliath-120b-exl2-4.25bpw-rpcal | Panchovix | 2023-11-30T00:28:49Z | 2 | 0 | null | [
"transformers",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:28:49Z | 2023-11-26T22:43:29.000Z | null | null | ---
license: llama2
---
EXL2 quant of alpindale/goliath-120b (https://huggingface.co/alpindale/goliath-120b), to be used on exllamav2. 4.25bpw to being to able to use CFG comfortably on 72GB VRAM. (20,21,22 for gpu split)
Calibration dataset is a cleaned, fixed pippa RP dataset, which does affect the results (in favor) for RP usage.
You can find the calibration dataset [here](https://huggingface.co/datasets/royallab/PIPPA-cleaned)
I've added a measurement.json file if you want to do your own quants.
# Original model card
# Goliath 120B
An auto-regressive causal LM created by combining 2x finetuned [Llama-2 70B](https://huggingface.co/meta-llama/llama-2-70b-hf) into one.
Please check out the quantized formats provided by [@TheBloke](https:///huggingface.co/TheBloke) and [@Panchovix](https://huggingface.co/Panchovix):
- [GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) (llama.cpp)
- [GPTQ](https://huggingface.co/TheBloke/goliath-120b-GPTQ) (KoboldAI, TGW, Aphrodite)
- [AWQ](https://huggingface.co/TheBloke/goliath-120b-AWQ) (TGW, Aphrodite, vLLM)
- [Exllamav2](https://huggingface.co/Panchovix/goliath-120b-exl2) (TGW, KoboldAI)
# Prompting Format
Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, I expect Vicuna to work the best.
# Merge process
The models used in the merge are [Xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B).
The layer ranges used are as follows:
```yaml
- range 0, 16
Xwin
- range 8, 24
Euryale
- range 17, 32
Xwin
- range 25, 40
Euryale
- range 33, 48
Xwin
- range 41, 56
Euryale
- range 49, 64
Xwin
- range 57, 72
Euryale
- range 65, 80
Xwin
```
# Screenshots
# Benchmarks
Coming soon.
# Acknowledgements
Credits goes to [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).
Special thanks to [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Panchovix/goliath-120b-exl2-4.25bpw-rpcal | [
-0.5262765884399414,
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-0.5919662714004517,
-0.27703455090522766,
-0.376080185174942,
-0.4591346681118011... |
MNC-LLM/batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu16 | MNC-LLM | 2023-11-29T16:15:50Z | 2 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:MNC-LLM/Mistral-7B-NWS-u2k-eng-cot-ep4-lr1e-05",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:15:50Z | 2023-11-27T01:00:56.000Z | null | null | ---
base_model: MNC-LLM/Mistral-7B-NWS-u2k-eng-cot-ep4-lr1e-05
tags:
- generated_from_trainer
model-index:
- name: batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu16
This model is a fine-tuned version of [MNC-LLM/Mistral-7B-NWS-u2k-eng-cot-ep4-lr1e-05](https://huggingface.co/MNC-LLM/Mistral-7B-NWS-u2k-eng-cot-ep4-lr1e-05) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | MNC-LLM/batch1_epochs4_lr1e-05_paged_adamw_32bit_cosine_length2048_warmup_0.05_max_grad1.0_grad_accu16 | [
-0.5201066732406616,
-0.5542664527893066,
0.010517803020775318,
0.2560248374938965,
-0.4152999222278595,
-0.4592888355255127,
-0.06688010692596436,
-0.33223938941955566,
0.14487037062644958,
0.3638175129890442,
-0.7959687113761902,
-0.6426063179969788,
-0.59407639503479,
0.0622748099267482... |
RyotaroOKabe/ope_mgpt_v1.2 | RyotaroOKabe | 2023-11-29T10:37:42Z | 2 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:37:42Z | 2023-11-27T14:47:04.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | RyotaroOKabe/ope_mgpt_v1.2 | [
-0.32276472449302673,
-0.22568491101264954,
0.862226128578186,
0.43461504578590393,
-0.5282993912696838,
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0.07618598639965057,
0.774603009223938,
0.2563214898109436,
-0.7852815389633179,
-0.22573868930339813,
-0.9104477763175964,
0.5715674161911011,
... |
benayas/llama-2-7b-snips_v2 | benayas | 2023-11-29T23:33:24Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:33:24Z | 2023-11-28T03:27:14.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | benayas/llama-2-7b-snips_v2 | [
-0.32276472449302673,
-0.22568491101264954,
0.862226128578186,
0.43461504578590393,
-0.5282993912696838,
0.7012975811958313,
0.7915716171264648,
0.07618598639965057,
0.774603009223938,
0.2563214898109436,
-0.7852815389633179,
-0.22573868930339813,
-0.9104477763175964,
0.5715674161911011,
... |
Frrrrrrrrank/Llama-2-7b-chat-hf-process_engineering_one_firsttwokap | Frrrrrrrrank | 2023-11-29T12:20:00Z | 2 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2023-11-29T12:20:00Z | 2023-11-28T11:48:49.000Z | null | null | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | Frrrrrrrrank/Llama-2-7b-chat-hf-process_engineering_one_firsttwokap | [
