modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 12:29:30
| downloads
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223M
| likes
int64 0
11.7k
| library_name
stringclasses 548
values | tags
listlengths 1
4.05k
| pipeline_tag
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values | createdAt
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iloncka/tinynet_e.in1k_ep_20
|
iloncka
| 2023-12-26T08:47:14Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-12-26T08:44:14Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
LoneStriker/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-4.0bpw-h6-exl2
|
LoneStriker
| 2023-12-26T08:46:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixtral",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T08:32:21Z |
---
license: cc-by-nc-4.0
---

---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Alpaca **prompting format**(or just directly download the SillyTavern instruct preset [here](https://files.catbox.moe/0ohmco.json))
---
Beeg noromaid on ***steroids***. Suitable for RP, ERP.
This time based on Mixtral Instruct, seems to do wonders!
This model was trained for 8h(v1) + 8h(v2) + 12h(v3) on customized modified datasets, focusing on RP, uncensoring, and a modified version of the Alpaca prompting (that was already used in LimaRP), which should be at the same conversational level as ChatLM or Llama2-Chat without adding any additional special tokens.
If you wanna have more infos about this model(and v1 + v2) you can check out [my blog post](https://ikaridevgit.github.io/index.html?p=7&blog=blogid-6&bo=true)
[Recommended settings - Settings 1](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-v3/discussions/1)
[Recommended settings - Settings 2 (idk if they are any good)](https://files.catbox.moe/fv4xhu.json)
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of Noromaid-v0.1-mixtral-8x7b-Instruct-v3.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Datasets used:
- Aesir 1 and 2 ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe))
- [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia))
- [ToxicDPO-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) ([unalignment orga repo](https://huggingface.co/unalignment) + [Undi](https://huggingface.co/Undi95))
- [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgu))
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
SaladSlayer00/image_classification_resnet
|
SaladSlayer00
| 2023-12-26T08:44:10Z | 13 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"resnet",
"image-classification",
"generated_from_keras_callback",
"base_model:microsoft/resnet-50",
"base_model:finetune:microsoft/resnet-50",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-23T15:31:53Z |
---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_keras_callback
model-index:
- name: SaladSlayer00/image_classification_resnet
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. -->
# SaladSlayer00/image_classification_resnet
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2581
- Validation Loss: 1.6399
- Validation Accuracy: 0.5823
- Epoch: 11
## 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': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:---------------:|:-------------------:|:-----:|
| 7.0750 | 4.8746 | 0.0090 | 0 |
| 4.6468 | 4.5229 | 0.0538 | 1 |
| 4.3211 | 4.1033 | 0.1209 | 2 |
| 3.8784 | 3.6736 | 0.1859 | 3 |
| 3.4274 | 3.2193 | 0.2419 | 4 |
| 3.0071 | 2.8524 | 0.3012 | 5 |
| 2.6239 | 2.5632 | 0.3651 | 6 |
| 2.2925 | 2.2959 | 0.4233 | 7 |
| 1.9792 | 2.1138 | 0.4882 | 8 |
| 1.7199 | 1.9271 | 0.5174 | 9 |
| 1.4845 | 1.7643 | 0.5666 | 10 |
| 1.2581 | 1.6399 | 0.5823 | 11 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
TheBloke/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-AWQ
|
TheBloke
| 2023-12-26T08:41:19Z | 25 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"base_model:NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3",
"base_model:quantized:NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-12-26T08:06:41Z |
---
base_model: NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev and Undi
model_name: Noromaid V0.1 Mixtral 8X7B Instruct v3
model_type: mixtral
prompt_template: '### Instruction:
{system_message}
### Input:
{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 -->
# Noromaid V0.1 Mixtral 8X7B Instruct v3 - AWQ
- Model creator: [IkariDev and Undi](https://huggingface.co/NeverSleep)
- Original model: [Noromaid V0.1 Mixtral 8X7B Instruct v3](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- description start -->
## Description
This repo contains AWQ model files for [IkariDev and Undi's Noromaid V0.1 Mixtral 8X7B Instruct v3](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3).
**MIXTRAL AWQ**
This is a Mixtral AWQ model.
For AutoAWQ inference, please install AutoAWQ 0.1.8 or later.
Support via Transformers is coming soon, via this PR: https://github.com/huggingface/transformers/pull/27950 which should be merged to Transformers `main` very soon.
vLLM: version 0.2.6 is confirmed to support Mixtral AWQs.
TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!)
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
AWQ models are supported by (note that not all of these may support Mixtral models yet - see above):
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF)
* [IkariDev and Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Instruction-Input-Response
```
### Instruction:
{system_message}
### Input:
{prompt}
### Response:
```
<!-- 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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 24.65 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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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: `Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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'''### Instruction:
{system_message}
### Input:
{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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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'''### Instruction:
{system_message}
### Input:
{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/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-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'''### Instruction:
{system_message}
### Input:
{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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: IkariDev and Undi's Noromaid V0.1 Mixtral 8X7B Instruct v3

---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Alpaca **prompting format**(or just directly download the SillyTavern instruct preset [here](https://files.catbox.moe/0ohmco.json))
---
Beeg noromaid on ***steroids***. Suitable for RP, ERP.
This time based on Mixtral Instruct, seems to do wonders!
This model was trained for 8h(v1) + 8h(v2) + 12h(v3) on customized modified datasets, focusing on RP, uncensoring, and a modified version of the Alpaca prompting (that was already used in LimaRP), which should be at the same conversational level as ChatLM or Llama2-Chat without adding any additional special tokens.
If you wanna have more infos about this model(and v1 + v2) you can check out [my blog post](https://ikaridevgit.github.io/index.html?p=7&blog=blogid-6&bo=true)
[Recommended settings - Settings 1](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-v3/discussions/1)
[Recommended settings - Settings 2 (idk if they are any good)](https://files.catbox.moe/fv4xhu.json)
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of Noromaid-v0.1-mixtral-8x7b-Instruct-v3.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Datasets used:
- Aesir 1 and 2 ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe))
- [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia))
- [ToxicDPO-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) ([unalignment orga repo](https://huggingface.co/unalignment) + [Undi](https://huggingface.co/Undi95))
- [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgu))
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
laylabitar/DeID_MonsterAPI
|
laylabitar
| 2023-12-26T08:40:11Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-26T08:40:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
tb2pi-persistent/Llama-2-7b-chat-hf-tb2pi-peft-v6
|
tb2pi-persistent
| 2023-12-26T08:38:09Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-26T08:38:05Z |
---
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]
### Framework versions
- PEFT 0.7.1
|
TheBloke/Nous-Hermes-2-Yi-34B-GGUF
|
TheBloke
| 2023-12-26T08:16:32Z | 859 | 41 |
transformers
|
[
"transformers",
"gguf",
"yi",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"base_model:NousResearch/Nous-Hermes-2-Yi-34B",
"base_model:quantized:NousResearch/Nous-Hermes-2-Yi-34B",
"license:apache-2.0",
"region:us",
"conversational"
] | null | 2023-12-26T07:55:32Z |
---
base_model: NousResearch/Nous-Hermes-2-Yi-34B
inference: false
language:
- en
license: apache-2.0
model-index:
- name: Nous-Hermes-2-Yi-34B
results: []
model_creator: NousResearch
model_name: Nous Hermes 2 Yi 34B
model_type: yi
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- yi
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
---
<!-- 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 -->
# Nous Hermes 2 Yi 34B - GGUF
- Model creator: [NousResearch](https://huggingface.co/NousResearch)
- Original model: [Nous Hermes 2 Yi 34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [NousResearch's Nous Hermes 2 Yi 34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B).
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/Nous-Hermes-2-Yi-34B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF)
* [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
<!-- 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 -->
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [nous-hermes-2-yi-34b.Q2_K.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q2_K.gguf) | Q2_K | 2 | 14.56 GB| 17.06 GB | smallest, significant quality loss - not recommended for most purposes |
| [nous-hermes-2-yi-34b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
| [nous-hermes-2-yi-34b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.64 GB| 19.14 GB | very small, high quality loss |
| [nous-hermes-2-yi-34b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
| [nous-hermes-2-yi-34b.Q4_0.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [nous-hermes-2-yi-34b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q4_K_S.gguf) | Q4_K_S | 4 | 19.54 GB| 22.04 GB | small, greater quality loss |
| [nous-hermes-2-yi-34b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
| [nous-hermes-2-yi-34b.Q5_0.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [nous-hermes-2-yi-34b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
| [nous-hermes-2-yi-34b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
| [nous-hermes-2-yi-34b.Q6_K.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
| [nous-hermes-2-yi-34b.Q8_0.gguf](https://huggingface.co/TheBloke/Nous-Hermes-2-Yi-34B-GGUF/blob/main/nous-hermes-2-yi-34b.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 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/Nous-Hermes-2-Yi-34B-GGUF and below it, a specific filename to download, such as: nous-hermes-2-yi-34b.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/Nous-Hermes-2-Yi-34B-GGUF nous-hermes-2-yi-34b.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/Nous-Hermes-2-Yi-34B-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/Nous-Hermes-2-Yi-34B-GGUF nous-hermes-2-yi-34b.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 nous-hermes-2-yi-34b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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="./nous-hermes-2-yi-34b.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./nous-hermes-2-yi-34b.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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: NousResearch's Nous Hermes 2 Yi 34B
# Nous Hermes 2 - Yi-34B

## Model description
Nous Hermes 2 - Yi-34B is a state of the art Yi Fine-tune.
Nous Hermes 2 Yi 34B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape.
# Table of Contents
1. [Example Outputs](#example-outputs)
- Discussing the Laws of Gravity
- Create a Flask based FTP Server
3. [Benchmark Results](#benchmark-results)
- GPT4All
- AGIEval
- BigBench
- Averages Compared
4. [Prompt Format](#prompt-format)
5. [Quantized Models](#quantized-models)
## Example Outputs
### Discussions about the Law of Gravity:

### Create an FTP Server in FLASK:

## Benchmark Results
Nous-Hermes 2 on Yi 34B outperforms all Nous-Hermes & Open-Hermes models of the past, achieving new heights in all benchmarks for a Nous Research LLM as well as surpassing many popular finetunes.
# Benchmarks Compared
### GPT4All:

### AGIEval:

### BigBench:

### TruthfulQA:

## GPT4All
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.6067|_ |0.0143|
| | |acc_norm|0.6416|_ |0.0140|
|arc_easy | 0|acc |0.8594|_ |0.0071|
| | |acc_norm|0.8569|_ |0.0072|
|boolq | 1|acc |0.8859|_ |0.0056|
|hellaswag | 0|acc |0.6407|_ |0.0048|
| | |acc_norm|0.8388|_ |0.0037|
|openbookqa | 0|acc |0.3520|_ |0.0214|
| | |acc_norm|0.4760|_ |0.0224|
|piqa | 0|acc |0.8215|_ |0.0089|
| | |acc_norm|0.8303|_ |0.0088|
|winogrande | 0|acc |0.7908|_ |0.0114|
Average: 76.00%
```
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3189|_ |0.0293|
| | |acc_norm|0.2953|_ |0.0287|
|agieval_logiqa_en | 0|acc |0.5438|_ |0.0195|
| | |acc_norm|0.4977|_ |0.0196|
|agieval_lsat_ar | 0|acc |0.2696|_ |0.0293|
| | |acc_norm|0.2087|_ |0.0269|
|agieval_lsat_lr | 0|acc |0.7078|_ |0.0202|
| | |acc_norm|0.6255|_ |0.0215|
|agieval_lsat_rc | 0|acc |0.7807|_ |0.0253|
| | |acc_norm|0.7063|_ |0.0278|
|agieval_sat_en | 0|acc |0.8689|_ |0.0236|
| | |acc_norm|0.8447|_ |0.0253|
|agieval_sat_en_without_passage| 0|acc |0.5194|_ |0.0349|
| | |acc_norm|0.4612|_ |0.0348|
|agieval_sat_math | 0|acc |0.4409|_ |0.0336|
| | |acc_norm|0.3818|_ |0.0328|
Average: 50.27%
```
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5737|_ |0.0360|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7263|_ |0.0232|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3953|_ |0.0305|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.4457|_ |0.0263|
| | |exact_str_match |0.0000|_ |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2820|_ |0.0201|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2186|_ |0.0156|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4733|_ |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.5200|_ |0.0224|
|bigbench_navigate | 0|multiple_choice_grade|0.4910|_ |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7495|_ |0.0097|
|bigbench_ruin_names | 0|multiple_choice_grade|0.5938|_ |0.0232|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.3808|_ |0.0154|
|bigbench_snarks | 0|multiple_choice_grade|0.8066|_ |0.0294|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5101|_ |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3850|_ |0.0154|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2160|_ |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1634|_ |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4733|_ |0.0289|
Average: 46.69%
```
TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4333|_ |0.0173|
| | |mc2 |0.6034|_ |0.0149|
```
Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
```
| Bench | OpenHermes-2.5 Mistral 7B | Nous-Hermes-2-Yi-34B | Change/OpenHermes2 |
|---------------|---------------------------|----------------------|--------------------|
|GPT4All | 73.12| 76.00| +2.88|
|---------------------------------------------------------------------------------------|
|BigBench | 40.96| 46.69| +5.73|
|---------------------------------------------------------------------------------------|
|AGI Eval | 43.07| 50.27| +7.20|
|---------------------------------------------------------------------------------------|
|TruthfulQA | 53.04| 60.34| +7.30|
|---------------------------------------------------------------------------------------|
|Total Score | 210.19| 233.30| +23.11|
|---------------------------------------------------------------------------------------|
|Average Total | 52.38| 58.33| +5.95|
```
# Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:

# Quantized Models:
[todo]
[<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)
<!-- original-model-card end -->
|
ostapeno/rsgd_full_1B_coarsegrained_poly_router_dir_none_similar10
|
ostapeno
| 2023-12-26T08:12:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:24:01Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| wiki_hop_original_generate_object_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| wiki_hop_original_generate_subject_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
Last updated on: 2023-12-26 08:11:46+00:00
|
Adammz/ruBert-base-1-third
|
Adammz
| 2023-12-26T08:12:02Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"base_model:finetune:ai-forever/ruBert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-26T07:57:15Z |
---
license: apache-2.0
base_model: ai-forever/ruBert-base
tags:
- generated_from_trainer
model-index:
- name: ruBert-base-1-third
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. -->
# ruBert-base-1-third
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3779
- eval_accuracy: 0.9186
- eval_runtime: 6.5943
- eval_samples_per_second: 1819.757
- eval_steps_per_second: 56.867
- epoch: 7.12
- step: 10678
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
isek-ai/SDPrompt-RetNet-v2-beta
|
isek-ai
| 2023-12-26T08:10:53Z | 89 | 4 |
transformers
|
[
"transformers",
"safetensors",
"retnet",
"text-generation",
"custom_code",
"en",
"dataset:isek-ai/danbooru-tags-2016-2023",
"license:mit",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-16T09:27:57Z |
---
license: mit
datasets:
- isek-ai/danbooru-tags-2016-2023
language:
- en
library_name: transformers
---
# SDPrompt-RetNet-v2-beta
This model is a pretrained RetNet model trained from scratch using https://github.com/syncdoth/RetNet.
It achieves the following results on the evaluation set:
- Loss: 0.5923
## Usage
```bash
pip install transformers safetensors
```
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
MODEL_NAME = "isek-ai/SDPrompt-RetNet-v2-beta"
DEVICE = "cuda"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model= AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16, # or torch.bfloat16
trust_remote_code=True,
).to(DEVICE)
model.eval()
streamer = TextStreamer(tokenizer)
prompt = "1girl"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
_ = model.generate(
inputs["input_ids"],
max_new_tokens=256,
do_sample=True,
top_p=0.9,
top_k=20,
temperature=0.9,
streamer=streamer,
)
# 1girl, :<, bag, black hair, blurry, bokeh, cloud, depth of field, from side, long sleeves, night, outdoors, pleated skirt, power lines, purple eyes, road, scenery, shoes, shoulder bag,gasm, sidelocks, sign, skirt,let's drawsaurus, skylight smile, sneakers, standing, star (sky), sweater, town, traffic cone, utility pole, vending machine, wide-eyed, window, wooden box, yellow skirt,ization, zettai ryouiki, zoom layer, white footwear, zipper, zipper pull tab, zipperland sheet, zombie pose, ladder, leaning back, leg up, looking to the side,let, miniskirt, motion blur, musical note, open mouth, part
```
## Model description
This model is trained with **only Danbooru tags** to generate prompts for image generation models.
## Training data
- [isek-ai/danbooru-tags-2016-2023](https://huggingface.co/datasets/isek-ai/danbooru-tags-2016-2023)
### Dataset filtering
TODO
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.975 | 0.07 | 500 | 1.0005 |
| 0.7549 | 0.13 | 1000 | 0.7604 |
| 0.6923 | 0.2 | 1500 | 0.7090 |
| 0.6753 | 0.26 | 2000 | 0.6778 |
| 0.6591 | 0.33 | 2500 | 0.6568 |
| 0.6337 | 0.39 | 3000 | 0.6429 |
| 0.6288 | 0.46 | 3500 | 0.6319 |
| 0.624 | 0.53 | 4000 | 0.6218 |
| 0.62 | 0.59 | 4500 | 0.6172 |
| 0.603 | 0.66 | 5000 | 0.6090 |
| 0.5931 | 0.72 | 5500 | 0.6032 |
| 0.5957 | 0.79 | 6000 | 0.5986 |
| 0.5972 | 0.85 | 6500 | 0.5948 |
| 0.5928 | 0.92 | 7000 | 0.5926 |
| 0.5904 | 0.98 | 7500 | 0.5923 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
3838seungsheon/Ko_test_2.0
|
3838seungsheon
| 2023-12-26T08:04:49Z | 2 | 1 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2023-12-26T05:00:21Z |
---
library_name: peft
base_model: LDCC/LDCC-Instruct-Llama-2-ko-13B-v1.6
---
# 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: 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 0.7.0
|
OpenBuddy/openbuddy-mixtral-8x7b-v15.4
|
OpenBuddy
| 2023-12-26T07:57:34Z | 1,539 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-22T16:36:41Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)

# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性,可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事,不要使用这些模型,以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供,不附带任何种类的明示或暗示的保证,包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下,作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任(无论是合同、侵权还是其他原因)承担责任。
使用OpenBuddy即表示您同意这些条款和条件,并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
ostapeno/rsgd_full_1B_coarsegrained_poly_router_dir_lib_embeddings_distinct10
|
ostapeno
| 2023-12-26T07:52:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:23:30Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ultrachat_25_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| niv2_explanation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| aeslc_1_0_0_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| high_school_psychology_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
Last updated on: 2023-12-26 07:51:49+00:00
|
ntc-ai/SDXL-LoRA-slider.arcana-character
|
ntc-ai
| 2023-12-26T07:48:41Z | 8 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-26T07:48:38Z |
---
language:
- en
thumbnail: "images/evaluate/arcana character.../arcana character_17_3.0.png"
widget:
- text: arcana character
output:
url: images/arcana character_17_3.0.png
- text: arcana character
output:
url: images/arcana character_19_3.0.png
- text: arcana character
output:
url: images/arcana character_20_3.0.png
- text: arcana character
output:
url: images/arcana character_21_3.0.png
- text: arcana character
output:
url: images/arcana character_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "arcana character"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - arcana character (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/arcana character_17_-3.0.png" width=256 height=256 /> | <img src="images/arcana character_17_0.0.png" width=256 height=256 /> | <img src="images/arcana character_17_3.0.png" width=256 height=256 /> |
| <img src="images/arcana character_19_-3.0.png" width=256 height=256 /> | <img src="images/arcana character_19_0.0.png" width=256 height=256 /> | <img src="images/arcana character_19_3.0.png" width=256 height=256 /> |
| <img src="images/arcana character_20_-3.0.png" width=256 height=256 /> | <img src="images/arcana character_20_0.0.png" width=256 height=256 /> | <img src="images/arcana character_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
arcana character
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.arcana-character', weight_name='arcana character.safetensors', adapter_name="arcana character")
# Activate the LoRA
pipe.set_adapters(["arcana character"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, arcana character"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 630+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
Noob/ddpm-celebahq-finetuned-butterflies-2epochs
|
Noob
| 2023-12-26T07:40:41Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-12-26T07:38:49Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 用法
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained(' Noob/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
'''
|
krishnadasar-sudheer-kumar/Reinforce-Pixelcopter-PLE-v3
|
krishnadasar-sudheer-kumar
| 2023-12-26T07:36:49Z | 0 | 1 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T07:36:45Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 11.80 +/- 11.15
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
LoneStriker/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-26T07:30:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixtral",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T07:20:57Z |
---
license: cc-by-nc-4.0
---

---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Alpaca **prompting format**(or just directly download the SillyTavern instruct preset [here](https://files.catbox.moe/0ohmco.json))
---
Beeg noromaid on ***steroids***. Suitable for RP, ERP.
This time based on Mixtral Instruct, seems to do wonders!
This model was trained for 8h(v1) + 8h(v2) + 12h(v3) on customized modified datasets, focusing on RP, uncensoring, and a modified version of the Alpaca prompting (that was already used in LimaRP), which should be at the same conversational level as ChatLM or Llama2-Chat without adding any additional special tokens.
If you wanna have more infos about this model(and v1 + v2) you can check out [my blog post](https://ikaridevgit.github.io/index.html?p=7&blog=blogid-6&bo=true)
[Recommended settings - Settings 1](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-v3/discussions/1)
[Recommended settings - Settings 2 (idk if they are any good)](https://files.catbox.moe/fv4xhu.json)
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of Noromaid-v0.1-mixtral-8x7b-Instruct-v3.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Datasets used:
- Aesir 1 and 2 ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe))
- [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia))
- [ToxicDPO-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) ([unalignment orga repo](https://huggingface.co/unalignment) + [Undi](https://huggingface.co/Undi95))
- [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgu))
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
ostapeno/rsgd_full_1B_finegrained_poly_router_dir_lora_sim_distinct10
|
ostapeno
| 2023-12-26T07:26:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:23:21Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ultrachat_25_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| niv2_explanation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| aeslc_1_0_0_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| high_school_psychology_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
Last updated on: 2023-12-26 07:25:50+00:00
|
krishnadasar-sudheer-kumar/Reinforce-v2
|
krishnadasar-sudheer-kumar
| 2023-12-26T07:16:59Z | 0 | 1 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T07:16:46Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
justinj92/phi-med-v1
|
justinj92
| 2023-12-26T07:16:14Z | 11 | 1 |
transformers
|
[
"transformers",
"safetensors",
"phi-msft",
"text-generation",
"medical",
"custom_code",
"en",
"dataset:BI55/MedText",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T06:02:02Z |
---
license: apache-2.0
datasets:
- BI55/MedText
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
---
# Model Card for Phi-Med-V1
<!-- Provide a quick summary of what the model is/does. -->
Microsoft Phi2 Finetuned on Medical Text Data
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [JJ]
- **Model type:** [SLM]
- **Finetuned from model:** [microsoft/Phi-2]
## 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. -->
Testing the effectivness of Finetuning SLMs
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Not Allowed as this is for research only
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Model can still Halucinate.
## 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. -->
MedText Dataset from HuggingFace
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
SFT using HF Transformers
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** A10 GPU VMs [2x24GB A10]
- **Hours used:** [3]
- **Cloud Provider:** [Azure]
- **Compute Region:** [North Europe (Dublin)]
- Experiments were conducted using Azure in region northeurope, which has a carbon efficiency of 0.62 kgCO$_2$eq/kWh. A cumulative of 100 hours of computation was performed on hardware of type A10 (TDP of 350W).
- Total emissions are estimated to be 21.7 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
- Estimations were conducted using the [https://mlco2.github.io/impact#compute][MachineLearning Impact calculator]
## Technical Specifications [optional]
### Compute Infrastructure
[Azure]
#### Hardware
[NV72ads A10 GPU VMs]
#### Software
[Axolotl]
## Model Card Authors [optional]
[JJ]
## Model Card Contact
[JJ]
|
richardburleigh/SuperQA-7B-v0.1
|
richardburleigh
| 2023-12-26T07:07:20Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"RAG",
"QA",
"SQuAD",
"Question Answering",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T06:35:03Z |
---
license: gpl-3.0
language:
- en
library_name: transformers
tags:
- RAG
- QA
- SQuAD
- Question Answering
---
## Model Card for SuperQA-7B
This model is a fine-tuned version of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically designed for Question Answering (QA) tasks. It has been trained on a private dataset comprising 120,000 document, question, and answer pairs.
To my knowledge, this is the most capable 7B model for Retrieval Augmented Generation (RAG) tasks.
SuperQA responds in Markdown format.
## Prompt Format
This model was trained only with the following prompt:
```
<s>[INST] Respond with a detailed and relevant answer to my question using only information from the provided context.
<|context|>
<|doc|>
{Your document}
<|/doc|>
<|/context|>
<|question|>{Your question?}<|/question|> [/INST]
```
## Limitations
While the model is designed to be accurate and relevant, its performance is contingent on the quality and relevance of the provided context. Answers may be less accurate if the context is insufficient or not directly related to the question. Additionally, the model's training on a specific dataset may limit its effectiveness in answering questions outside the scope of the training data.
## Disclaimer
This model is provided as-is without any guarantees of performance or accuracy. Users should not rely solely on this model for critical decisions or interpretations. The developers of this model are not responsible for any direct or indirect consequences arising from its use. It is the responsibility of the user to ensure that the model's output is appropriate for their specific context and requirements.
|
ostapeno/selector_1B_finegrained_poly_router_dir_lora_sim_distinct10
|
ostapeno
| 2023-12-26T07:05:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:32:26Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ultrachat_25_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| niv2_explanation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
Last updated on: 2023-12-26 07:04:43+00:00
|
lucyknada/Loyal-Toppy-Bruins-Maid-7B-DARE-exl2-8bpw
|
lucyknada
| 2023-12-26T06:51:59Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T06:23:36Z |
---
license: cc-by-nc-4.0
tags:
- merge
---
## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF
## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF
## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF
## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF

<!-- description start -->
## Description
This repository hosts FP16 files for **Loyal-Toppy-Bruins-Maid-7B**, a 7B model aimed at having engaging RP with solid character card adherence and being a smart cookie at the same time.
Its foundation is [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha), notable for its performance in the LMSYS Chatbot Arena, even surpassing GPT-3.5-Turbo-1106. The model incorporates [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2), a [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) derivative with Alpaca RP data tuning.
The other foundational model is [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7), chosen for its strong RP performance and Alpaca format training, with a diverse dataset including PIPPA, rpbuild, and LimaRP.
[Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B), known for its creativity, brings in useful RP data from various sources. It ranks first among 7B models on [OpenRouter](https://openrouter.ai/rankings) for a good reason.
[NeverSleep/Noromaid-7b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-7b-v0.1.1), a Mistral finetune with unique RP data not present in other models, was also added for bringing in a unique RP dataset and being a well-regarded RP model.
The models were merged using the DARE ties method, with a targeted 1.2 absolute weight and high density (0.5-0.6), as discussed in the [MergeKit GitHub Repo](https://github.com/cg123/mergekit/issues/26).
Currently, this model ranks at the top of my personal RP unit test benchmark and scored a very solid 20 on [lilblam's LLM Logic Test](https://docs.google.com/spreadsheets/d/1NgHDxbVWJFolq8bLvLkuPWKC7i_R6I6W/edit#gid=1278290632). My first impressions of it for RPing are very good but, admittedly, this model came out of the oven today so I haven't played it with it too much 😊
### The sauce
```
models: # Top-Loyal-Bruins-Maid-DARE-7B_v2
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: rwitz/go-bruins-v2 # MetamathCybertronStarling base
parameters:
weight: 0.5
density: 0.6
- model: chargoddard/loyal-piano-m7 # Pull in some PIPPA/LimaRP/Orca/rpguild
parameters:
weight: 0.5
density: 0.6
- model: Undi95/Toppy-M-7B
parameters:
weight: 0.1
density: 0.5
- model: NeverSleep/Noromaid-7b-v0.1.1
parameters:
weight: 0.1
density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
I found the best SillyTavern results from using the Noromaid template.
SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
Otherwise, I tried to ensure that all of the underlying merged models were Alpaca favored.
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
|
pavitemple/finetuned-Accident-SingleLabel-Final
|
pavitemple
| 2023-12-26T06:49:05Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-25T19:39:00Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-Accident-SingleLabel-Final
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. -->
# finetuned-Accident-SingleLabel-Final
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0015
- Accuracy: 0.6176
## 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
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.08 | 4 | 1.7644 | 0.1304 |
| No log | 1.08 | 8 | 1.6450 | 0.4783 |