-0.5745360851287842,
-0.5525276064872742,
0.4029620587825775,
0.08021732419729233,
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0.05754851922392845,
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0.6140533685684204,
-0.7280026078224182,
-0.6281034350395203,
-0.5591193437576294,
-0.08146179467439651... |
harpone/Llama-2-7b-hf-chat-compiled-2core | harpone | 2023-11-29T09:42:14Z | 2 | 0 | null | [
"transformers",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:42:14Z | 2023-11-28T13:38:02.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | harpone/Llama-2-7b-hf-chat-compiled-2core | [
-0.3227648437023163,
-0.2256842851638794,
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0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
Optikan/V2_Image_classification__points_durs__google_vit-base-patch16-224-in21k | Optikan | 2023-11-29T15:12:19Z | 2 | 0 | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:12:19Z | 2023-11-28T13:58:29.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | Optikan/V2_Image_classification__points_durs__google_vit-base-patch16-224-in21k | [
-0.3227648437023163,
-0.2256842851638794,
0.8622258305549622,
0.4346150755882263,
-0.5282991528511047,
0.7012966275215149,
0.7915719151496887,
0.07618607580661774,
0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
nitzanb/mlm-heb-medical | nitzanb | 2023-11-29T08:36:23Z | 2 | 0 | null | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:imvladikon/alephbertgimmel-base-512",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:36:23Z | 2023-11-28T16:44:43.000Z | null | null | ---
base_model: imvladikon/alephbertgimmel-base-512
tags:
- generated_from_trainer
model-index:
- name: mlm-heb-medical
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mlm-heb-medical
This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8215
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.482 | 1.0 | 8617 | 1.3660 |
| 1.2531 | 2.0 | 17234 | 1.1445 |
| 1.126 | 3.0 | 25851 | 1.0444 |
| 1.0572 | 4.0 | 34468 | 0.9741 |
| 1.0177 | 5.0 | 43085 | 0.9232 |
| 0.9681 | 6.0 | 51702 | 0.8872 |
| 0.9515 | 7.0 | 60319 | 0.8633 |
| 0.931 | 8.0 | 68936 | 0.8433 |
| 0.9067 | 9.0 | 77553 | 0.8264 |
| 0.9072 | 10.0 | 86170 | 0.8191 |
### Framework versions
- Transformers 4.35.1
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.14.1
| null | transformers | fill-mask | null | null | null | null | null | null | null | null | null | nitzanb/mlm-heb-medical | [
-0.44653913378715515,
-0.5018031597137451,
0.09325931966304779,
0.15698832273483276,
-0.22381338477134705,
-0.45966529846191406,
0.01422242820262909,
-0.13954557478427887,
0.2343728244304657,
0.5198449492454529,
-0.9286720156669617,
-0.8200376033782959,
-0.7301910519599915,
-0.141660213470... |
folflo/Bert2Bert_m_finetined_on_HunSum_1128 | folflo | 2023-11-29T18:43:17Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:arrow",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:43:17Z | 2023-11-28T21:17:38.000Z | null | null | ---
tags:
- summarization
- generated_from_trainer
datasets:
- arrow
model-index:
- name: Bert2Bert_m_finetined_on_HunSum_1128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bert2Bert_m_finetined_on_HunSum_1128
This model is a fine-tuned version of [](https://huggingface.co/) on the arrow dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | summarization | null | null | null | null | null | null | null | null | null | folflo/Bert2Bert_m_finetined_on_HunSum_1128 | [
-0.3585442304611206,
-0.696241557598114,
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-0.06844174861907959,
-0.4289678931236267,
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-0.027938099578022957,
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0.053143903613090515,
0.16787225008010864,
-0.6447039246559143,
-0.5881914496421814,
-0.5620347261428833,
-0.1236283257... |
Rofoman/GTS-Lewd-13b-V0.21 | Rofoman | 2023-11-29T02:31:53Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T02:31:53Z | 2023-11-29T02:11:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Rofoman/GTS-Lewd-13b-V0.21 | [
-0.32276490330696106,
-0.22568461298942566,
0.862226128578186,
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0.7012966871261597,
0.7915717363357544,
0.07618622481822968,
0.7746026515960693,
0.25632232427597046,
-0.785281777381897,
-0.22573840618133545,
-0.9104479551315308,
0.5715670585632324,
... |
khanhlinh/convnext-tiny-finetuned-eurosat | khanhlinh | 2023-11-29T03:10:15Z | 2 | 0 | null | [
"transformers",
"safetensors",
"convnext",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:10:15Z | 2023-11-29T03:09:51.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | khanhlinh/convnext-tiny-finetuned-eurosat | [
-0.32276490330696106,
-0.22568461298942566,