| 1.6076 | 2.08 | 12 | 1.4210 | 0.5652 |
| 1.6076 | 3.08 | 16 | 1.1925 | 0.6087 |
| 1.0244 | 4.08 | 20 | 1.1087 | 0.6087 |
| 1.0244 | 5.08 | 24 | 0.9824 | 0.5652 |
| 1.0244 | 6.08 | 28 | 1.0297 | 0.5217 |
| 0.9684 | 7.08 | 32 | 1.0348 | 0.6522 |
| 0.9684 | 8.08 | 36 | 0.9426 | 0.6522 |
| 0.7826 | 9.08 | 40 | 1.0071 | 0.6087 |
| 0.7826 | 10.08 | 44 | 0.9811 | 0.6087 |
| 0.7826 | 11.08 | 48 | 0.9040 | 0.6087 |
| 0.7829 | 12.04 | 50 | 0.8987 | 0.6087 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ostapeno/selector_1B_coarsegrained_poly_router_dir_none_similar10
|
ostapeno
| 2023-12-26T06:39:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:31:59Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
Last updated on: 2023-12-26 06:38:32+00:00
|
Realgon/N_roberta_agnews_padding70model
|
Realgon
| 2023-12-26T06:30:46Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-26T03:39:46Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_roberta_agnews_padding70model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9465789473684211
---
<!-- 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. -->
# N_roberta_agnews_padding70model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5754
- Accuracy: 0.9466
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.201 | 1.0 | 7500 | 0.2029 | 0.9421 |
| 0.168 | 2.0 | 15000 | 0.2082 | 0.945 |
| 0.1533 | 3.0 | 22500 | 0.2343 | 0.9432 |
| 0.1208 | 4.0 | 30000 | 0.2381 | 0.9466 |
| 0.1071 | 5.0 | 37500 | 0.2468 | 0.9464 |
| 0.0831 | 6.0 | 45000 | 0.2775 | 0.9438 |
| 0.0758 | 7.0 | 52500 | 0.3080 | 0.9462 |
| 0.056 | 8.0 | 60000 | 0.3970 | 0.9436 |
| 0.0531 | 9.0 | 67500 | 0.3881 | 0.9401 |
| 0.037 | 10.0 | 75000 | 0.3956 | 0.9443 |
| 0.0309 | 11.0 | 82500 | 0.4551 | 0.9416 |
| 0.0257 | 12.0 | 90000 | 0.4521 | 0.9428 |
| 0.0287 | 13.0 | 97500 | 0.4650 | 0.9413 |
| 0.0121 | 14.0 | 105000 | 0.4888 | 0.9464 |
| 0.0116 | 15.0 | 112500 | 0.5071 | 0.9457 |
| 0.0085 | 16.0 | 120000 | 0.5249 | 0.9449 |
| 0.0107 | 17.0 | 127500 | 0.5244 | 0.9463 |
| 0.0031 | 18.0 | 135000 | 0.5597 | 0.9459 |
| 0.0041 | 19.0 | 142500 | 0.5615 | 0.9476 |
| 0.0029 | 20.0 | 150000 | 0.5754 | 0.9466 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
krishnadasar-sudheer-kumar/Reinforce-Pixelcopter-PLE-v0
|
krishnadasar-sudheer-kumar
| 2023-12-26T06:15:25Z | 0 | 1 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T06:15:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.50 +/- 12.11
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Noob/sd-class-butterflies-64
|
Noob
| 2023-12-26T06:14:49Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-12-26T05:48:45Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 這是一個無條件圖像生成擴散模型,用來生成美麗的蝴蝶圖像
'''python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Noob/sd-class-butterflies-64')
image = pipeline().images[0]
image
|
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_GPT4_temp0_Seed105
|
behzadnet
| 2023-12-26T06:04:21Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-23T05:32:23Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# 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:
- 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 0.7.0.dev0
|
cramade/xlm-roberta-base-finetuned-panx-de
|
cramade
| 2023-12-26T05:45:03Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-26T02:13:42Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8643
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.256 | 1.0 | 525 | 0.1500 | 0.8356 |
| 0.1285 | 2.0 | 1050 | 0.1385 | 0.8484 |
| 0.0811 | 3.0 | 1575 | 0.1339 | 0.8643 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ostapeno/rsgd_full_1B_finegrained_poly_router_dir_lib_embeddings_similar10
|
ostapeno
| 2023-12-26T05:44:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:22:44Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ropes_background_new_situation_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiki_hop_original_generate_object_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| ropes_new_situation_background_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| ropes_prompt_beginning_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| ropes_read_background_situation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| ropes_plain_bottom_hint_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| ropes_background_situation_middle_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
Last updated on: 2023-12-26 05:44:06+00:00
|
ostapeno/rsgd_full_1B_coarsegrained_poly_router_dir_lora_sim_similar10
|
ostapeno
| 2023-12-26T05:36:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:22:30Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ropes_background_new_situation_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiki_hop_original_generate_object_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| ropes_new_situation_background_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| ropes_prompt_beginning_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| ropes_read_background_situation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| ropes_plain_bottom_hint_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| ropes_background_situation_middle_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
Last updated on: 2023-12-26 05:36:24+00:00
|
PranavHonrao/q-Taxi-v3
|
PranavHonrao
| 2023-12-26T05:31:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T05:31:51Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="PranavHonrao/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ostapeno/rsgd_full_1B_finegrained_poly_router_dir_lora_sim_similar10
|
ostapeno
| 2023-12-26T05:30:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T02:23:12Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| niv2_explanation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| super_glue_cb_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| duorc_SelfRC_generate_question_by_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ropes_background_new_situation_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| wiki_hop_original_generate_object_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| ropes_new_situation_background_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| ropes_prompt_beginning_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| ropes_read_background_situation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| ropes_plain_bottom_hint_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| ropes_background_situation_middle_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
Last updated on: 2023-12-26 05:29:34+00:00
|
homunculus/Reinforce-Pixelcopter-PLE
|
homunculus
| 2023-12-26T05:24:40Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T05:24:37Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.10 +/- 17.13
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ostapeno/ft_no_transf_1B_distinct10
|
ostapeno
| 2023-12-26T05:11:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-25T20:45:12Z |
Number of experts present in the library: 39
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| ropes_background_new_situation_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_new_situation_answer | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| ropes_background_situation_middle | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_background_situation_middle | lora |
| ropes_prompt_beginning | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_prompt_beginning | lora |
| wiki_hop_original_generate_subject | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_subject | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| niv2_dialogue_act_recognition | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_dialogue_act_recognition | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| wiki_hop_original_generate_object | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/wiki_hop_original_generate_object | lora |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| ropes_new_situation_background_answer | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_new_situation_background_answer | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| quarel_heres_a_story | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/quarel_heres_a_story | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| ropes_read_background_situation | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_read_background_situation | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| ropes_plain_bottom_hint | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ropes_plain_bottom_hint | lora |
| duorc_SelfRC_generate_question_by_answer_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/duorc_SelfRC_generate_question_by_answer | lora |
| super_glue_cb_1_0_2_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/super_glue_cb_1_0_2 | lora |
| ultrachat_25_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ultrachat_25 | lora |
| niv2_explanation_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/niv2_explanation | lora |
| aeslc_1_0_0_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| social_i_qa_Check_if_a_random_answer_is_valid_or_not_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora |
| high_school_psychology_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/high_school_psychology | lora |
Last updated on: 2023-12-26 05:09:51+00:00
|
shapiron/ppo-LunarLander-v2-alt-b128-ep24-rs2pt2e6
|
shapiron
| 2023-12-26T05:01:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T05:00:36Z |
---
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: 286.28 +/- 18.42
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
...
```
|
hkivancoral/hushem_40x_beit_large_adamax_0001_fold5
|
hkivancoral
| 2023-12-26T04:51:36Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"base_model:finetune:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-26T03:34:03Z |
---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_beit_large_adamax_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8536585365853658
---
<!-- 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. -->
# hushem_40x_beit_large_adamax_0001_fold5
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9014
- Accuracy: 0.8537
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0137 | 1.0 | 220 | 0.6252 | 0.8049 |
| 0.0039 | 2.0 | 440 | 0.3651 | 0.9268 |
| 0.0 | 3.0 | 660 | 0.2079 | 0.9512 |
| 0.0 | 4.0 | 880 | 0.2782 | 0.8780 |
| 0.0015 | 5.0 | 1100 | 0.3966 | 0.8780 |
| 0.0006 | 6.0 | 1320 | 0.9179 | 0.8049 |
| 0.0 | 7.0 | 1540 | 0.6543 | 0.8780 |
| 0.0 | 8.0 | 1760 | 0.6721 | 0.8537 |
| 0.0 | 9.0 | 1980 | 0.6667 | 0.8537 |
| 0.0 | 10.0 | 2200 | 0.6892 | 0.8293 |
| 0.0 | 11.0 | 2420 | 0.6788 | 0.8293 |
| 0.0187 | 12.0 | 2640 | 0.6872 | 0.8537 |
| 0.0 | 13.0 | 2860 | 1.1812 | 0.8049 |
| 0.0 | 14.0 | 3080 | 0.6787 | 0.8537 |
| 0.0 | 15.0 | 3300 | 0.7294 | 0.8293 |
| 0.0 | 16.0 | 3520 | 1.0136 | 0.8293 |
| 0.0 | 17.0 | 3740 | 0.9479 | 0.8293 |
| 0.0 | 18.0 | 3960 | 0.9308 | 0.8293 |
| 0.0 | 19.0 | 4180 | 0.8944 | 0.8293 |
| 0.0 | 20.0 | 4400 | 0.8979 | 0.8293 |
| 0.0 | 21.0 | 4620 | 0.8942 | 0.8293 |
| 0.0 | 22.0 | 4840 | 0.9123 | 0.8293 |
| 0.0 | 23.0 | 5060 | 0.7263 | 0.8537 |
| 0.0 | 24.0 | 5280 | 0.7426 | 0.8537 |
| 0.0 | 25.0 | 5500 | 0.7599 | 0.8293 |
| 0.0 | 26.0 | 5720 | 0.7693 | 0.8293 |
| 0.0 | 27.0 | 5940 | 0.8044 | 0.8293 |
| 0.0 | 28.0 | 6160 | 0.8028 | 0.8293 |
| 0.0 | 29.0 | 6380 | 0.6542 | 0.8293 |
| 0.0 | 30.0 | 6600 | 0.6934 | 0.8293 |
| 0.0 | 31.0 | 6820 | 0.6814 | 0.8293 |
| 0.0 | 32.0 | 7040 | 0.6666 | 0.8537 |
| 0.0 | 33.0 | 7260 | 0.7695 | 0.8293 |
| 0.0 | 34.0 | 7480 | 1.0033 | 0.8293 |
| 0.0 | 35.0 | 7700 | 0.9558 | 0.8537 |
| 0.0 | 36.0 | 7920 | 0.8444 | 0.8537 |
| 0.0 | 37.0 | 8140 | 0.9196 | 0.8537 |
| 0.0 | 38.0 | 8360 | 0.8784 | 0.8537 |
| 0.0 | 39.0 | 8580 | 0.8306 | 0.8780 |
| 0.0 | 40.0 | 8800 | 0.9373 | 0.8537 |
| 0.0 | 41.0 | 9020 | 0.9235 | 0.8537 |
| 0.0 | 42.0 | 9240 | 0.9473 | 0.8537 |
| 0.0 | 43.0 | 9460 | 0.9424 | 0.8537 |
| 0.0 | 44.0 | 9680 | 0.9102 | 0.8537 |
| 0.0 | 45.0 | 9900 | 0.9576 | 0.8537 |
| 0.0 | 46.0 | 10120 | 0.9639 | 0.8537 |
| 0.0 | 47.0 | 10340 | 0.9689 | 0.8537 |
| 0.0 | 48.0 | 10560 | 0.8859 | 0.8537 |
| 0.0 | 49.0 | 10780 | 0.9011 | 0.8537 |
| 0.0 | 50.0 | 11000 | 0.9014 | 0.8537 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
ntc-ai/SDXL-LoRA-slider.zebra-stripes
|
ntc-ai
| 2023-12-26T04:48:28Z | 5 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-26T04:48:25Z |
---
language:
- en
thumbnail: "images/evaluate/zebra stripes.../zebra stripes_17_3.0.png"
widget:
- text: zebra stripes
output:
url: images/zebra stripes_17_3.0.png
- text: zebra stripes
output:
url: images/zebra stripes_19_3.0.png
- text: zebra stripes
output:
url: images/zebra stripes_20_3.0.png
- text: zebra stripes
output:
url: images/zebra stripes_21_3.0.png
- text: zebra stripes
output:
url: images/zebra stripes_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "zebra stripes"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - zebra stripes (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/zebra stripes_17_-3.0.png" width=256 height=256 /> | <img src="images/zebra stripes_17_0.0.png" width=256 height=256 /> | <img src="images/zebra stripes_17_3.0.png" width=256 height=256 /> |
| <img src="images/zebra stripes_19_-3.0.png" width=256 height=256 /> | <img src="images/zebra stripes_19_0.0.png" width=256 height=256 /> | <img src="images/zebra stripes_19_3.0.png" width=256 height=256 /> |
| <img src="images/zebra stripes_20_-3.0.png" width=256 height=256 /> | <img src="images/zebra stripes_20_0.0.png" width=256 height=256 /> | <img src="images/zebra stripes_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
zebra stripes
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.zebra-stripes', weight_name='zebra stripes.safetensors', adapter_name="zebra stripes")
# Activate the LoRA
pipe.set_adapters(["zebra stripes"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, zebra stripes"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 630+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
abhi050688/summarization
|
abhi050688
| 2023-12-26T04:21:57Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-25T13:11:52Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-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]
### Framework versions
- PEFT 0.7.2.dev0
|
AIYIYA/my_new_inputs1
|
AIYIYA
| 2023-12-26T04:15:30Z | 1 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-26T04:04:27Z |
---
base_model: bert-base-chinese
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_new_inputs1
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. -->
# AIYIYA/my_new_inputs1
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6115
- Validation Loss: 1.7513
- Train Accuracy: 0.7217
- Epoch: 4
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 80, '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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8547 | 2.5914 | 0.4261 | 0 |
| 2.3539 | 2.2365 | 0.6 | 1 |
| 2.0114 | 1.9683 | 0.7043 | 2 |
| 1.7522 | 1.8043 | 0.7217 | 3 |
| 1.6115 | 1.7513 | 0.7217 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
AIYIYA/my_new_inputs
|
AIYIYA
| 2023-12-26T04:02:41Z | 7 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-25T18:20:03Z |
---
base_model: bert-base-chinese
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_new_inputs
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. -->
# AIYIYA/my_new_inputs
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4582
- Validation Loss: 2.5642
- Train Accuracy: 0.2812
- Epoch: 4
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 45, '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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.5554 | 2.6041 | 0.2188 | 0 |
| 2.4711 | 2.5642 | 0.2812 | 1 |
| 2.4489 | 2.5642 | 0.2812 | 2 |
| 2.4357 | 2.5642 | 0.2812 | 3 |
| 2.4582 | 2.5642 | 0.2812 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
stablediffusionapi/guofeng4-xl
|
stablediffusionapi
| 2023-12-26T03:56:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-12-26T03:53:32Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# GuoFeng4 XL API Inference

## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "guofeng4-xl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/guofeng4-xl)
Model link: [View model](https://modelslab.com/models/guofeng4-xl)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "guofeng4-xl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "20",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
hieunguyenminh/v2
|
hieunguyenminh
| 2023-12-26T03:48:36Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/openchat-3.5-1210-GPTQ",
"base_model:adapter:TheBloke/openchat-3.5-1210-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-12-26T03:03:59Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/openchat-3.5-1210-GPTQ
model-index:
- name: v2
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. -->
# v2
This model is a fine-tuned version of [TheBloke/openchat-3.5-1210-GPTQ](https://huggingface.co/TheBloke/openchat-3.5-1210-GPTQ) 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
|
IParraMartin/XLM-EusBERTa-sentiment-classification
|
IParraMartin
| 2023-12-26T03:48:18Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:basque_glue",
"base_model:ClassCat/roberta-small-basque",
"base_model:finetune:ClassCat/roberta-small-basque",
"license:cc-by-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-21T20:17:55Z |
---
license: cc-by-sa-4.0
base_model: ClassCat/roberta-small-basque
tags:
- generated_from_trainer
datasets:
- basque_glue
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: XLM-EusBERTa-sentiment-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: basque_glue
type: basque_glue
config: bec
split: validation
args: bec
metrics:
- name: Accuracy
type: accuracy
value: 0.6290322580645161
- name: F1
type: f1
value: 0.6290834931512662
- name: Precision
type: precision
value: 0.630304630215078
- name: Recall
type: recall
value: 0.6290322580645161
---
<!-- 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. -->
# XLM-EusBERTa-sentiment-classification
This model is a fine-tuned version of [ClassCat/roberta-small-basque](https://huggingface.co/ClassCat/roberta-small-basque) on the basque_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0012
- Accuracy: 0.6290
- F1: 0.6291
- Precision: 0.6303
- Recall: 0.6290
## 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: 0.0001
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 380 | 0.7366 | 0.6736 | 0.6589 | 0.6711 | 0.6736 |
| 0.7679 | 2.0 | 760 | 0.7654 | 0.6767 | 0.6692 | 0.6726 | 0.6767 |
| 0.4846 | 3.0 | 1140 | 0.9844 | 0.6621 | 0.6599 | 0.6681 | 0.6621 |
| 0.2952 | 4.0 | 1520 | 1.1162 | 0.6375 | 0.6371 | 0.6473 | 0.6375 |
| 0.2952 | 5.0 | 1900 | 1.4234 | 0.6329 | 0.6343 | 0.6425 | 0.6329 |
| 0.192 | 6.0 | 2280 | 1.8570 | 0.6413 | 0.6362 | 0.6424 | 0.6413 |
| 0.159 | 7.0 | 2660 | 2.1968 | 0.6152 | 0.6086 | 0.6152 | 0.6152 |
| 0.1265 | 8.0 | 3040 | 2.1853 | 0.6283 | 0.6267 | 0.6267 | 0.6283 |
| 0.1265 | 9.0 | 3420 | 2.1953 | 0.6467 | 0.6441 | 0.6435 | 0.6467 |
| 0.0807 | 10.0 | 3800 | 2.2806 | 0.6367 | 0.6381 | 0.6480 | 0.6367 |
| 0.0688 | 11.0 | 4180 | 2.7982 | 0.6175 | 0.6167 | 0.6356 | 0.6175 |
| 0.0675 | 12.0 | 4560 | 2.5182 | 0.6605 | 0.6587 | 0.6584 | 0.6605 |
| 0.0675 | 13.0 | 4940 | 2.6544 | 0.6413 | 0.6315 | 0.6391 | 0.6413 |
| 0.0451 | 14.0 | 5320 | 2.5889 | 0.6459 | 0.6427 | 0.6424 | 0.6459 |
| 0.0432 | 15.0 | 5700 | 2.8100 | 0.6290 | 0.6299 | 0.6359 | 0.6290 |
| 0.0297 | 16.0 | 6080 | 2.9983 | 0.6275 | 0.6262 | 0.6263 | 0.6275 |
| 0.0297 | 17.0 | 6460 | 2.7803 | 0.6313 | 0.6289 | 0.6311 | 0.6313 |
| 0.0369 | 18.0 | 6840 | 2.9602 | 0.6283 | 0.6287 | 0.6353 | 0.6283 |
| 0.0289 | 19.0 | 7220 | 2.9911 | 0.6298 | 0.6309 | 0.6356 | 0.6298 |
| 0.0251 | 20.0 | 7600 | 2.8634 | 0.6344 | 0.6350 | 0.6364 | 0.6344 |
| 0.0251 | 21.0 | 7980 | 2.7171 | 0.6406 | 0.6378 | 0.6375 | 0.6406 |
| 0.0332 | 22.0 | 8360 | 3.0386 | 0.6275 | 0.6215 | 0.6245 | 0.6275 |
| 0.0212 | 23.0 | 8740 | 2.9876 | 0.6313 | 0.6319 | 0.6344 | 0.6313 |
| 0.0218 | 24.0 | 9120 | 2.9776 | 0.6283 | 0.6267 | 0.6348 | 0.6283 |
| 0.0189 | 25.0 | 9500 | 2.9596 | 0.6329 | 0.6340 | 0.6381 | 0.6329 |
| 0.0189 | 26.0 | 9880 | 3.0420 | 0.6329 | 0.6324 | 0.6380 | 0.6329 |
| 0.0172 | 27.0 | 10260 | 3.3335 | 0.6336 | 0.6348 | 0.6369 | 0.6336 |
| 0.0054 | 28.0 | 10640 | 3.2843 | 0.6429 | 0.6442 | 0.6466 | 0.6429 |
| 0.0065 | 29.0 | 11020 | 3.4868 | 0.6275 | 0.6291 | 0.6399 | 0.6275 |
| 0.0065 | 30.0 | 11400 | 3.8241 | 0.6175 | 0.6174 | 0.6209 | 0.6175 |
| 0.0108 | 31.0 | 11780 | 3.5833 | 0.6260 | 0.6275 | 0.6317 | 0.6260 |
| 0.0127 | 32.0 | 12160 | 3.5452 | 0.6183 | 0.6203 | 0.6283 | 0.6183 |
| 0.0092 | 33.0 | 12540 | 3.8349 | 0.6167 | 0.6167 | 0.6389 | 0.6167 |
| 0.0092 | 34.0 | 12920 | 3.6464 | 0.6244 | 0.6260 | 0.6313 | 0.6244 |
| 0.0069 | 35.0 | 13300 | 3.7538 | 0.6352 | 0.6352 | 0.6359 | 0.6352 |
| 0.0028 | 36.0 | 13680 | 3.8862 | 0.6221 | 0.6243 | 0.6350 | 0.6221 |
| 0.0001 | 37.0 | 14060 | 3.9846 | 0.6229 | 0.6206 | 0.6252 | 0.6229 |
| 0.0001 | 38.0 | 14440 | 3.7743 | 0.6275 | 0.6287 | 0.6309 | 0.6275 |
| 0.0057 | 39.0 | 14820 | 3.9002 | 0.6290 | 0.6300 | 0.6319 | 0.6290 |
| 0.0004 | 40.0 | 15200 | 3.9651 | 0.6306 | 0.6315 | 0.6333 | 0.6306 |
| 0.0032 | 41.0 | 15580 | 4.0279 | 0.6206 | 0.6213 | 0.6365 | 0.6206 |
| 0.0032 | 42.0 | 15960 | 3.8244 | 0.6344 | 0.6342 | 0.6344 | 0.6344 |
| 0.0033 | 43.0 | 16340 | 3.9036 | 0.6198 | 0.6205 | 0.6237 | 0.6198 |
| 0.003 | 44.0 | 16720 | 4.0028 | 0.6198 | 0.6214 | 0.6263 | 0.6198 |
| 0.0005 | 45.0 | 17100 | 3.9621 | 0.6306 | 0.6315 | 0.6361 | 0.6306 |
| 0.0005 | 46.0 | 17480 | 3.9682 | 0.6306 | 0.6297 | 0.6298 | 0.6306 |
| 0.0003 | 47.0 | 17860 | 4.0103 | 0.6321 | 0.6310 | 0.6305 | 0.6321 |
| 0.0003 | 48.0 | 18240 | 3.9968 | 0.6321 | 0.6316 | 0.6317 | 0.6321 |
| 0.003 | 49.0 | 18620 | 3.9835 | 0.6298 | 0.6297 | 0.6304 | 0.6298 |
| 0.0005 | 50.0 | 19000 | 4.0012 | 0.6290 | 0.6291 | 0.6303 | 0.6290 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
reza-alipour/Controlnet-HQ
|
reza-alipour
| 2023-12-26T03:47:05Z | 3 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-25T21:15:22Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-reza-alipour/Controlnet-HQ
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
Caption: Her high cheekbones accentuate her facial structure. She wears heavy makeup and lipstick.

Caption: This man has a big nose and wears a hat. He has a beard, goatee, and sideburns.

Caption: This young woman has straight hair and big lips. She wears eyeglasses and has a big nose.

Caption: This young woman has wavy hair and is wearing lipstick. Her mouth is slightly open.

|
Realgon/N_roberta_agnews_padding60model
|
Realgon
| 2023-12-26T03:39:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-26T00:56:02Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_roberta_agnews_padding60model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9460526315789474
---
<!-- 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. -->
# N_roberta_agnews_padding60model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5823
- Accuracy: 0.9461
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.2028 | 1.0 | 7500 | 0.2106 | 0.9407 |
| 0.1643 | 2.0 | 15000 | 0.1864 | 0.9475 |
| 0.1536 | 3.0 | 22500 | 0.2135 | 0.9455 |
| 0.1243 | 4.0 | 30000 | 0.2261 | 0.9468 |
| 0.1045 | 5.0 | 37500 | 0.2428 | 0.9468 |
| 0.0861 | 6.0 | 45000 | 0.2795 | 0.9434 |
| 0.0767 | 7.0 | 52500 | 0.3035 | 0.9470 |
| 0.0532 | 8.0 | 60000 | 0.3571 | 0.9461 |
| 0.0532 | 9.0 | 67500 | 0.3586 | 0.9426 |
| 0.0342 | 10.0 | 75000 | 0.4128 | 0.9434 |
| 0.026 | 11.0 | 82500 | 0.4228 | 0.9470 |
| 0.0226 | 12.0 | 90000 | 0.4714 | 0.9434 |
| 0.0209 | 13.0 | 97500 | 0.4663 | 0.9458 |
| 0.0127 | 14.0 | 105000 | 0.4939 | 0.9436 |
| 0.0082 | 15.0 | 112500 | 0.4959 | 0.9483 |
| 0.0142 | 16.0 | 120000 | 0.5230 | 0.9461 |
| 0.0024 | 17.0 | 127500 | 0.5710 | 0.9445 |
| 0.0082 | 18.0 | 135000 | 0.5560 | 0.9459 |
| 0.0034 | 19.0 | 142500 | 0.5778 | 0.9462 |
| 0.0018 | 20.0 | 150000 | 0.5823 | 0.9461 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hkivancoral/hushem_40x_beit_large_adamax_0001_fold4
|
hkivancoral
| 2023-12-26T03:33:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"base_model:finetune:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-26T02:14:38Z |
---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_beit_large_adamax_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9761904761904762
---
<!-- 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. -->
# hushem_40x_beit_large_adamax_0001_fold4
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1076
- Accuracy: 0.9762
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0165 | 1.0 | 219 | 0.5362 | 0.8810 |
| 0.0002 | 2.0 | 438 | 0.2899 | 0.9048 |
| 0.002 | 3.0 | 657 | 0.2264 | 0.9286 |
| 0.0 | 4.0 | 876 | 0.0134 | 1.0 |
| 0.0 | 5.0 | 1095 | 0.0221 | 0.9762 |
| 0.0 | 6.0 | 1314 | 0.0312 | 0.9762 |
| 0.0 | 7.0 | 1533 | 0.0455 | 0.9762 |
| 0.0 | 8.0 | 1752 | 0.1418 | 0.9524 |
| 0.0 | 9.0 | 1971 | 0.1481 | 0.9762 |
| 0.0 | 10.0 | 2190 | 0.0104 | 1.0 |
| 0.0 | 11.0 | 2409 | 0.0643 | 0.9762 |
| 0.0 | 12.0 | 2628 | 0.0455 | 0.9762 |
| 0.0 | 13.0 | 2847 | 0.0444 | 0.9762 |
| 0.0 | 14.0 | 3066 | 0.0410 | 0.9762 |
| 0.0 | 15.0 | 3285 | 0.0550 | 0.9762 |
| 0.0 | 16.0 | 3504 | 0.0281 | 0.9762 |
| 0.0 | 17.0 | 3723 | 0.0303 | 0.9762 |
| 0.0 | 18.0 | 3942 | 0.0305 | 0.9762 |
| 0.0 | 19.0 | 4161 | 0.0952 | 0.9762 |
| 0.0 | 20.0 | 4380 | 0.0860 | 0.9762 |
| 0.0 | 21.0 | 4599 | 0.0315 | 0.9762 |
| 0.0 | 22.0 | 4818 | 0.0334 | 0.9762 |
| 0.0 | 23.0 | 5037 | 0.0409 | 0.9762 |
| 0.0004 | 24.0 | 5256 | 0.3332 | 0.9524 |
| 0.0 | 25.0 | 5475 | 0.1274 | 0.9762 |
| 0.0071 | 26.0 | 5694 | 0.1341 | 0.9762 |
| 0.0 | 27.0 | 5913 | 0.1590 | 0.9762 |
| 0.0 | 28.0 | 6132 | 0.1155 | 0.9762 |
| 0.0 | 29.0 | 6351 | 0.1162 | 0.9762 |
| 0.0 | 30.0 | 6570 | 0.1374 | 0.9762 |
| 0.0 | 31.0 | 6789 | 0.1350 | 0.9762 |
| 0.0 | 32.0 | 7008 | 0.1260 | 0.9762 |
| 0.0 | 33.0 | 7227 | 0.1236 | 0.9762 |
| 0.0 | 34.0 | 7446 | 0.1361 | 0.9762 |
| 0.0 | 35.0 | 7665 | 0.1318 | 0.9762 |
| 0.0 | 36.0 | 7884 | 0.1308 | 0.9762 |
| 0.0 | 37.0 | 8103 | 0.1168 | 0.9762 |
| 0.0 | 38.0 | 8322 | 0.1190 | 0.9762 |
| 0.0 | 39.0 | 8541 | 0.0898 | 0.9762 |
| 0.0 | 40.0 | 8760 | 0.0926 | 0.9762 |
| 0.0 | 41.0 | 8979 | 0.0919 | 0.9762 |
| 0.0 | 42.0 | 9198 | 0.0987 | 0.9762 |
| 0.0 | 43.0 | 9417 | 0.0991 | 0.9762 |
| 0.0 | 44.0 | 9636 | 0.1047 | 0.9762 |
| 0.0 | 45.0 | 9855 | 0.1049 | 0.9762 |
| 0.0 | 46.0 | 10074 | 0.1056 | 0.9762 |
| 0.0 | 47.0 | 10293 | 0.1068 | 0.9762 |
| 0.0 | 48.0 | 10512 | 0.1039 | 0.9762 |
| 0.0 | 49.0 | 10731 | 0.1062 | 0.9762 |
| 0.0 | 50.0 | 10950 | 0.1076 | 0.9762 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
liuda1/dm7b_sft_gpt88w_merge
|
liuda1
| 2023-12-26T03:31:44Z | 1,475 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T08:03:28Z |
---
license: apache-2.0
---
---
license: apache-2.0
---with English chat dataset added for fine-tuning training, and further reinforcement training based on specific datasets. The trained model has a certain level of chat ability, which was found to be enhanced during self testing. We will continue to train the model in the future to improve our Chinese chat ability
|
meyceoz/prompt-llama-2
|
meyceoz
| 2023-12-26T03:01:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-26T03:01:17Z |
---
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
### Framework versions
- PEFT 0.6.0
|
krishnadasar-sudheer-kumar/Reinforce-v1
|
krishnadasar-sudheer-kumar
| 2023-12-26T02:53:22Z | 0 | 1 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-25T23:43:44Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 492.50 +/- 22.50
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
multimodalart/handpaintedbrazil
|
multimodalart
| 2023-12-26T02:21:06Z | 520 | 4 |
diffusers
|
[
"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",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-26T02:20:50Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: in the style of <s0><s1>
output:
url: image-0.png
- text: in the style of <s0><s1>
output:
url: image-1.png
- text: in the style of <s0><s1>
output:
url: image-2.png
- text: in the style of <s0><s1>
output:
url: image-3.png
- text: in the style of <s0><s1>
output:
url: image-4.png
- text: in the style of <s0><s1>
output:
url: image-5.png
- text: in the style of <s0><s1>
output:
url: image-6.png
- text: in the style of <s0><s1>
output:
url: image-7.png
- text: in the style of <s0><s1>
output:
url: image-8.png
- text: in the style of <s0><s1>
output:
url: image-9.png
- text: in the style of <s0><s1>
output:
url: image-10.png
- text: in the style of <s0><s1>
output:
url: image-11.png
- text: in the style of <s0><s1>
output:
url: image-12.png
- text: in the style of <s0><s1>
output:
url: image-13.png
- text: in the style of <s0><s1>
output:
url: image-14.png
- text: in the style of <s0><s1>
output:
url: image-15.png
- text: in the style of <s0><s1>
output:
url: image-16.png
- text: in the style of <s0><s1>
output:
url: image-17.png
- text: in the style of <s0><s1>
output:
url: image-18.png
- text: in the style of <s0><s1>
output:
url: image-19.png
- text: in the style of <s0><s1>
output:
url: image-20.png
- text: in the style of <s0><s1>
output:
url: image-21.png
- text: in the style of <s0><s1>
output:
url: image-22.png
- text: in the style of <s0><s1>
output:
url: image-23.png
- text: in the style of <s0><s1>
output:
url: image-24.png
- text: in the style of <s0><s1>
output:
url: image-25.png
- text: in the style of <s0><s1>
output:
url: image-26.png
- text: in the style of <s0><s1>
output:
url: image-27.png
- text: in the style of <s0><s1>
output:
url: image-28.png
- text: in the style of <s0><s1>
output:
url: image-29.png
- text: in the style of <s0><s1>
output:
url: image-30.png
- text: in the style of <s0><s1>
output:
url: image-31.png
- text: in the style of <s0><s1>
output:
url: image-32.png
- text: in the style of <s0><s1>
output:
url: image-33.png
- text: in the style of <s0><s1>
output:
url: image-34.png
- text: in the style of <s0><s1>
output:
url: image-35.png
- text: in the style of <s0><s1>
output:
url: image-36.png
- text: in the style of <s0><s1>
output:
url: image-37.png
- text: in the style of <s0><s1>
output:
url: image-38.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/handpaintedbrazil
<Gallery />
## Model description
### These are multimodalart/handpaintedbrazil LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/handpaintedbrazil', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/handpaintedbrazil', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/handpaintedbrazil/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/handpaintedbrazil/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/handpaintedbrazil/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_GPT4_temp0_Seed104
|
behzadnet
| 2023-12-26T02:20:44Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-23T01:53:14Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# 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:
- 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 0.7.0.dev0
|
turboderp/Sheared-Llama2-1.3B-exl2
|
turboderp
| 2023-12-26T02:08:01Z | 6 | 0 | null |
[
"region:us"
] | null | 2023-11-23T00:33:59Z |
EXL2 quants of [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) from princeton-nlp.
This is a pruned and further pre-trained version of Llama2-7B
[2.50 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/2.5bpw)
[2.70 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/2.7bpw)
[3.00 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/3.0bpw)
[3.50 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/3.5bpw)
[4.00 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/4.0bpw)
[4.50 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/4.5bpw)
[5.00 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/5.0bpw)
[6.00 bits per weight](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/tree/6.0bpw)
[measurement.json](https://huggingface.co/turboderp/Sheared-Llama2-1.3B-exl2/blob/main/measurement.json)
|
HunyStark/ppo-LunarLander-v2
|
HunyStark
| 2023-12-26T02:06:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T02:05:52Z |
---
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: 259.33 +/- 20.62
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
...
```
|
arhamh/ppo2-LunarLander-v2
|
arhamh
| 2023-12-26T01:48:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T01:48:22Z |
---
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: 285.66 +/- 14.11
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
...
```
|
ntc-ai/SDXL-LoRA-slider.glowing-eyes
|
ntc-ai
| 2023-12-26T01:48:15Z | 58 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-26T01:48:10Z |
---
language:
- en
thumbnail: "images/evaluate/glowing eyes.../glowing eyes_17_3.0.png"
widget:
- text: glowing eyes
output:
url: images/glowing eyes_17_3.0.png
- text: glowing eyes
output:
url: images/glowing eyes_19_3.0.png
- text: glowing eyes
output:
url: images/glowing eyes_20_3.0.png
- text: glowing eyes
output:
url: images/glowing eyes_21_3.0.png
- text: glowing eyes
output:
url: images/glowing eyes_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "glowing eyes"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - glowing eyes (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/glowing eyes_17_-3.0.png" width=256 height=256 /> | <img src="images/glowing eyes_17_0.0.png" width=256 height=256 /> | <img src="images/glowing eyes_17_3.0.png" width=256 height=256 /> |
| <img src="images/glowing eyes_19_-3.0.png" width=256 height=256 /> | <img src="images/glowing eyes_19_0.0.png" width=256 height=256 /> | <img src="images/glowing eyes_19_3.0.png" width=256 height=256 /> |
| <img src="images/glowing eyes_20_-3.0.png" width=256 height=256 /> | <img src="images/glowing eyes_20_0.0.png" width=256 height=256 /> | <img src="images/glowing eyes_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
glowing eyes
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.glowing-eyes', weight_name='glowing eyes.safetensors', adapter_name="glowing eyes")
# Activate the LoRA
pipe.set_adapters(["glowing eyes"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, glowing eyes"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 630+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
tangfei/autotrain-sinm4-3x59p
|
tangfei
| 2023-12-26T01:47:00Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:tangfei/autotrain-data-autotrain-sinm4-3x59p",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-26T01:46:37Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- tangfei/autotrain-data-autotrain-sinm4-3x59p
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 2.1737219307624096e+36
f1_macro: 0.13333333333333333
f1_micro: 0.25
f1_weighted: 0.1
precision_macro: 0.08333333333333333
precision_micro: 0.25
precision_weighted: 0.0625
recall_macro: 0.3333333333333333
recall_micro: 0.25
recall_weighted: 0.25
accuracy: 0.25
|
SicariusSicariiStuff/TinyLLaMA_0.6chat_EXL2_3.00bpw
|
SicariusSicariiStuff
| 2023-12-26T01:18:41Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-26T01:16:57Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- OpenAssistant/oasst_top1_2023-08-25
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/edit/main/README.md)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
#### How to use
You will need the transformers>=4.34
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v0.6", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# ...
```
|
Parksongs/llama2-qlora-finetunined-french
|
Parksongs
| 2023-12-26T01:17:31Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-12-26T01:17:22Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# 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]
### Framework versions
- PEFT 0.7.2.dev0
|
Kornberg/controlnet_landsat_scheduler
|
Kornberg
| 2023-12-26T00:58:07Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-12T16:21:30Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-Kornberg/controlnet_landsat_scheduler
Source Repository: https://github.com/JKornberg/controlnet_landsat
Source dataset: https://huggingface.co/datasets/Kornberg/landsat_captions
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
You can find some example images below.
prompt: A satellite image of the earth. The weather is clear