0.862226128578186,
0.43461498618125916,
-0.5282989740371704,
0.7012966871261597,
0.7915717363357544,
0.07618622481822968,
0.7746026515960693,
0.25632232427597046,
-0.785281777381897,
-0.22573840618133545,
-0.9104479551315308,
0.5715670585632324,
... |
alpha2303/PPO-LunarLander-v2 | alpha2303 | 2023-11-29T03:55:16Z | 2 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T03:55:16Z | 2023-11-29T03:54:56.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 161.52 +/- 92.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | alpha2303/PPO-LunarLander-v2 | [
-0.0031747741159051657,
-0.3944118022918701,
0.24817673861980438,
0.3390541076660156,
-0.0878758355975151,
0.040079906582832336,
0.5000530481338501,
-0.1760786473751068,
0.28882232308387756,
0.944482684135437,
-0.6269252896308899,
-0.512033998966217,
-0.49809572100639343,
-0.27938351035118... |
giangduong/train-ver-4 | giangduong | 2023-11-29T04:41:54Z | 2 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:41:54Z | 2023-11-29T04:35:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | giangduong/train-ver-4 | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
Matupom/thainer-corpus-v2-dataset-new | Matupom | 2023-11-29T05:24:03Z | 2 | 0 | null | [
"transformers",
"safetensors",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:24:03Z | 2023-11-29T05:23:41.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | Matupom/thainer-corpus-v2-dataset-new | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
Vishal24/Keyword_category_adapter_v1 | Vishal24 | 2023-11-29T05:58:17Z | 2 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2023-11-29T05:58:17Z | 2023-11-29T05:58:07.000Z | null | null | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | Vishal24/Keyword_category_adapter_v1 | [
-0.591433048248291,
-0.5809996128082275,
0.4054913818836212,
0.09657455235719681,
-0.2866509258747101,
-0.2384118288755417,
0.033515773713588715,
-0.5095619559288025,
0.028747087344527245,
0.5746444463729858,
-0.7255621552467346,
-0.5850713849067688,
-0.5810012221336365,
-0.039173047989606... |
Tippawan/thainer_corpus_v2_model | Tippawan | 2023-11-29T07:35:36Z | 2 | 0 | null | [
"transformers",
"safetensors",
"camembert",
"token-classification",
"ner",
"generated_from_trainer",
"base_model:pythainlp/thainer-corpus-v2-base-model",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:35:36Z | 2023-11-29T07:34:57.000Z | null | null | ---
license: cc-by-4.0
base_model: pythainlp/thainer-corpus-v2-base-model
tags:
- ner
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: thainer_corpus_v2_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# thainer_corpus_v2_model
This model is a fine-tuned version of [pythainlp/thainer-corpus-v2-base-model](https://huggingface.co/pythainlp/thainer-corpus-v2-base-model) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- Precision: 0.7190
- Recall: 0.7629
- F1: 0.7403
- Accuracy: 0.9457
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3777 | 1.0 | 791 | 0.1971 | 0.6874 | 0.7258 | 0.7061 | 0.9393 |
| 0.1257 | 2.0 | 1582 | 0.1763 | 0.7257 | 0.7518 | 0.7385 | 0.9444 |
| 0.1113 | 3.0 | 2373 | 0.1739 | 0.7190 | 0.7629 | 0.7403 | 0.9457 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | Tippawan/thainer_corpus_v2_model | [
-0.3294867277145386,
-0.6545957922935486,
0.13691246509552002,
0.14148372411727905,
-0.3672970235347748,
-0.4296186864376068,
-0.22956690192222595,
-0.2078389674425125,
0.2338102012872696,
0.39353877305984497,
-0.35341084003448486,
-0.6893635392189026,
-0.8061788082122803,
-0.1275326758623... |
pig4431/TextGPT4V-7B-LORA-1E | pig4431 | 2023-11-29T07:52:40Z | 2 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T07:52:40Z | 2023-11-29T07:51:02.000Z | null | null | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
| null | peft | null | null | null | null | null | null | null | null | null | null | pig4431/TextGPT4V-7B-LORA-1E | [
-0.3588191866874695,
-0.21651539206504822,
0.3385324478149414,
0.9048228859901428,
-0.1370280385017395,
0.12893113493919373,
0.653630793094635,
0.004545632284134626,
0.2342871129512787,
0.9200680255889893,
-0.5605350732803345,
-0.1502453088760376,
-0.4179035425186157,
0.12698647379875183,
... |
nateraw/llama-2-7b-english-to-hinglish | nateraw | 2023-11-29T09:07:42Z | 2 | 1 | null | [
"peft",
"hinglish",
"en-to-hi",
"text-generation",
"en",
"hi",
"dataset:findnitai/english-to-hinglish",
"dataset:nateraw/english-to-hinglish",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-hf",
"license:apache-2.0",
"region:us"
] | 2023-11-29T09:07:42Z | 2023-11-29T08:13:22.000Z | null | null | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-hf
license: apache-2.0
widget:
- text: |
Translate from english to hinglish:
Where is the bathroom?