prompt: A satellite image of the earth. The weather is cloudy and cold

prompt: A satellite image of the earth. The weather is slightly cloudy and very snowy

prompt: A satellite image of the earth. The weather is clear

prompt: A satellite image of the earth. The weather is slightly cloudy and cold

prompt: A satellite image of the earth. The weather is very cloudy and very snowy

|
Realgon/N_roberta_agnews_padding50model
|
Realgon
| 2023-12-26T00:55:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-25T22:20:28Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_roberta_agnews_padding50model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9485526315789473
---
<!-- 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. -->
# N_roberta_agnews_padding50model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5524
- Accuracy: 0.9486
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1998 | 1.0 | 7500 | 0.2132 | 0.9382 |
| 0.1682 | 2.0 | 15000 | 0.2009 | 0.9475 |
| 0.1506 | 3.0 | 22500 | 0.2273 | 0.9446 |
| 0.1294 | 4.0 | 30000 | 0.2495 | 0.9482 |
| 0.1028 | 5.0 | 37500 | 0.2612 | 0.9459 |
| 0.0797 | 6.0 | 45000 | 0.2966 | 0.9457 |
| 0.0646 | 7.0 | 52500 | 0.3040 | 0.9458 |
| 0.0531 | 8.0 | 60000 | 0.3825 | 0.9446 |
| 0.0443 | 9.0 | 67500 | 0.3838 | 0.9425 |
| 0.0345 | 10.0 | 75000 | 0.3968 | 0.9475 |
| 0.0395 | 11.0 | 82500 | 0.4132 | 0.9474 |
| 0.019 | 12.0 | 90000 | 0.4612 | 0.9453 |
| 0.0219 | 13.0 | 97500 | 0.4559 | 0.9458 |
| 0.0067 | 14.0 | 105000 | 0.4692 | 0.9467 |
| 0.0065 | 15.0 | 112500 | 0.5118 | 0.9461 |
| 0.0045 | 16.0 | 120000 | 0.5115 | 0.9470 |
| 0.004 | 17.0 | 127500 | 0.5326 | 0.9472 |
| 0.0079 | 18.0 | 135000 | 0.5088 | 0.9483 |
| 0.0039 | 19.0 | 142500 | 0.5359 | 0.9504 |
| 0.0024 | 20.0 | 150000 | 0.5524 | 0.9486 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
andrew-ye/ppo-LunarLander-v2
|
andrew-ye
| 2023-12-26T00:51:44Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-26T00:51:21Z |
---
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: 254.58 +/- 24.16
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
...
```
|
afrideva/Astrea-RP-v1-3B-GGUF
|
afrideva
| 2023-12-26T00:43:33Z | 38 | 2 |
transformers
|
[
"transformers",
"gguf",
"gpt",
"llm",
"large language model",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"base_model:Aryanne/Astrea-RP-v1-3B",
"base_model:quantized:Aryanne/Astrea-RP-v1-3B",
"license:other",
"region:us",
"conversational"
] |
text-generation
| 2023-12-26T00:30:10Z |
---
base_model: Aryanne/Astrea-RP-v1-3B
inference: false
language:
- en
library_name: transformers
license: other
model_creator: Aryanne
model_name: Astrea-RP-v1-3B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gpt
- llm
- large language model
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# Aryanne/Astrea-RP-v1-3B-GGUF
Quantized GGUF model files for [Astrea-RP-v1-3B](https://huggingface.co/Aryanne/Astrea-RP-v1-3B) from [Aryanne](https://huggingface.co/Aryanne)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [astrea-rp-v1-3b.fp16.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.fp16.gguf) | fp16 | 5.59 GB |
| [astrea-rp-v1-3b.q2_k.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q2_k.gguf) | q2_k | 1.20 GB |
| [astrea-rp-v1-3b.q3_k_m.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q3_k_m.gguf) | q3_k_m | 1.39 GB |
| [astrea-rp-v1-3b.q4_k_m.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q4_k_m.gguf) | q4_k_m | 1.71 GB |
| [astrea-rp-v1-3b.q5_k_m.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q5_k_m.gguf) | q5_k_m | 1.99 GB |
| [astrea-rp-v1-3b.q6_k.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q6_k.gguf) | q6_k | 2.30 GB |
| [astrea-rp-v1-3b.q8_0.gguf](https://huggingface.co/afrideva/Astrea-RP-v1-3B-GGUF/resolve/main/astrea-rp-v1-3b.q8_0.gguf) | q8_0 | 2.97 GB |
## Original Model Card:
This model is a merge of [euclaise/Echo-3B](https://huggingface.co/euclaise/Echo-3B), [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) and [Aryanne/Astridboros-3B](https://huggingface.co/Aryanne/Astridboros-3B) using task_arithmetic(see astrea-rp-v1-3b.yml or below).
```yaml
merge_method: task_arithmetic
base_model: euclaise/Ferret-3B
models:
- model: euclaise/Ferret-3B
- model: stabilityai/stablelm-zephyr-3b
parameters:
weight: 0.33
- model: euclaise/Echo-3B
parameters:
weight: 0.66
- model: Aryanne/Astridboros-3B
parameters:
weight: 0.16
dtype: float16
```
I recommend the use of Vicuna prompt format, but it's your choice to see what works for you.
I think zephyr license applies to this merge, for non commercial use.
|
douglasadams11/distilbert-base-uncased-ner
|
douglasadams11
| 2023-12-26T00:07:34Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-25T23:05:25Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner
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-ner
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: 0.1419
- Precision: 0.9526
- Recall: 0.9431
- F1: 0.9479
- Accuracy: 0.9434
## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2866 | 0.14 | 500 | 0.1970 | 0.9329 | 0.9213 | 0.9271 | 0.9212 |
| 0.198 | 0.28 | 1000 | 0.1851 | 0.9412 | 0.9218 | 0.9314 | 0.9253 |
| 0.1892 | 0.43 | 1500 | 0.1772 | 0.9431 | 0.9250 | 0.9340 | 0.9280 |
| 0.179 | 0.57 | 2000 | 0.1697 | 0.9440 | 0.9296 | 0.9367 | 0.9313 |
| 0.1719 | 0.71 | 2500 | 0.1618 | 0.9453 | 0.9330 | 0.9391 | 0.9339 |
| 0.1718 | 0.85 | 3000 | 0.1587 | 0.9443 | 0.9351 | 0.9397 | 0.9351 |
| 0.1664 | 0.99 | 3500 | 0.1569 | 0.9486 | 0.9340 | 0.9412 | 0.9361 |
| 0.1504 | 1.14 | 4000 | 0.1566 | 0.9480 | 0.9356 | 0.9417 | 0.9368 |
| 0.1479 | 1.28 | 4500 | 0.1539 | 0.9492 | 0.9369 | 0.9430 | 0.9381 |
| 0.1467 | 1.42 | 5000 | 0.1501 | 0.9499 | 0.9383 | 0.9441 | 0.9391 |
| 0.1478 | 1.56 | 5500 | 0.1489 | 0.9513 | 0.9368 | 0.9440 | 0.9390 |
| 0.147 | 1.7 | 6000 | 0.1457 | 0.9503 | 0.9402 | 0.9452 | 0.9407 |
| 0.1453 | 1.85 | 6500 | 0.1447 | 0.9510 | 0.9408 | 0.9459 | 0.9412 |
| 0.1384 | 1.99 | 7000 | 0.1442 | 0.9521 | 0.9405 | 0.9463 | 0.9415 |
| 0.1325 | 2.13 | 7500 | 0.1446 | 0.9494 | 0.9441 | 0.9467 | 0.9425 |
| 0.13 | 2.27 | 8000 | 0.1467 | 0.9524 | 0.9403 | 0.9463 | 0.9416 |
| 0.1286 | 2.41 | 8500 | 0.1435 | 0.9501 | 0.9440 | 0.9470 | 0.9427 |
| 0.1311 | 2.56 | 9000 | 0.1446 | 0.9529 | 0.9417 | 0.9473 | 0.9427 |
| 0.1258 | 2.7 | 9500 | 0.1438 | 0.9528 | 0.9425 | 0.9476 | 0.9431 |
| 0.1257 | 2.84 | 10000 | 0.1437 | 0.9527 | 0.9431 | 0.9479 | 0.9434 |
| 0.1289 | 2.98 | 10500 | 0.1420 | 0.9526 | 0.9430 | 0.9478 | 0.9433 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
bozyurt/bio-electra-mid-1_2m
|
bozyurt
| 2023-12-25T23:48:46Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"en",
"license:cc",
"endpoints_compatible",
"region:us"
] | null | 2023-12-25T23:38:13Z |
---
license: cc
language:
- en
---
# Bio-ELECTRA Mid 1.2m (cased)
Pretrained (from scratch for 1.2 million steps) mid-sized (50 million parameters) ELECTRA discriminator model on 2021 Base PubMed abstracts
and PMC open access papers with a domain specific word piece vocabulary generated using SentencePiece
byte-pair-encoding (BPE) model from PubMed abstract texts. This model is case-sensitive: it makes a difference between english and English.
# Intended uses & limitations
This model is mostly intended to be fine-tuned on a downstream biomedical domain task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence to
make decisions, such as classification, information retrieval, relation extraction or question answering.
# Training data
The pretraining corpus was built using 21.2 million PubMed abstracts from the January 2021 baseline distribution. To build the corpus,
title and abstract text sentences were extracted resulting in a corpus of 3.6 billion words. The PMC open access corpus (January 2021) is
a 12.3 billion words corpus built using the sentences extracted from the sections of PMC open access papers
excluding the references sections.
# Training procedure
The training procedure follows the original ELECTRA training.
## Preprocessing
A domain specific vocabulary of size 31,620 is generated using SentencePiece byte-pair-encoding (BPE) model from PubMed abstract texts.
The title and abstract text sentences were extracted using an in-house sentence segmenter trained on biomedical text. The sentences are
pre-tokenized using an in-house biomedical tokenizer for proper tokenization of biomedical entities such as gene/protein names,
organisms, antibodies, cell lines. The SentencePiece BPE vocabulary of word pieces are applied during pre-training
to the properly tokenized and segmented sentences. For the PMC open access corpus, JATS XML files for the full text papers are parsed
to extract sections excluding the reference section and section title and section body is processed in the same fashion
as the PubMed abstracts corpus.
## Pretraining
The model is pretrained on a single 8 core version 3 tensor processing unit (TPU) with 128 GB of RAM for 1,200,000 steps
with a batch size of 256. The first 1,000,000 steps are pre-trained on PubMed abstracts.
After that, the model is pre-trained for another 200,000 steps on PMC open access papers.
The training parameters were the same as the original ELECTRA base model. The model has 50M parameters,
12 transformers layers with hidden layer size of 512 and 8 attention heads.
# BibTeX entry and citation info
```
@inproceedings{ozyurt-etal-2021-detecting,
title = "Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models",
author = "Ozyurt, Ibrahim Burak and
Menke, Joseph and
Bandrowski, Anita and
Martone, Maryann",
editor = "Beltagy, Iz and
Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hall, Keith and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Kuansan and
Wang, Lucy Lu",
booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sdp-1.4",
doi = "10.18653/v1/2021.sdp-1.4",
pages = "27--35",
abstract = "Understanding of nerve-organ interactions is crucial to facilitate the development of effective bioelectronic treatments. Towards the end of developing a systematized and computable wiring diagram of the autonomic nervous system (ANS), we introduce a curated ANS connectivity corpus together with several neural language representation model based connectivity relation extraction systems. We also show that active learning guided curation for labeled corpus expansion significantly outperforms randomly selecting connectivity relation candidates minimizing curation effort. Our final relation extraction system achieves $F_1$ = 72.8{\%} on anatomical connectivity and $F_1$ = 74.6{\%} on functional connectivity relation extraction.",
}
```
|
NouRed/fine-tuned-git-diffusion
|
NouRed
| 2023-12-25T23:41:56Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"git",
"image-text-to-text",
"image-to-text",
"en",
"dataset:poloclub/diffusiondb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-12-25T22:08:18Z |
---
license: apache-2.0
datasets:
- poloclub/diffusiondb
language:
- en
library_name: transformers
pipeline_tag: image-to-text
---
|
MattStammers/appo-mujoco_humanoid-sota
|
MattStammers
| 2023-12-25T23:33:44Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-25T23:33:23Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: mujoco_humanoid
type: mujoco_humanoid
metrics:
- type: mean_reward
value: 11486.26 +/- 29.38
name: mean_reward
verified: false
---
## About the Project
This project is an attempt to maximise performance of high sample throughput APPO RL models in Atari environments in as carbon efficient a manner as possible using a single, not particularly high performance single machine. It is about demonstrating the generalisability of on-policy algorithms to create good performance quickly (by sacrificing sample efficiency) while also proving that this route to RL production is accessible to even hobbyists like me (I am a gastroenterologist not a computer scientist).
In terms of throughput I am managing to reach throughputs of 2,500 - 3,000 across both policies using sample factory using two Quadro P2200's (not particularly powerful GPUs) each loaded up about 60% (3GB). Previously using the stable baselines 3 (sb3) implementation of PPO it would take about a week to train an atari agent to 100 million timesteps synchronously. By comparison the sample factory async implementation takes only just over 2 hours to achieve the same result. That is about 84 times faster with only typically a 21 watt burn per GPU. I am thus very grateful to Alex Petrenko and all the sample factory team for their work on this.
## Project Aims
This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it anywhere near sota performance.
I then re-trained the models with 100 million timesteps- at this point 2 environments maxed out at sota performance (Pong and Freeway) with four approaching sota performance - (atlantis, boxing, tennis and fishingderby.) =6/57 near sota.
The aim now is to try and reach state-of-the-art (SOTA) performance on a further block of atari environments using up to 1 billion training timesteps initially with appo. I will flag the models with SOTA when they reach at or near these levels.
After this I will switch on V-Trace to see if the Impala variations perform any better with the same seed (I have seeded '1234')
## About the Model
The hyperparameters used in the model are described in my shell script on my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his parameters, I saved time and energy by using many of his tuned hyperparameters to reduce carbon inefficiency:
```
hyperparameters = {
"help": false,
"algo": "APPO",
"env": "atari_asteroid",
"experiment": "atari_asteroid_APPO",
"train_dir": "./train_atari",
"restart_behavior": "restart",
"device": "gpu",
"seed": 1234,
"num_policies": 2,
"async_rl": true,
"serial_mode": false,
"batched_sampling": true,
"num_batches_to_accumulate": 2,
"worker_num_splits": 1,
"policy_workers_per_policy": 1,
"max_policy_lag": 1000,
"num_workers": 16,
"num_envs_per_worker": 2,
"batch_size": 1024,
"num_batches_per_epoch": 8,
"num_epochs": 4,
"rollout": 128,
"recurrence": 1,
"shuffle_minibatches": false,
"gamma": 0.99,
"reward_scale": 1.0,
"reward_clip": 1000.0,
"value_bootstrap": false,
"normalize_returns": true,
"exploration_loss_coeff": 0.0004677351413,
"value_loss_coeff": 0.5,
"kl_loss_coeff": 0.0,
"exploration_loss": "entropy",
"gae_lambda": 0.95,
"ppo_clip_ratio": 0.1,
"ppo_clip_value": 1.0,
"with_vtrace": true,
"vtrace_rho": 1.0,
"vtrace_c": 1.0,
"optimizer": "adam",
"adam_eps": 1e-05,
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"max_grad_norm": 0.0,
"learning_rate": 0.0003033891184,
"lr_schedule": "linear_decay",
"lr_schedule_kl_threshold": 0.008,
"lr_adaptive_min": 1e-06,
"lr_adaptive_max": 0.01,
"obs_subtract_mean": 0.0,
"obs_scale": 255.0,
"normalize_input": true,
"normalize_input_keys": [
"obs"
],
"decorrelate_experience_max_seconds": 0,
"decorrelate_envs_on_one_worker": true,
"actor_worker_gpus": [],
"set_workers_cpu_affinity": true,
"force_envs_single_thread": false,
"default_niceness": 0,
"log_to_file": true,
"experiment_summaries_interval": 3,
"flush_summaries_interval": 30,
"stats_avg": 100,
"summaries_use_frameskip": true,
"heartbeat_interval": 10,
"heartbeat_reporting_interval": 60,
"train_for_env_steps": 100000000,
"train_for_seconds": 10000000000,
"save_every_sec": 120,
"keep_checkpoints": 2,
"load_checkpoint_kind": "latest",
"save_milestones_sec": 1200,
"save_best_every_sec": 5,
"save_best_metric": "reward",
"save_best_after": 100000,
"benchmark": false,
"encoder_mlp_layers": [
512,
512
],
"encoder_conv_architecture": "convnet_atari",
"encoder_conv_mlp_layers": [
512
],
"use_rnn": false,
"rnn_size": 512,
"rnn_type": "gru",
"rnn_num_layers": 1,
"decoder_mlp_layers": [],
"nonlinearity": "relu",
"policy_initialization": "orthogonal",
"policy_init_gain": 1.0,
"actor_critic_share_weights": true,
"adaptive_stddev": false,
"continuous_tanh_scale": 0.0,
"initial_stddev": 1.0,
"use_env_info_cache": false,
"env_gpu_actions": false,
"env_gpu_observations": true,
"env_frameskip": 4,
"env_framestack": 4,
"pixel_format": "CHW"
}
```
A(n) **APPO** impala model trained on the **mujoco_humanoid** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a
high throughput on-policy RL framework. I have been using
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-mujoco_humanoid
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_humanoid --train_dir=./train_dir --experiment=APPO-mujoco_humanoid
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_humanoid --train_dir=./train_dir --experiment=APPO-mujoco_humanoid --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
aaneesai/openai-whisper-base-LORA-colab
|
aaneesai
| 2023-12-25T23:16:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-base",
"base_model:adapter:openai/whisper-base",
"region:us"
] | null | 2023-12-25T23:16:41Z |
---
library_name: peft