---
Translation:
example_title: Nature Calls
output:
text: "bathroom kaha hai?"
- text: |
Translate from english to hinglish:
Can I pet your dog?
---
Translation:
example_title: Pet a Dog
output:
text: "kya mai apke dog ko pet kar sakta hoon?"
datasets:
- findnitai/english-to-hinglish
- nateraw/english-to-hinglish
language:
- en
- hi
pipeline_tag: text-generation
tags:
- hinglish
- en-to-hi
---
# Model Card for Model ID
Lora fine-tune of Llama-2-7b for english to hinglish translation.
```python
import torch
from transformers import AutoModelForCausalLM, pipeline
PROMPT_TEMPLATE = (
f"Translate from english to hinglish:\n{{en}}\n---\nTranslation:\n"
)
model_id = "nousresearch/llama-2-7b-hf"
peft_model_id = "nateraw/llama-2-7b-english-to-hinglish"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
model.load_adapter(peft_model_id)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=model_id,
)
out = pipe(
PROMPT_TEMPLATE.format(en="Can I pet your dog?"),
return_full_text=False,
do_sample=False,
max_new_tokens=256
)[0]['generated_text']
print(out)
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [@nateraw](https://huggingface.co/nateraw)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [nousresearch/llama-2-7b-hf](https://huggingface.co/nousresearch/llama-2-7b-hf)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2 | null | peft | text-generation | null | null | null | null | null | null | null | null | null | nateraw/llama-2-7b-english-to-hinglish | [
-0.48070254921913147,
-0.5731812715530396,
0.3764045536518097,
0.1704908013343811,
-0.3740181028842926,
-0.286347359418869,
0.07142163813114166,
-0.607920229434967,
0.1830245703458786,
0.7110090255737305,
-0.6716300845146179,
-0.6270197033882141,
-0.5979321002960205,
0.023943660780787468,
... |
vvkropochev/hw5calc | vvkropochev | 2023-11-29T17:02:36Z | 2 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:02:36Z | 2023-11-29T08:26:51.000Z | null | null | ---
license: mit
---
Это учебная модель для калькулятора текстового ввода и вывода. Только операция сложения для десятизначных натуральных чисел.
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | vvkropochev/hw5calc | [
0.01535259559750557,
-0.9345703125,
0.7327014207839966,
-0.11055485904216766,
-0.5080662369728088,
0.2241898626089096,
0.33290019631385803,
-0.013446321710944176,
1.057023286819458,
-0.04994173347949982,
-0.8248473405838013,
-0.46457284688949585,
-0.6303681135177612,
-0.30549556016921997,
... |
kfkas/my_test_LLM | kfkas | 2023-11-29T09:23:31Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:23:31Z | 2023-11-29T09:17:51.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | kfkas/my_test_LLM | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
sanjit23/as | sanjit23 | 2023-11-29T09:26:17Z | 2 | 0 | null | [
"region:us"
] | 2023-11-29T09:26:17Z | 2023-11-29T09:26:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sanjit23/as | [
-0.3227648437023163,
-0.22568459808826447,
0.8622260093688965,
0.434614896774292,
-0.5282989144325256,
0.7012966275215149,
0.7915716171264648,
0.07618634402751923,
0.7746022343635559,
0.25632208585739136,
-0.7852813005447388,
-0.22573812305927277,
-0.9104481935501099,
0.5715669393539429,
... |
Minirecord/Mini_DPO_test_01 | Minirecord | 2023-11-29T10:18:19Z | 2 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:18:19Z | 2023-11-29T10:11:42.000Z | null | null | ---
license: cc-by-sa-4.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Minirecord/Mini_DPO_test_01 | [
-0.1285337656736374,
-0.18616777658462524,
0.6529129147529602,
0.4943626821041107,
-0.19319315254688263,
0.23607446253299713,
0.3607197403907776,
0.05056322365999222,
0.5793652534484863,
0.740013837814331,
-0.6508102416992188,
-0.23783965408802032,
-0.7102248668670654,
-0.04782604798674583... |
maxymoo2/checkpoint-50000 | maxymoo2 | 2023-11-29T10:24:30Z | 2 | 0 | null | [
"transformers",
"pytorch",
"pixel",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:24:30Z | 2023-11-29T10:22:16.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | maxymoo2/checkpoint-50000 | [
-0.3227648437023163,
-0.22568459808826447,
0.8622260093688965,
0.434614896774292,
-0.5282989144325256,
0.7012966275215149,
0.7915716171264648,
0.07618634402751923,
0.7746022343635559,
0.25632208585739136,
-0.7852813005447388,
-0.22573812305927277,
-0.9104481935501099,
0.5715669393539429,
... |
Gbssreejith/new-type | Gbssreejith | 2023-11-29T10:47:51Z | 2 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:47:51Z | 2023-11-29T10:29:10.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | Gbssreejith/new-type | [
-0.3227648437023163,
-0.22568459808826447,
0.8622260093688965,
0.434614896774292,
-0.5282989144325256,
0.7012966275215149,
0.7915716171264648,
0.07618634402751923,
0.7746022343635559,
0.25632208585739136,
-0.7852813005447388,
-0.22573812305927277,
-0.9104481935501099,
0.5715669393539429,
... |
harshith-7/lora-trained-sdxl-saina | harshith-7 | 2023-11-29T13:23:40Z | 2 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2023-11-29T13:23:40Z | 2023-11-29T10:52:30.000Z | null | null |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'A photo of [P] Saina Nehwal smiling brightly'
output:
url:
"image_0.png"
- text: 'A photo of [P] Saina Nehwal smiling brightly'
output:
url:
"image_1.png"
- text: 'A photo of [P] Saina Nehwal smiling brightly'
output:
url:
"image_2.png"
- text: 'A photo of [P] Saina Nehwal smiling brightly'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of [P] Saina Nehwal
license: openrail++
---
# SDXL LoRA DreamBooth - harshith-7/lora-trained-sdxl-saina
<Gallery />
## Model description
These are harshith-7/lora-trained-sdxl-saina LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of [P] Saina Nehwal to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](harshith-7/lora-trained-sdxl-saina/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | harshith-7/lora-trained-sdxl-saina | [
-0.119685098528862,
-0.35401368141174316,
0.3579433858394623,
0.10943806171417236,
-0.6392367482185364,
0.12566205859184265,
0.2693093419075012,
-0.22677236795425415,
0.38388484716415405,
0.5985144376754761,
-0.6477072238922119,
-0.5047116279602051,
-0.6546609997749329,
-0.1741485297679901... |
paul-w-qs/fine_tuned_donut_carpenter_v7 | paul-w-qs | 2023-11-29T11:20:46Z | 2 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:20:46Z | 2023-11-29T11:19:40.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | paul-w-qs/fine_tuned_donut_carpenter_v7 | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
sronger/ko-llm-llama-2-7b-LoRA-IA3 | sronger | 2023-11-29T11:34:36Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T11:34:36Z | 2023-11-29T11:32:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | sronger/ko-llm-llama-2-7b-LoRA-IA3 | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
SamuelHarner/whisper | SamuelHarner | 2023-11-29T12:25:27Z | 2 | 0 | null | [
"region:us"
] | 2023-11-29T12:25:27Z | 2023-11-29T12:25:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | SamuelHarner/whisper | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
alexkoo300/burgundy-puma | alexkoo300 | 2023-11-29T13:20:17Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:20:17Z | 2023-11-29T12:36:02.000Z | null | null | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [h2oai/h2ogpt-4096-llama2-13b-chat](https://huggingface.co/h2oai/h2ogpt-4096-llama2-13b-chat)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="alexkoo300/burgundy-puma",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?</s>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"alexkoo300/burgundy-puma",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"alexkoo300/burgundy-puma",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "alexkoo300/burgundy-puma" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?</s>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 5120, padding_idx=0)
(layers): ModuleList(
(0-39): 40 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=5120, out_features=5120, bias=False)
(k_proj): Linear(in_features=5120, out_features=5120, bias=False)
(v_proj): Linear(in_features=5120, out_features=5120, bias=False)
(o_proj): Linear(in_features=5120, out_features=5120, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=5120, out_features=13824, bias=False)
(up_proj): Linear(in_features=5120, out_features=13824, bias=False)
(down_proj): Linear(in_features=13824, out_features=5120, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=5120, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | alexkoo300/burgundy-puma | [
-0.2552868723869324,
-0.862714946269989,
0.4198070764541626,
0.24679946899414062,
-0.3151869773864746,
-0.09869039058685303,
-0.21251091361045837,
-0.3282266855239868,
0.17995375394821167,
0.35817158222198486,
-0.4543418884277344,
-0.5909875631332397,
-0.7035472393035889,
0.046868868172168... |
omriKramer/ppo-LunarLander-v2 | omriKramer | 2023-11-29T12:38:48Z | 2 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T12:38:48Z | 2023-11-29T12:38:28.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.79 +/- 18.21
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | omriKramer/ppo-LunarLander-v2 | [
-0.003174747806042433,
-0.3944118320941925,
0.2481766641139984,
0.3390541672706604,
-0.08787565678358078,
0.04007994756102562,
0.5000532269477844,
-0.17607858777046204,
0.2888225317001343,
0.9444827437400818,
-0.6269251108169556,
-0.5120341181755066,
-0.49809587001800537,
-0.27938339114189... |
Seooooooogi/lora-sdxl-bag | Seooooooogi | 2023-11-29T13:10:52Z | 2 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2023-11-29T13:10:52Z | 2023-11-29T12:42:20.000Z | null | null |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a sbu bag, red colored'
output:
url:
"image_0.png"
- text: 'a sbu bag, red colored'
output:
url:
"image_1.png"
- text: 'a sbu bag, red colored'
output:
url:
"image_2.png"
- text: 'a sbu bag, red colored'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a sbu bag
license: openrail++
---
# SDXL LoRA DreamBooth - Seooooooogi/lora-sdxl-bag
<Gallery />
## Model description
These are Seooooooogi/lora-sdxl-bag LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use a sbu bag to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Seooooooogi/lora-sdxl-bag/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | Seooooooogi/lora-sdxl-bag | [