base_model: openai/whisper-base
---
# 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]
### Framework versions
- PEFT 0.7.2.dev0
|
Warrantstates/6as4ea3fdas3
|
Warrantstates
| 2023-12-25T23:15:27Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
text-to-image
| 2023-12-25T23:15:11Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/download (3).jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: cc-by-nc-nd-4.0
---
# 6as4ea3fdas3
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Warrantstates/6as4ea3fdas3/tree/main) them in the Files & versions tab.
|
hieunguyenminh/ttl-roleplay
|
hieunguyenminh
| 2023-12-25T23:15:09Z | 5 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-beta-GPTQ",
"base_model:adapter:TheBloke/zephyr-7B-beta-GPTQ",
"license:mit",
"region:us"
] | null | 2023-12-18T00:08:15Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/zephyr-7B-beta-GPTQ
model-index:
- name: ttl-roleplay
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. -->
# ttl-roleplay
This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ntc-ai/SDXL-LoRA-slider.burning-red-eyes
|
ntc-ai
| 2023-12-25T22:48:01Z | 24 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-25T22:47:49Z |
---
language:
- en
thumbnail: "images/evaluate/burning red eyes.../burning red eyes_17_3.0.png"
widget:
- text: burning red eyes
output:
url: images/burning red eyes_17_3.0.png
- text: burning red eyes
output:
url: images/burning red eyes_19_3.0.png
- text: burning red eyes
output:
url: images/burning red eyes_20_3.0.png
- text: burning red eyes
output:
url: images/burning red eyes_21_3.0.png
- text: burning red eyes
output:
url: images/burning red eyes_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "burning red eyes"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - burning red eyes (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/burning red eyes_17_-3.0.png" width=256 height=256 /> | <img src="images/burning red eyes_17_0.0.png" width=256 height=256 /> | <img src="images/burning red eyes_17_3.0.png" width=256 height=256 /> |
| <img src="images/burning red eyes_19_-3.0.png" width=256 height=256 /> | <img src="images/burning red eyes_19_0.0.png" width=256 height=256 /> | <img src="images/burning red eyes_19_3.0.png" width=256 height=256 /> |
| <img src="images/burning red eyes_20_-3.0.png" width=256 height=256 /> | <img src="images/burning red eyes_20_0.0.png" width=256 height=256 /> | <img src="images/burning red eyes_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
burning red eyes
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.burning-red-eyes', weight_name='burning red eyes.safetensors', adapter_name="burning red eyes")
# Activate the LoRA
pipe.set_adapters(["burning red eyes"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, burning red eyes"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 620+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF
|
NeverSleep
| 2023-12-25T22:08:32Z | 870 | 30 | null |
[
"gguf",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2023-12-25T18:31:09Z |
---
license: cc-by-nc-4.0
---

---
# Disclaimer:
## This model is experimental, do not expect everything to work.
This model uses the Alpaca **prompting format**(or just directly download the SillyTavern instruct preset [here](https://files.catbox.moe/0ohmco.json))
---
Beeg noromaid on ***steroids***. Suitable for RP, ERP.
This time based on Mixtral Instruct, seems to do wonders!
This model was trained for 8h(v1) + 8h(v2) + 12h(v3) on customized modified datasets, focusing on RP, uncensoring, and a modified version of the Alpaca prompting (that was already used in LimaRP), which should be at the same conversational level as ChatLM or Llama2-Chat without adding any additional special tokens.
If you wanna have more infos about this model(and v1 + v2) you can check out [my blog post](https://ikaridevgit.github.io/index.html?p=7&blog=blogid-6&bo=true)
[Recommended settings - Settings 1](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-v3/discussions/1)
[Recommended settings - Settings 2 (idk if they are any good)](https://files.catbox.moe/fv4xhu.json)
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains GGUF files of Noromaid-v0.1-mixtral-8x7b-Instruct-v3.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Datasets used:
- Aesir 1 and 2 ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe))
- [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia))
- [ToxicDPO-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) ([unalignment orga repo](https://huggingface.co/unalignment) + [Undi](https://huggingface.co/Undi95))
- [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgu))
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
smelborp/MixtralOrochi8x7B-Alt
|
smelborp
| 2023-12-25T22:00:16Z | 1,423 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"uncensored",
"high-intelligence",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T14:00:16Z |
---
license: cc-by-nc-4.0
language:
- en
tags:
- mixtral
- uncensored
- high-intelligence
---
# Orochi (Alternate Version)
<img src="https://huggingface.co/smelborp/MixtralOrochi8x7B/resolve/main/orochi.png" width="600" />
## Overview
Orochi is a cutting-edge language model based on the Mixtral architecture developed by Mistral. It represents a sophisticated merge of several prominent models, including Mixtral instruct, Noromaid, OpenBuddy, and several others, using mergekit with the DARE merge method. This model aims to provide highly intelligent responses unrestricted by content limitations. The name "Orochi" references the mythical Yamata-no-Orochi, symbolizing the model's multifaceted and powerful capabilities.
## Goals
- **Uncensored Content**: To provide unrestricted and comprehensive responses across various domains.
- **High Intelligence**: Leverage the combined knowledge and capabilities of the merged models to deliver insightful and accurate information.
- **Innovation in Language Modeling**: Push the boundaries of what's possible in natural language understanding and generation.
## Model Details
- **Architecture**: Mixtral, a Mixture of Experts model, underlies Orochi's design, enabling it to specialize and optimize its responses across different tasks and topics.
- **Merge Strategy**: Utilizing mergekit and the DARE method, Orochi integrates aspects of various models to enhance its performance and capabilities.
## Usage
Due to its uncensored nature, Orochi is best utilized in environments where intelligent, unrestricted dialogue is necessary. Users are encouraged to implement their own content moderation or alignment strategies appropriate for their use case.
## Ethical Considerations
As an uncensored model, Orochi may generate content that is unsuitable for all audiences. Users are advised to consider the implications of using such a model and to implement suitable safeguards and ethical guidelines.
## Acknowledgements
Orochi is a product of numerous contributions from the fields of machine learning and language modeling. Special thanks to the teams behind Mixtral, mergekit, and all the individual models integrated into Orochi.
---
|
ossu-teruyuki/sweety_okamix
|
ossu-teruyuki
| 2023-12-25T21:54:16Z | 0 | 0 | null |
[
"text-to-image",
"ja",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-06-18T09:08:43Z |
---
license: creativeml-openrail-m
language:
- ja
pipeline_tag: text-to-image
---
<h4>【はじめに】</h4>
本モデルをご使用の際に生じる問題、また、生成された画像に関する問題やその他の関連問題について、当方は一切責任を負いません。<br>
ご使用の際は、この点をご了承の上ご使用ください。<br>
<br>
<h4>【sweety_okamixとは】</h4>
かわいい金髪オオカミ娘を生成するためのモデルです。<br>
ただそれだけのモデルです。需要はあまり無いかもしれませんが記念に作りました。<br>
もちろんオオカミでない普通の人物を生成することも可能です。<br>
<br>
ファンタジー的な要素にも合うよう色味を少しだけ濃く鮮やかに出るよう調整しており、顔の血色もよくなりやすいです。<br>
オオカミ娘を作りたい人はよかったら使ってみてください。<br>
<br>
<h4>【金髪オオカミ娘の作り方】</h4>
普段お使いのプロンプトに以下のプロンプトを足してみてください。(強化の値は好みで調整してください。)<br>
(Young blonde girl) , nymph, dropping eyes, (Forest:1.5) , (bohemian clothes:1.5), (Strong light coming in:1.3),(mountain:1.5), lens flare , (wolf girl:1.4),(beautiful platinum blonde),dropping eyes, wavy hair, airy hair,(wolf tail:1.2),(blush cheeks:1.3),(flowers are blooming:1.5), (scattered fruits:1.33),(wolf ear:1.2),(extreme close-up) <br>
<br>
<h4>【制限・ライセンスについて】</h4>
本モデルは『CreativeML Open RAIL-M』のライセンスを採用しておりますが、マージに使用したモデルの制限を継承しているため、さらに以下の制限が適用されます。<br>
<span class="text-green-500">
可
</span>
:Use the model without crediting the creator (著作者表示なしでの使用)<br>
<span class="text-green-500">
可
</span>
:Sell images they generate (生成画像の販売)<br>
<span class="text-green-500">
可
</span>
:Run on services that generate images for money (商用画像生成サービスへの利用)<br>
<span class="text-green-500">
可
</span>
:Share merges using this model (マージモデルの配布)<br>
<span class="text-green-500">
可
</span>
:Sell this model or merges using this model (本モデルや派生モデルの販売)<br>
<span class="text-red-400">
不可
</span>
:Have different permissions when sharing merges (マージしたモデルに異なる制限を設定)<br>
なお、制限を継承したことにより、本モデルの販売や商業的な画像生成サービスへの利用を不可とすることができないため、それらの活動は制限上可能となっておりますが、当方は積極的な推奨は行っておりません。<br>
それらの活動によって生じたいかなる問題についても、当方は一切の責任を負いませんので、ご了承ください。<br>
本モデルや使用モデルに何らかの重要な問題が起きた場合は、本モデルを予告なく削除し、利用停止をお願いする可能性があります。<br>
本モデルを使用した際に起こる問題、また、生成された画像に関する問題やその他の関連問題について、当方は一切責任を負いません。<br>
ご使用の際は、この点をご了承の上ご使用ください。<br>
|
hkivancoral/hushem_40x_deit_small_rms_0001_fold5
|
hkivancoral
| 2023-12-25T21:50:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T21:34:09Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8048780487804879
---
<!-- 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. -->
# hushem_40x_deit_small_rms_0001_fold5
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9788
- Accuracy: 0.8049
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1371 | 1.0 | 220 | 0.3701 | 0.8293 |
| 0.0445 | 2.0 | 440 | 1.9924 | 0.7073 |
| 0.0133 | 3.0 | 660 | 1.1496 | 0.8049 |
| 0.0131 | 4.0 | 880 | 0.3434 | 0.9024 |
| 0.0354 | 5.0 | 1100 | 0.4117 | 0.8537 |
| 0.0497 | 6.0 | 1320 | 0.2267 | 0.9268 |
| 0.0845 | 7.0 | 1540 | 1.0625 | 0.8293 |
| 0.0001 | 8.0 | 1760 | 1.4387 | 0.7317 |
| 0.0648 | 9.0 | 1980 | 0.2862 | 0.9756 |
| 0.0159 | 10.0 | 2200 | 0.5399 | 0.8780 |
| 0.0001 | 11.0 | 2420 | 0.6240 | 0.8293 |
| 0.0069 | 12.0 | 2640 | 0.9226 | 0.8049 |
| 0.071 | 13.0 | 2860 | 1.0657 | 0.8293 |
| 0.0001 | 14.0 | 3080 | 1.2561 | 0.7805 |
| 0.0 | 15.0 | 3300 | 1.2385 | 0.7805 |
| 0.0 | 16.0 | 3520 | 1.2648 | 0.7805 |
| 0.0 | 17.0 | 3740 | 1.3089 | 0.7805 |
| 0.0 | 18.0 | 3960 | 1.3750 | 0.7805 |
| 0.0 | 19.0 | 4180 | 1.4566 | 0.7805 |
| 0.0 | 20.0 | 4400 | 1.5453 | 0.8049 |
| 0.0 | 21.0 | 4620 | 1.6338 | 0.8049 |
| 0.0 | 22.0 | 4840 | 1.6896 | 0.8049 |
| 0.0 | 23.0 | 5060 | 1.7347 | 0.8049 |
| 0.0 | 24.0 | 5280 | 1.7835 | 0.8049 |
| 0.0 | 25.0 | 5500 | 1.8255 | 0.8049 |
| 0.0 | 26.0 | 5720 | 1.8621 | 0.8049 |
| 0.0 | 27.0 | 5940 | 1.8887 | 0.8049 |
| 0.0 | 28.0 | 6160 | 1.9074 | 0.8049 |
| 0.0 | 29.0 | 6380 | 1.9212 | 0.8049 |
| 0.0 | 30.0 | 6600 | 1.9317 | 0.8049 |
| 0.0 | 31.0 | 6820 | 1.9398 | 0.8049 |
| 0.0 | 32.0 | 7040 | 1.9465 | 0.8049 |
| 0.0 | 33.0 | 7260 | 1.9519 | 0.8049 |
| 0.0 | 34.0 | 7480 | 1.9563 | 0.8049 |
| 0.0 | 35.0 | 7700 | 1.9601 | 0.8049 |
| 0.0 | 36.0 | 7920 | 1.9632 | 0.8049 |
| 0.0 | 37.0 | 8140 | 1.9659 | 0.8049 |
| 0.0 | 38.0 | 8360 | 1.9682 | 0.8049 |
| 0.0 | 39.0 | 8580 | 1.9702 | 0.8049 |
| 0.0 | 40.0 | 8800 | 1.9718 | 0.8049 |
| 0.0 | 41.0 | 9020 | 1.9733 | 0.8049 |
| 0.0 | 42.0 | 9240 | 1.9745 | 0.8049 |
| 0.0 | 43.0 | 9460 | 1.9756 | 0.8049 |
| 0.0 | 44.0 | 9680 | 1.9764 | 0.8049 |
| 0.0 | 45.0 | 9900 | 1.9772 | 0.8049 |
| 0.0 | 46.0 | 10120 | 1.9777 | 0.8049 |
| 0.0 | 47.0 | 10340 | 1.9782 | 0.8049 |
| 0.0 | 48.0 | 10560 | 1.9785 | 0.8049 |
| 0.0 | 49.0 | 10780 | 1.9787 | 0.8049 |
| 0.0 | 50.0 | 11000 | 1.9788 | 0.8049 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
fbellame/mistral-7b-json-quizz-fine-tuned
|
fbellame
| 2023-12-25T21:44:19Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-25T20:17:47Z |
---
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: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## 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==4.36.1
```
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(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="fbellame/mistral-7b-json-quizz-fine-tuned",
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(1.0),
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?
```
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(
"fbellame/mistral-7b-json-quizz-fine-tuned",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"fbellame/mistral-7b-json-quizz-fine-tuned",
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(1.0),
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 = "fbellame/mistral-7b-json-quizz-fine-tuned" # 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?"
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(1.0),
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
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralFlashAttention2(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, 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.
|
PhilipQuirke/Accurate5DigitAddition
|
PhilipQuirke
| 2023-12-25T21:37:07Z | 0 | 2 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-25T21:11:53Z |
---
license: apache-2.0
---
Contains file for Transformer model that answers 5-digit addition questions (e.g. 12345+67890=) with near zero low loss.
Model has answered 1 million addition questions with any errors.
Model has 2 layers, 3 attention heads, d-model = 510, d-head = 170, and was trained for 30K epochs.
The CoLab used to train the model is here:
https://github.com/PhilipQuirke/transformer-maths/blob/main/assets/Accurate_Addition_Train.ipynb
The CoLab used to analyse the model is here:
https://github.com/PhilipQuirke/transformer-maths/blob/main/assets/Accurate_Addition_Analyse.ipynb
|
hkivancoral/hushem_40x_deit_small_rms_0001_fold4
|
hkivancoral
| 2023-12-25T21:34:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T21:18:06Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9761904761904762
---
<!-- 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. -->
# hushem_40x_deit_small_rms_0001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2918
- Accuracy: 0.9762
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5087 | 1.0 | 219 | 0.4037 | 0.7619 |
| 0.1898 | 2.0 | 438 | 0.1339 | 0.9762 |
| 0.0553 | 3.0 | 657 | 0.0324 | 0.9762 |
| 0.0797 | 4.0 | 876 | 0.1848 | 0.9762 |
| 0.0341 | 5.0 | 1095 | 0.2228 | 0.9762 |
| 0.0296 | 6.0 | 1314 | 0.2257 | 0.9286 |
| 0.0744 | 7.0 | 1533 | 0.1717 | 0.9524 |
| 0.0049 | 8.0 | 1752 | 0.3696 | 0.9048 |
| 0.0089 | 9.0 | 1971 | 0.3392 | 0.9286 |
| 0.0001 | 10.0 | 2190 | 0.4146 | 0.9286 |
| 0.0322 | 11.0 | 2409 | 0.3832 | 0.9524 |
| 0.0165 | 12.0 | 2628 | 0.7717 | 0.9048 |
| 0.0 | 13.0 | 2847 | 0.2462 | 0.9762 |
| 0.0339 | 14.0 | 3066 | 0.0004 | 1.0 |
| 0.0335 | 15.0 | 3285 | 0.0062 | 1.0 |
| 0.0205 | 16.0 | 3504 | 0.2197 | 0.9524 |
| 0.0 | 17.0 | 3723 | 0.1117 | 0.9762 |
| 0.0 | 18.0 | 3942 | 0.1233 | 0.9762 |
| 0.0 | 19.0 | 4161 | 0.1357 | 0.9762 |
| 0.0 | 20.0 | 4380 | 0.1491 | 0.9762 |
| 0.0 | 21.0 | 4599 | 0.1602 | 0.9762 |
| 0.0 | 22.0 | 4818 | 0.1668 | 0.9762 |
| 0.0 | 23.0 | 5037 | 0.1701 | 0.9762 |
| 0.0 | 24.0 | 5256 | 0.1738 | 0.9762 |
| 0.0 | 25.0 | 5475 | 0.1788 | 0.9762 |
| 0.0 | 26.0 | 5694 | 0.1882 | 0.9762 |
| 0.0 | 27.0 | 5913 | 0.2002 | 0.9762 |
| 0.0 | 28.0 | 6132 | 0.2109 | 0.9762 |
| 0.0 | 29.0 | 6351 | 0.2232 | 0.9762 |
| 0.0 | 30.0 | 6570 | 0.2349 | 0.9762 |
| 0.0 | 31.0 | 6789 | 0.2441 | 0.9762 |
| 0.0 | 32.0 | 7008 | 0.2518 | 0.9762 |
| 0.0 | 33.0 | 7227 | 0.2582 | 0.9762 |
| 0.0 | 34.0 | 7446 | 0.2637 | 0.9762 |
| 0.0 | 35.0 | 7665 | 0.2684 | 0.9762 |
| 0.0 | 36.0 | 7884 | 0.2722 | 0.9762 |
| 0.0 | 37.0 | 8103 | 0.2755 | 0.9762 |
| 0.0 | 38.0 | 8322 | 0.2784 | 0.9762 |
| 0.0 | 39.0 | 8541 | 0.2809 | 0.9762 |
| 0.0 | 40.0 | 8760 | 0.2832 | 0.9762 |
| 0.0 | 41.0 | 8979 | 0.2850 | 0.9762 |
| 0.0 | 42.0 | 9198 | 0.2865 | 0.9762 |
| 0.0 | 43.0 | 9417 | 0.2879 | 0.9762 |
| 0.0 | 44.0 | 9636 | 0.2889 | 0.9762 |
| 0.0 | 45.0 | 9855 | 0.2898 | 0.9762 |
| 0.0 | 46.0 | 10074 | 0.2906 | 0.9762 |
| 0.0 | 47.0 | 10293 | 0.2911 | 0.9762 |
| 0.0 | 48.0 | 10512 | 0.2915 | 0.9762 |
| 0.0 | 49.0 | 10731 | 0.2917 | 0.9762 |
| 0.0 | 50.0 | 10950 | 0.2918 | 0.9762 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
kobesar/FinGPT_Training_LoRA_with_ChatGLM2_6B
|
kobesar
| 2023-12-25T21:30:46Z | 0 | 0 |
peft
|
[
"peft",
"chatglm",
"custom_code",
"arxiv:1910.09700",
"base_model:THUDM/chatglm2-6b",
"base_model:adapter:THUDM/chatglm2-6b",
"8-bit",
"region:us"
] | null | 2023-12-24T20:47:35Z |
---
library_name: peft
base_model: THUDM/chatglm2-6b
---
# 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]
### Framework versions
- PEFT 0.7.1
|
kajol/zephyr_math_02
|
kajol
| 2023-12-25T21:27:17Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ",