-0.27005282044410706,
-0.4689118266105652,
0.3264138400554657,
0.07307209819555283,
-0.6332821846008301,
0.06874974071979523,
0.2422095537185669,
-0.27407100796699524,
0.6209257245063782,
0.5601798295974731,
-0.5610620975494385,
-0.5694723725318909,
-0.6851539611816406,
-0.1979730427265167... |
xiaopch/swin-tiny-patch4-window7-224-finetuned-eurosat | xiaopch | 2023-11-29T13:03:41Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:03:41Z | 2023-11-29T12:44:31.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9837037037037037
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0520
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2341 | 1.0 | 190 | 0.1160 | 0.9593 |
| 0.1813 | 2.0 | 380 | 0.0715 | 0.9752 |
| 0.1401 | 3.0 | 570 | 0.0520 | 0.9837 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | xiaopch/swin-tiny-patch4-window7-224-finetuned-eurosat | [
-0.4189855754375458,
-0.5055399537086487,
0.1015252023935318,
0.10789177566766739,
-0.28035280108451843,
-0.47125884890556335,
-0.147430419921875,
-0.38701921701431274,
-0.05523458495736122,
0.15680068731307983,
-0.7860333323478699,
-0.566081166267395,
-0.5591407418251038,
-0.1639231443405... |
wataruew/bert-base-japanese-v3-jsts | wataruew | 2023-11-29T13:22:06Z | 2 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:22:06Z | 2023-11-29T12:44:56.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | wataruew/bert-base-japanese-v3-jsts | [
-0.32276469469070435,
-0.22568437457084656,
0.8622258901596069,
0.43461552262306213,
-0.5282984375953674,
0.7012969255447388,
0.7915719747543335,
0.07618630677461624,
0.7746025323867798,
0.2563221752643585,
-0.7852816581726074,
-0.22573848068714142,
-0.9104477167129517,
0.5715667605400085,... |
vrhoward/esm2_t12_35M_UR50D-viralfinetuned | vrhoward | 2023-11-29T13:50:01Z | 2 | 0 | null | [
"transformers",
"safetensors",
"esm",
"fill-mask",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:50:01Z | 2023-11-29T12:45:45.000Z | null | null | ---
license: mit
base_model: facebook/esm2_t12_35M_UR50D
tags:
- generated_from_trainer
model-index:
- name: esm2_t12_35M_UR50D-viralfinetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M_UR50D-viralfinetuned
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5644
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 80 | 1.2801 |
| No log | 2.0 | 160 | 0.6840 |
| No log | 3.0 | 240 | 0.5645 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | fill-mask | null | null | null | null | null | null | null | null | null | vrhoward/esm2_t12_35M_UR50D-viralfinetuned | [
-0.33234331011772156,
-0.8007488250732422,
0.11487783491611481,
0.24894501268863678,
-0.3401143252849579,
-0.4428321123123169,
-0.1950695961713791,
-0.21769402921199799,
0.25334253907203674,
0.4653986990451813,
-0.9022366404533386,
-0.8130320906639099,
-0.668225109577179,
-0.09048552066087... |
seatond/testing | seatond | 2023-11-29T13:11:39Z | 2 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"region:us"
] | 2023-11-29T13:11:39Z | 2023-11-29T13:10:38.000Z | null | null | ---
library_name: peft
base_model: TheBloke/Mistral-7B-v0.1-GPTQ
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- use_exllama: False
- max_input_length: None
- exllama_config: {'version': <ExllamaVersion.ONE: 1>}
- cache_block_outputs: True
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- use_exllama: False
- max_input_length: None
- exllama_config: {'version': <ExllamaVersion.ONE: 1>}
- cache_block_outputs: True
### Framework versions
- PEFT 0.7.0.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | seatond/testing | [
-0.5557228922843933,
-0.6443319916725159,
0.3989015817642212,
0.08277954906225204,
-0.3103562295436859,
-0.26054638624191284,
0.022643566131591797,
-0.44638580083847046,
0.008491509594023228,
0.5384764075279236,
-0.7160070538520813,
-0.6812155246734619,
-0.5678321719169617,
-0.112697117030... |
NLDoc/lilt-xlm-roberta-base-finetuned-DocLayNet-large_paragraphs_ml512-v1 | NLDoc | 2023-11-29T15:55:18Z | 2 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"lilt",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:55:18Z | 2023-11-29T13:17:39.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | NLDoc/lilt-xlm-roberta-base-finetuned-DocLayNet-large_paragraphs_ml512-v1 | [
-0.32276469469070435,
-0.22568437457084656,
0.8622258901596069,
0.43461552262306213,
-0.5282984375953674,
0.7012969255447388,
0.7915719747543335,
0.07618630677461624,
0.7746025323867798,
0.2563221752643585,
-0.7852816581726074,
-0.22573848068714142,
-0.9104477167129517,
0.5715667605400085,... |
simoneprete/llama-2-7b-prova11 | simoneprete | 2023-11-29T13:29:36Z | 2 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:29:36Z | 2023-11-29T13:23:59.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | simoneprete/llama-2-7b-prova11 | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
skuma307/llama-2-7b-gosu | skuma307 | 2023-11-29T13:37:08Z | 2 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:37:08Z | 2023-11-29T13:30:24.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | skuma307/llama-2-7b-gosu | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
TheBloke/deepseek-llm-67b-chat-GPTQ | TheBloke | 2023-11-29T18:02:24Z | 2 | 1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:deepseek-ai/deepseek-llm-67b-chat",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T18:02:24Z | 2023-11-29T13:56:33.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-67b-chat
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek Llm 67B Chat
model_type: deepseek
prompt_template: 'User: {prompt}
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 -->
# Deepseek Llm 67B Chat - GPTQ
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- description start -->
# Description
This repo contains GPTQ model files for [DeepSeek's Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: DeepSeek-LLM
```
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 36.29 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 37.56 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 41.41 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 28.07 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 29.27 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 32.93 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/deepseek-llm-67b-chat-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/deepseek-llm-67b-chat-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `deepseek-llm-67b-chat-GPTQ`:
```shell
mkdir deepseek-llm-67b-chat-GPTQ
huggingface-cli download TheBloke/deepseek-llm-67b-chat-GPTQ --local-dir deepseek-llm-67b-chat-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-llm-67b-chat-GPTQ
huggingface-cli download TheBloke/deepseek-llm-67b-chat-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir deepseek-llm-67b-chat-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir deepseek-llm-67b-chat-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-67b-chat-GPTQ --local-dir deepseek-llm-67b-chat-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.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/deepseek-llm-67b-chat-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/deepseek-llm-67b-chat-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
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: `deepseek-llm-67b-chat-GPTQ`
7. The model will automatically load, and is now ready for use!
8. 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.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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/deepseek-llm-67b-chat-GPTQ --port 3000 --quantize gptq --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'''User: {prompt}
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_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/deepseek-llm-67b-chat-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''User: {prompt}
Assistant:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' 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_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: DeepSeek's Deepseek Llm 67B Chat
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/deepseek-llm-67b-chat-GPTQ | [
-0.6775489449501038,
-0.7009779214859009,
0.41664445400238037,
0.1472833752632141,
-0.2638595402240753,
-0.24001850187778473,
0.004304717760533094,
-0.3901370167732239,
-0.017403535544872284,
0.4502841532230377,
-0.6994842290878296,
-0.6539867520332336,
-0.39210739731788635,
-0.22967939078... |
khanhnto/ilyto1 | khanhnto | 2023-11-29T14:24:29Z | 2 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:24:29Z | 2023-11-29T14:12:09.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | khanhnto/ilyto1 | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
leo99/db-mult-inst-3000 | leo99 | 2023-11-29T14:20:35Z | 2 | 0 | null | [
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T14:20:35Z | 2023-11-29T14:18:12.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | leo99/db-mult-inst-3000 | [
-0.32276451587677,
-0.2256847620010376,
0.8622261881828308,
0.43461543321609497,
-0.5282991528511047,
0.7012973427772522,
0.7915714979171753,
0.07618623226881027,
0.7746027708053589,
0.25632160902023315,
-0.7852810025215149,
-0.22573824226856232,
-0.9104477763175964,
0.5715674161911011,
... |
ycycyc02/chatglm3-6b | ycycyc02 | 2023-11-29T15:41:07Z | 2 | 0 | null | [
"transformers",
"pytorch",
"chatglm",
"feature-extraction",
"custom_code",
"region:us"
] | 2023-11-29T15:41:07Z | 2023-11-29T14:18:49.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | ycycyc02/chatglm3-6b | [
-0.32276451587677,
-0.2256847620010376,
0.8622261881828308,
0.43461543321609497,
-0.5282991528511047,
0.7012973427772522,
0.7915714979171753,
0.07618623226881027,
0.7746027708053589,
0.25632160902023315,
-0.7852810025215149,
-0.22573824226856232,
-0.9104477763175964,
0.5715674161911011,
... |
personal1802/32 | personal1802 | 2023-11-29T14:33:38Z | 2 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:latent-consistency/lcm-lora-sdv1-5",
"region:us"
] | 2023-11-29T14:33:38Z | 2023-11-29T14:27:14.000Z | null | null | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/WHITE.png
base_model: latent-consistency/lcm-lora-sdv1-5
instance_prompt: null
---
# zhmixDramatic_v30
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/personal1802/32/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | personal1802/32 | [
-0.06794683635234833,
0.3049960136413574,
0.2466048300266266,
0.3339822292327881,
-0.6243401169776917,
-0.04190049320459366,
0.29095762968063354,
-0.3785959482192993,
0.11933661252260208,
0.4300157129764557,
-0.6013256907463074,
-0.6392835974693298,
-0.5759520530700684,
-0.3328011035919189... |
TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF | TheBloke | 2023-11-30T00:07:02Z | 2 | 0 | null | [
"transformers",
"gguf",
"llama",
"en",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"base_model:harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:07:02Z | 2023-11-29T15:09:34.000Z | null | null | ---
base_model: harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: L
model_name: Open Llama 3B V2 Wizard Evol Instuct V2 196K
model_type: llama
prompt_template: '### HUMAN:
{prompt}
### RESPONSE:
'
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 -->
# Open Llama 3B V2 Wizard Evol Instuct V2 196K - GGUF
- Model creator: [L](https://huggingface.co/harborwater)
- Original model: [Open Llama 3B V2 Wizard Evol Instuct V2 196K](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k)
<!-- description start -->
## Description
This repo contains GGUF format model files for [L's Open Llama 3B V2 Wizard Evol Instuct V2 196K](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF)
* [L's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Human-Response
```
### HUMAN:
{prompt}
### RESPONSE:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_0.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_0.gguf) | Q4_0 | 4 | 1.98 GB| 4.48 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q2_K.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q2_K.gguf) | Q2_K | 2 | 2.15 GB| 4.65 GB | smallest, significant quality loss - not recommended for most purposes |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_S.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_S.gguf) | Q3_K_S | 3 | 2.19 GB| 4.69 GB | very small, high quality loss |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_M.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_M.gguf) | Q3_K_M | 3 | 2.27 GB| 4.77 GB | very small, high quality loss |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_L.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q3_K_L.gguf) | Q3_K_L | 3 | 2.34 GB| 4.84 GB | small, substantial quality loss |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_0.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_0.gguf) | Q5_0 | 5 | 2.40 GB| 4.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_S.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_S.gguf) | Q4_K_S | 4 | 2.40 GB| 4.90 GB | small, greater quality loss |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf) | Q4_K_M | 4 | 2.58 GB| 5.08 GB | medium, balanced quality - recommended |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_K_S.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_K_S.gguf) | Q5_K_S | 5 | 2.60 GB| 5.10 GB | large, low quality loss - recommended |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_K_M.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q5_K_M.gguf) | Q5_K_M | 5 | 2.76 GB| 5.26 GB | large, very low quality loss - recommended |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q6_K.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q6_K.gguf) | Q6_K | 6 | 3.64 GB| 6.14 GB | very large, extremely low quality loss |
| [open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q8_0.gguf](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF/blob/main/open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q8_0.gguf) | Q8_0 | 8 | 3.64 GB| 6.14 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF and below it, a specific filename to download, such as: open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### HUMAN:\n{prompt}\n\n### RESPONSE:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf", # Download the model file first
n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"### HUMAN:\n{prompt}\n\n### RESPONSE:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: L's Open Llama 3B V2 Wizard Evol Instuct V2 196K
Trained on 1 epoch of the WizardLM_evol_instruct_v2_196k dataset
Link to [GGUF](https://huggingface.co/maddes8cht/harborwater-open-llama-3b-v2-wizard-evol-instuct-v2-196k-gguf) formats.
Prompt template:
```
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 36.33 |
| ARC (25-shot) | 41.81 |
| HellaSwag (10-shot) | 73.01 |
| MMLU (5-shot) | 26.36 |
| TruthfulQA (0-shot) | 38.99 |
| Winogrande (5-shot) | 66.69 |
| GSM8K (5-shot) | 1.9 |
| DROP (3-shot) | 5.57 |
<!-- original-model-card end -->
| null | transformers | null | null | null | null | null | null | null | null | null | null | TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF | [
-0.6149698495864868,
-0.8219457268714905,
0.3581355810165405,
0.29467952251434326,
-0.2592826783657074,
-0.02446930855512619,
0.10147178918123245,
-0.5853812098503113,
0.3199213743209839,
0.2597169578075409,
-0.7007194757461548,
-0.5436265468597412,
-0.44714826345443726,
0.0389907620847225... |
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