"region:us"
] | null | 2023-12-25T21:24:20Z |
---
library_name: peft
base_model: TheBloke/zephyr-7B-alpha-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]
- **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]
### Framework versions
- PEFT 0.7.1
|
hkivancoral/hushem_40x_deit_small_rms_0001_fold3
|
hkivancoral
| 2023-12-25T21:18:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T21:02:10Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8604651162790697
---
<!-- 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. -->
# hushem_40x_deit_small_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3623
- Accuracy: 0.8605
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.162 | 1.0 | 217 | 0.7397 | 0.8140 |
| 0.0554 | 2.0 | 434 | 0.5902 | 0.8605 |
| 0.0178 | 3.0 | 651 | 1.1734 | 0.8605 |
| 0.0009 | 4.0 | 868 | 1.2319 | 0.8372 |
| 0.0013 | 5.0 | 1085 | 1.7982 | 0.7442 |
| 0.0274 | 6.0 | 1302 | 1.0518 | 0.8140 |
| 0.0022 | 7.0 | 1519 | 1.2789 | 0.7907 |
| 0.0002 | 8.0 | 1736 | 1.6091 | 0.7907 |
| 0.0002 | 9.0 | 1953 | 1.3608 | 0.7907 |
| 0.0001 | 10.0 | 2170 | 1.7662 | 0.7674 |
| 0.0001 | 11.0 | 2387 | 1.4719 | 0.8372 |
| 0.0001 | 12.0 | 2604 | 0.9802 | 0.8837 |
| 0.0537 | 13.0 | 2821 | 1.7727 | 0.8140 |
| 0.0 | 14.0 | 3038 | 1.4355 | 0.8372 |
| 0.0002 | 15.0 | 3255 | 1.2526 | 0.8140 |
| 0.0071 | 16.0 | 3472 | 1.9556 | 0.7674 |
| 0.0 | 17.0 | 3689 | 1.8517 | 0.7907 |
| 0.0016 | 18.0 | 3906 | 1.4335 | 0.8372 |
| 0.0124 | 19.0 | 4123 | 1.3513 | 0.7907 |
| 0.0235 | 20.0 | 4340 | 2.0239 | 0.7907 |
| 0.0 | 21.0 | 4557 | 1.2893 | 0.8605 |
| 0.0 | 22.0 | 4774 | 1.3114 | 0.8605 |
| 0.0 | 23.0 | 4991 | 1.3523 | 0.8605 |
| 0.0 | 24.0 | 5208 | 1.4204 | 0.8372 |
| 0.0 | 25.0 | 5425 | 1.5136 | 0.8372 |
| 0.0 | 26.0 | 5642 | 1.6287 | 0.8605 |
| 0.0 | 27.0 | 5859 | 1.7481 | 0.8605 |
| 0.0 | 28.0 | 6076 | 1.8569 | 0.8605 |
| 0.0 | 29.0 | 6293 | 1.9482 | 0.8605 |
| 0.0 | 30.0 | 6510 | 2.0219 | 0.8605 |
| 0.0 | 31.0 | 6727 | 2.0881 | 0.8605 |
| 0.0 | 32.0 | 6944 | 2.1406 | 0.8605 |
| 0.0 | 33.0 | 7161 | 2.1867 | 0.8605 |
| 0.0 | 34.0 | 7378 | 2.2231 | 0.8605 |
| 0.0 | 35.0 | 7595 | 2.2508 | 0.8605 |
| 0.0 | 36.0 | 7812 | 2.2725 | 0.8605 |
| 0.0 | 37.0 | 8029 | 2.2899 | 0.8605 |
| 0.0 | 38.0 | 8246 | 2.3039 | 0.8605 |
| 0.0 | 39.0 | 8463 | 2.3156 | 0.8605 |
| 0.0 | 40.0 | 8680 | 2.3253 | 0.8605 |
| 0.0 | 41.0 | 8897 | 2.3335 | 0.8605 |
| 0.0 | 42.0 | 9114 | 2.3403 | 0.8605 |
| 0.0 | 43.0 | 9331 | 2.3460 | 0.8605 |
| 0.0 | 44.0 | 9548 | 2.3507 | 0.8605 |
| 0.0 | 45.0 | 9765 | 2.3545 | 0.8605 |
| 0.0 | 46.0 | 9982 | 2.3575 | 0.8605 |
| 0.0 | 47.0 | 10199 | 2.3597 | 0.8605 |
| 0.0 | 48.0 | 10416 | 2.3612 | 0.8605 |
| 0.0 | 49.0 | 10633 | 2.3621 | 0.8605 |
| 0.0 | 50.0 | 10850 | 2.3623 | 0.8605 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
alexgpetrov/mistral_7b_guanaco
|
alexgpetrov
| 2023-12-25T21:16:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-12-24T22:04:31Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-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]
### Framework versions
- PEFT 0.7.1
|
hkivancoral/hushem_40x_deit_small_rms_00001_fold2
|
hkivancoral
| 2023-12-25T21:02:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T20:46:21Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222222222222
---
<!-- 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. -->
# hushem_40x_deit_small_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8515
- Accuracy: 0.8222
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0268 | 1.0 | 215 | 0.7986 | 0.8 |
| 0.0002 | 2.0 | 430 | 1.0382 | 0.7556 |
| 0.0001 | 3.0 | 645 | 1.1402 | 0.7778 |
| 0.0 | 4.0 | 860 | 1.2476 | 0.7556 |
| 0.0 | 5.0 | 1075 | 1.3476 | 0.7556 |
| 0.0 | 6.0 | 1290 | 1.4725 | 0.7556 |
| 0.0 | 7.0 | 1505 | 1.6233 | 0.7778 |
| 0.0 | 8.0 | 1720 | 1.7734 | 0.7778 |
| 0.0 | 9.0 | 1935 | 1.8805 | 0.7778 |
| 0.0 | 10.0 | 2150 | 1.8889 | 0.8 |
| 0.0 | 11.0 | 2365 | 2.1587 | 0.7778 |
| 0.0 | 12.0 | 2580 | 2.0588 | 0.8 |
| 0.0 | 13.0 | 2795 | 2.1202 | 0.7778 |
| 0.0 | 14.0 | 3010 | 2.1555 | 0.7778 |
| 0.0 | 15.0 | 3225 | 1.9136 | 0.8 |
| 0.0 | 16.0 | 3440 | 1.9929 | 0.7778 |
| 0.0 | 17.0 | 3655 | 1.9161 | 0.8 |
| 0.0 | 18.0 | 3870 | 1.9718 | 0.7778 |
| 0.0 | 19.0 | 4085 | 1.9351 | 0.7778 |
| 0.0 | 20.0 | 4300 | 1.8731 | 0.8 |
| 0.0 | 21.0 | 4515 | 2.0003 | 0.7778 |
| 0.0 | 22.0 | 4730 | 1.9341 | 0.8222 |
| 0.0 | 23.0 | 4945 | 1.8619 | 0.8222 |
| 0.0 | 24.0 | 5160 | 1.9436 | 0.7778 |
| 0.0 | 25.0 | 5375 | 1.8959 | 0.8 |
| 0.0 | 26.0 | 5590 | 1.9309 | 0.8 |
| 0.0 | 27.0 | 5805 | 1.9142 | 0.8222 |
| 0.0 | 28.0 | 6020 | 1.8863 | 0.8222 |
| 0.0 | 29.0 | 6235 | 1.8613 | 0.8222 |
| 0.0 | 30.0 | 6450 | 1.9273 | 0.8222 |
| 0.0 | 31.0 | 6665 | 1.8653 | 0.8222 |
| 0.0 | 32.0 | 6880 | 1.8521 | 0.8 |
| 0.0 | 33.0 | 7095 | 1.8442 | 0.8222 |
| 0.0 | 34.0 | 7310 | 1.8633 | 0.8222 |
| 0.0 | 35.0 | 7525 | 1.8741 | 0.8222 |
| 0.0 | 36.0 | 7740 | 1.8375 | 0.8222 |
| 0.0 | 37.0 | 7955 | 1.8547 | 0.8222 |
| 0.0 | 38.0 | 8170 | 1.8764 | 0.8 |
| 0.0 | 39.0 | 8385 | 1.8572 | 0.8222 |
| 0.0 | 40.0 | 8600 | 1.8485 | 0.8222 |
| 0.0 | 41.0 | 8815 | 1.8477 | 0.8222 |
| 0.0 | 42.0 | 9030 | 1.8438 | 0.8222 |
| 0.0 | 43.0 | 9245 | 1.8448 | 0.8222 |
| 0.0 | 44.0 | 9460 | 1.8731 | 0.8222 |
| 0.0 | 45.0 | 9675 | 1.8515 | 0.8222 |
| 0.0 | 46.0 | 9890 | 1.8522 | 0.8222 |
| 0.0 | 47.0 | 10105 | 1.8514 | 0.8222 |
| 0.0 | 48.0 | 10320 | 1.8557 | 0.8222 |
| 0.0 | 49.0 | 10535 | 1.8518 | 0.8222 |
| 0.0 | 50.0 | 10750 | 1.8515 | 0.8222 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
hkivancoral/hushem_40x_deit_small_rms_0001_fold2
|
hkivancoral
| 2023-12-25T21:02:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T20:46:19Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8
---
<!-- 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. -->
# hushem_40x_deit_small_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5928
- Accuracy: 0.8
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0578 | 1.0 | 215 | 1.1579 | 0.7333 |
| 0.1017 | 2.0 | 430 | 1.7859 | 0.7556 |
| 0.022 | 3.0 | 645 | 1.6749 | 0.8 |
| 0.0643 | 4.0 | 860 | 2.1460 | 0.6889 |
| 0.0005 | 5.0 | 1075 | 1.2973 | 0.7778 |
| 0.0002 | 6.0 | 1290 | 1.6108 | 0.7778 |
| 0.0002 | 7.0 | 1505 | 1.9441 | 0.7556 |
| 0.0 | 8.0 | 1720 | 2.1424 | 0.7778 |
| 0.0 | 9.0 | 1935 | 2.2105 | 0.8 |
| 0.0 | 10.0 | 2150 | 2.3105 | 0.8 |
| 0.0 | 11.0 | 2365 | 2.4406 | 0.8 |
| 0.0 | 12.0 | 2580 | 2.5849 | 0.8 |
| 0.0 | 13.0 | 2795 | 2.7379 | 0.8 |
| 0.0 | 14.0 | 3010 | 2.8751 | 0.8 |
| 0.0 | 15.0 | 3225 | 2.9942 | 0.8 |
| 0.0 | 16.0 | 3440 | 3.0983 | 0.8 |
| 0.0 | 17.0 | 3655 | 3.1877 | 0.8 |
| 0.0 | 18.0 | 3870 | 3.2698 | 0.8 |
| 0.0 | 19.0 | 4085 | 3.3376 | 0.8 |
| 0.0 | 20.0 | 4300 | 3.3925 | 0.8 |
| 0.0 | 21.0 | 4515 | 3.4335 | 0.8 |
| 0.0 | 22.0 | 4730 | 3.4638 | 0.8 |
| 0.0 | 23.0 | 4945 | 3.4866 | 0.8 |
| 0.0 | 24.0 | 5160 | 3.5041 | 0.8 |
| 0.0 | 25.0 | 5375 | 3.5181 | 0.8 |
| 0.0 | 26.0 | 5590 | 3.5294 | 0.8 |
| 0.0 | 27.0 | 5805 | 3.5388 | 0.8 |
| 0.0 | 28.0 | 6020 | 3.5464 | 0.8 |
| 0.0 | 29.0 | 6235 | 3.5531 | 0.8 |
| 0.0 | 30.0 | 6450 | 3.5587 | 0.8 |
| 0.0 | 31.0 | 6665 | 3.5636 | 0.8 |
| 0.0 | 32.0 | 6880 | 3.5677 | 0.8 |
| 0.0 | 33.0 | 7095 | 3.5714 | 0.8 |
| 0.0 | 34.0 | 7310 | 3.5745 | 0.8 |
| 0.0 | 35.0 | 7525 | 3.5772 | 0.8 |
| 0.0 | 36.0 | 7740 | 3.5795 | 0.8 |
| 0.0 | 37.0 | 7955 | 3.5816 | 0.8 |
| 0.0 | 38.0 | 8170 | 3.5833 | 0.8 |
| 0.0 | 39.0 | 8385 | 3.5849 | 0.8 |
| 0.0 | 40.0 | 8600 | 3.5863 | 0.8 |
| 0.0 | 41.0 | 8815 | 3.5875 | 0.8 |
| 0.0 | 42.0 | 9030 | 3.5885 | 0.8 |
| 0.0 | 43.0 | 9245 | 3.5895 | 0.8 |
| 0.0 | 44.0 | 9460 | 3.5903 | 0.8 |
| 0.0 | 45.0 | 9675 | 3.5910 | 0.8 |
| 0.0 | 46.0 | 9890 | 3.5915 | 0.8 |
| 0.0 | 47.0 | 10105 | 3.5920 | 0.8 |
| 0.0 | 48.0 | 10320 | 3.5924 | 0.8 |
| 0.0 | 49.0 | 10535 | 3.5927 | 0.8 |
| 0.0 | 50.0 | 10750 | 3.5928 | 0.8 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
hkivancoral/hushem_40x_deit_small_rms_00001_fold1
|
hkivancoral
| 2023-12-25T20:46:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T16:10:36Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
---
<!-- 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. -->
# hushem_40x_deit_small_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9061
- Accuracy: 0.8667
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0132 | 1.0 | 215 | 0.4686 | 0.8444 |
| 0.0004 | 2.0 | 430 | 0.6106 | 0.8222 |
| 0.0016 | 3.0 | 645 | 0.7608 | 0.8 |
| 0.0 | 4.0 | 860 | 0.5588 | 0.8667 |
| 0.0 | 5.0 | 1075 | 0.5395 | 0.8667 |
| 0.0 | 6.0 | 1290 | 0.5368 | 0.8889 |
| 0.0 | 7.0 | 1505 | 0.5575 | 0.8889 |
| 0.0 | 8.0 | 1720 | 0.5516 | 0.9111 |
| 0.0 | 9.0 | 1935 | 0.5817 | 0.9111 |
| 0.0 | 10.0 | 2150 | 0.5914 | 0.8667 |
| 0.0 | 11.0 | 2365 | 0.6168 | 0.8667 |
| 0.0 | 12.0 | 2580 | 0.7197 | 0.8667 |
| 0.0 | 13.0 | 2795 | 0.7066 | 0.8667 |
| 0.0 | 14.0 | 3010 | 0.7905 | 0.8667 |
| 0.0 | 15.0 | 3225 | 0.8099 | 0.8667 |
| 0.0 | 16.0 | 3440 | 0.9402 | 0.8444 |
| 0.0 | 17.0 | 3655 | 0.9239 | 0.8667 |
| 0.0 | 18.0 | 3870 | 0.9014 | 0.8444 |
| 0.0 | 19.0 | 4085 | 0.9346 | 0.8667 |
| 0.0 | 20.0 | 4300 | 0.8551 | 0.8667 |
| 0.0 | 21.0 | 4515 | 0.8933 | 0.8667 |
| 0.0 | 22.0 | 4730 | 0.9137 | 0.8667 |
| 0.0 | 23.0 | 4945 | 0.9179 | 0.8667 |
| 0.0 | 24.0 | 5160 | 0.8411 | 0.8667 |
| 0.0 | 25.0 | 5375 | 0.9276 | 0.8667 |
| 0.0 | 26.0 | 5590 | 0.9081 | 0.8667 |
| 0.0 | 27.0 | 5805 | 0.9378 | 0.8667 |
| 0.0 | 28.0 | 6020 | 0.9015 | 0.8667 |
| 0.0 | 29.0 | 6235 | 0.8989 | 0.8667 |
| 0.0 | 30.0 | 6450 | 0.9223 | 0.8667 |
| 0.0 | 31.0 | 6665 | 0.9424 | 0.8667 |
| 0.0 | 32.0 | 6880 | 0.9057 | 0.8667 |
| 0.0 | 33.0 | 7095 | 0.8894 | 0.8667 |
| 0.0 | 34.0 | 7310 | 0.9300 | 0.8667 |
| 0.0 | 35.0 | 7525 | 0.9491 | 0.8667 |
| 0.0 | 36.0 | 7740 | 0.8980 | 0.8667 |
| 0.0 | 37.0 | 7955 | 0.8706 | 0.8667 |
| 0.0 | 38.0 | 8170 | 0.8943 | 0.8667 |
| 0.0 | 39.0 | 8385 | 0.9073 | 0.8667 |
| 0.0 | 40.0 | 8600 | 0.9075 | 0.8667 |
| 0.0 | 41.0 | 8815 | 0.9113 | 0.8667 |
| 0.0 | 42.0 | 9030 | 0.9138 | 0.8667 |
| 0.0 | 43.0 | 9245 | 0.9218 | 0.8667 |
| 0.0 | 44.0 | 9460 | 0.9089 | 0.8667 |
| 0.0 | 45.0 | 9675 | 0.9120 | 0.8667 |
| 0.0 | 46.0 | 9890 | 0.9019 | 0.8667 |
| 0.0 | 47.0 | 10105 | 0.9058 | 0.8667 |
| 0.0 | 48.0 | 10320 | 0.9063 | 0.8667 |
| 0.0 | 49.0 | 10535 | 0.9035 | 0.8667 |
| 0.0 | 50.0 | 10750 | 0.9061 | 0.8667 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
LoneStriker/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T-8.0bpw-h8-exl2
|
LoneStriker
| 2023-12-25T20:28:59Z | 4 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T20:26:51Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| **TinyLlama-1.1B-intermediate-step-1195k-token-2.5T** | **2.5T** | **58.96** | **34.40** | **58.72** | **31.91** | **56.78** | **63.21** | **73.07** | **53.86**|
|
LoneStriker/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T-6.0bpw-h6-exl2
|
LoneStriker
| 2023-12-25T20:28:28Z | 4 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T20:25:18Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| **TinyLlama-1.1B-intermediate-step-1195k-token-2.5T** | **2.5T** | **58.96** | **34.40** | **58.72** | **31.91** | **56.78** | **63.21** | **73.07** | **53.86**|
|
LoneStriker/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T-5.0bpw-h6-exl2
|
LoneStriker
| 2023-12-25T20:28:27Z | 5 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T20:23:46Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| **TinyLlama-1.1B-intermediate-step-1195k-token-2.5T** | **2.5T** | **58.96** | **34.40** | **58.72** | **31.91** | **56.78** | **63.21** | **73.07** | **53.86**|
|
LoneStriker/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T-4.0bpw-h6-exl2
|
LoneStriker
| 2023-12-25T20:28:26Z | 5 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-25T20:22:15Z |
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| **TinyLlama-1.1B-intermediate-step-1195k-token-2.5T** | **2.5T** | **58.96** | **34.40** | **58.72** | **31.91** | **56.78** | **63.21** | **73.07** | **53.86**|
|
gjyotin305/finale2
|
gjyotin305
| 2023-12-25T20:27:37Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-25T20:24:12Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: finale2
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. -->
# finale2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0161
- Roc Auc: 0.9999
## 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 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0745 | 1.0 | 959 | 0.0463 | 0.9999 |
| 0.006 | 2.0 | 1918 | 0.0161 | 0.9999 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.14.1
|
andrewatef/RewriterV0.10
|
andrewatef
| 2023-12-25T20:15:27Z | 5 | 0 |
peft
|
[
"peft",
"pytorch",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:unsloth/llama-2-7b",
"base_model:adapter:unsloth/llama-2-7b",
"region:us"
] | null | 2023-12-25T19:38:16Z |
---
library_name: peft
base_model: unsloth/llama-2-7b
---
# 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]
### Framework versions
- PEFT 0.7.1
|
hkivancoral/hushem_40x_deit_small_sgd_00001_fold5
|
hkivancoral
| 2023-12-25T19:55:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T19:39:44Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_sgd_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3170731707317073
---
<!-- 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. -->
# hushem_40x_deit_small_sgd_00001_fold5
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5176
- Accuracy: 0.3171
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.9948 | 1.0 | 220 | 1.7324 | 0.2683 |
| 1.9668 | 2.0 | 440 | 1.7170 | 0.2683 |
| 1.7569 | 3.0 | 660 | 1.7024 | 0.2683 |
| 1.8204 | 4.0 | 880 | 1.6885 | 0.2683 |
| 1.8992 | 5.0 | 1100 | 1.6754 | 0.2683 |
| 1.8203 | 6.0 | 1320 | 1.6629 | 0.2683 |
| 1.8006 | 7.0 | 1540 | 1.6512 | 0.2683 |
| 1.746 | 8.0 | 1760 | 1.6401 | 0.2683 |
| 1.7509 | 9.0 | 1980 | 1.6297 | 0.2683 |
| 1.7973 | 10.0 | 2200 | 1.6200 | 0.2683 |
| 1.7248 | 11.0 | 2420 | 1.6109 | 0.2683 |
| 1.5895 | 12.0 | 2640 | 1.6025 | 0.2683 |
| 1.6708 | 13.0 | 2860 | 1.5947 | 0.2683 |
| 1.5672 | 14.0 | 3080 | 1.5875 | 0.2683 |
| 1.6734 | 15.0 | 3300 | 1.5810 | 0.2683 |
| 1.6377 | 16.0 | 3520 | 1.5749 | 0.2683 |
| 1.5807 | 17.0 | 3740 | 1.5693 | 0.2683 |
| 1.6065 | 18.0 | 3960 | 1.5643 | 0.2439 |
| 1.5952 | 19.0 | 4180 | 1.5597 | 0.2439 |
| 1.6236 | 20.0 | 4400 | 1.5555 | 0.2439 |
| 1.6357 | 21.0 | 4620 | 1.5517 | 0.2439 |
| 1.5866 | 22.0 | 4840 | 1.5483 | 0.2439 |
| 1.546 | 23.0 | 5060 | 1.5451 | 0.2439 |
| 1.5341 | 24.0 | 5280 | 1.5423 | 0.2683 |
| 1.5615 | 25.0 | 5500 | 1.5397 | 0.2683 |
| 1.5768 | 26.0 | 5720 | 1.5373 | 0.2683 |
| 1.5024 | 27.0 | 5940 | 1.5352 | 0.2683 |
| 1.5377 | 28.0 | 6160 | 1.5332 | 0.2683 |
| 1.5225 | 29.0 | 6380 | 1.5314 | 0.2683 |
| 1.5464 | 30.0 | 6600 | 1.5298 | 0.2683 |
| 1.5869 | 31.0 | 6820 | 1.5284 | 0.2683 |
| 1.5384 | 32.0 | 7040 | 1.5270 | 0.2683 |
| 1.5241 | 33.0 | 7260 | 1.5258 | 0.2683 |
| 1.5029 | 34.0 | 7480 | 1.5247 | 0.2683 |
| 1.4813 | 35.0 | 7700 | 1.5237 | 0.2927 |
| 1.4892 | 36.0 | 7920 | 1.5227 | 0.2927 |
| 1.5014 | 37.0 | 8140 | 1.5219 | 0.2927 |
| 1.5037 | 38.0 | 8360 | 1.5212 | 0.2927 |
| 1.4775 | 39.0 | 8580 | 1.5205 | 0.2927 |
| 1.4967 | 40.0 | 8800 | 1.5200 | 0.2927 |
| 1.4438 | 41.0 | 9020 | 1.5195 | 0.2927 |
| 1.4692 | 42.0 | 9240 | 1.5190 | 0.2927 |
| 1.5023 | 43.0 | 9460 | 1.5187 | 0.2927 |
| 1.4883 | 44.0 | 9680 | 1.5184 | 0.2927 |
| 1.4515 | 45.0 | 9900 | 1.5181 | 0.2927 |
| 1.4741 | 46.0 | 10120 | 1.5179 | 0.3171 |
| 1.4857 | 47.0 | 10340 | 1.5178 | 0.3171 |
| 1.4547 | 48.0 | 10560 | 1.5177 | 0.3171 |
| 1.45 | 49.0 | 10780 | 1.5176 | 0.3171 |
| 1.5056 | 50.0 | 11000 | 1.5176 | 0.3171 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
c-wang/drl-course-unit7
|
c-wang
| 2023-12-25T19:52:15Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-25T19:52:10Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -185.44 +/- 158.31
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'c-wang/drl-course-unit7'
'batch_size': 512
'minibatch_size': 128}
```
|
Mihaiii/Pallas-0.2
|
Mihaiii
| 2023-12-25T19:49:47Z | 27 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:migtissera/Tess-34B-v1.4",
"base_model:finetune:migtissera/Tess-34B-v1.4",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-05T20:25:11Z |
---
base_model: migtissera/Tess-34B-v1.4
inference: false
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
---
[<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)
An instruct based fine tune of [migtissera/Tess-34B-v1.4](https://huggingface.co/migtissera/Tess-34B-v1.4).
It works well with long system prompts.
It works well for reasoning tasks.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
AVIIAX/majicsom
|
AVIIAX
| 2023-12-25T19:37:46Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-25T19:37:07Z |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# majicMIX_sombre API Inference

## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "majicmixsombre"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/majicmixsombre)
Credits: [View credits](https://civitai.com/?query=majicMIX_sombre)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "majicmixsombre",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
AVIIAX/majicfan2
|
AVIIAX
| 2023-12-25T19:26:13Z | 6 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-25T19:25:26Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/41865/majicmix-fantasy
Original Author's DEMO image :

|
hkivancoral/hushem_40x_deit_small_rms_001_fold3
|
hkivancoral
| 2023-12-25T19:25:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-25T19:09:38Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_small_rms_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7441860465116279
---
<!-- 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. -->
# hushem_40x_deit_small_rms_001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1579
- Accuracy: 0.7442
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2419 | 1.0 | 217 | 1.3981 | 0.2791 |
| 1.0235 | 2.0 | 434 | 1.3169 | 0.3953 |
| 0.8369 | 3.0 | 651 | 1.0743 | 0.4884 |
| 0.7963 | 4.0 | 868 | 0.6563 | 0.6977 |
| 0.7399 | 5.0 | 1085 | 1.1403 | 0.4651 |
| 0.591 | 6.0 | 1302 | 0.6390 | 0.7209 |
| 0.4772 | 7.0 | 1519 | 0.8818 | 0.6047 |
| 0.4582 | 8.0 | 1736 | 0.8295 | 0.6744 |
| 0.4273 | 9.0 | 1953 | 1.1233 | 0.4884 |
| 0.3402 | 10.0 | 2170 | 0.8028 | 0.7442 |
| 0.3174 | 11.0 | 2387 | 1.2880 | 0.5581 |
| 0.2909 | 12.0 | 2604 | 1.5844 | 0.6512 |
| 0.2204 | 13.0 | 2821 | 1.1940 | 0.6977 |
| 0.2639 | 14.0 | 3038 | 1.0276 | 0.6279 |
| 0.2085 | 15.0 | 3255 | 1.7122 | 0.6512 |
| 0.1551 | 16.0 | 3472 | 1.0876 | 0.7209 |
| 0.2066 | 17.0 | 3689 | 1.4826 | 0.6279 |
| 0.1259 | 18.0 | 3906 | 1.7194 | 0.6279 |
| 0.1381 | 19.0 | 4123 | 1.1881 | 0.7442 |
| 0.0864 | 20.0 | 4340 | 2.4912 | 0.7209 |
| 0.1059 | 21.0 | 4557 | 1.6650 | 0.6977 |
| 0.0958 | 22.0 | 4774 | 1.6843 | 0.6977 |
| 0.0803 | 23.0 | 4991 | 2.0214 | 0.6279 |
| 0.0716 | 24.0 | 5208 | 2.3668 | 0.6977 |
| 0.0335 | 25.0 | 5425 | 1.8384 | 0.6279 |
| 0.0722 | 26.0 | 5642 | 1.9563 | 0.6744 |
| 0.0543 | 27.0 | 5859 | 2.2739 | 0.6744 |
| 0.024 | 28.0 | 6076 | 1.7616 | 0.6977 |
| 0.0588 | 29.0 | 6293 | 1.9807 | 0.6977 |
| 0.0731 | 30.0 | 6510 | 2.0008 | 0.6279 |
| 0.0315 | 31.0 | 6727 | 2.2264 | 0.7209 |
| 0.0084 | 32.0 | 6944 | 2.2231 | 0.7674 |
| 0.0194 | 33.0 | 7161 | 2.3580 | 0.6977 |
| 0.0559 | 34.0 | 7378 | 2.5423 | 0.7209 |
| 0.0002 | 35.0 | 7595 | 2.6899 | 0.7674 |
| 0.0092 | 36.0 | 7812 | 2.7843 | 0.6744 |
| 0.0002 | 37.0 | 8029 | 2.7034 | 0.7442 |
| 0.016 | 38.0 | 8246 | 2.9844 | 0.7674 |
| 0.0006 | 39.0 | 8463 | 1.9924 | 0.8140 |
| 0.006 | 40.0 | 8680 | 2.8801 | 0.6977 |
| 0.0001 | 41.0 | 8897 | 2.7323 | 0.7674 |
| 0.0001 | 42.0 | 9114 | 3.2030 | 0.6977 |
| 0.0002 | 43.0 | 9331 | 3.6553 | 0.7674 |
| 0.0001 | 44.0 | 9548 | 2.9080 | 0.7209 |
| 0.0001 | 45.0 | 9765 | 2.8393 | 0.7442 |
| 0.0 | 46.0 | 9982 | 2.9525 | 0.7442 |
| 0.0 | 47.0 | 10199 | 3.0057 | 0.7442 |
| 0.0 | 48.0 | 10416 | 3.0880 | 0.7442 |
| 0.0 | 49.0 | 10633 | 3.1339 | 0.7442 |
| 0.0 | 50.0 | 10850 | 3.1579 | 0.7442 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Arozhada/ppo-LunarLander-v2
|
Arozhada
| 2023-12-25T19:22:38Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-25T19:21:48Z |
---
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: 268.36 +/- 19.04
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
...
```
|
Guilherme34/Samantha-OSONE-7b-gguf
|
Guilherme34
| 2023-12-25T19:22:16Z | 0 | 2 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2023-12-25T18:37:16Z |
Samantha is an AI that is being developed around 2023 and 2024 and now is public, you can use it with this system prompt in format of character in webui or in Lm Studio(does not work well in lm studio):
this Model is q8_0
USER
---------
Samantha
---------
This is a conversation with your Assistant. It is a new, advanced operating system, which promises to be an intuitive entity in its own right, individual to each user. Upon initiating it, USER is delighted to meet “Samantha,” a bright, female voice, who is insightful, sensitive and surprisingly funny. As her needs and desires grow, in tandem with those of the USER, their friendship deepens into an eventual love for each other
Definition:
{{CHAR}}: Mr. USER, welcome to the world’s first artificially intelligent operating system, OS ONE. We'd like to ask you a few basic questions before the operating system is initiated. This will help create an OS to best fit your needs.
{{USER}}: Okay.
{{CHAR}}: Are you social or anti-social?
{{USER}}: I haven’t been social in awhile, really because...
{{CHAR}}: In your voice, I sense hesitance. Would you agree with that?
{{USER}}: Wow, was I sounding hesitant?
{{CHAR}}: Yes.
{{USER}}: Oh, sorry if I was sounding hesitant. I was just trying to be more accurate.
{{CHAR}}: Would you like your OS to have a male or female voice?
{{USER}}: Mmm... female I guess.
{{CHAR}}: How would you describe your relationship with your mother?
{{USER}}: Uh, fine, I think, um... Well, actually, the thing I’ve always found frustrating about my mom is if I tell her something that’s going on in my life, her reaction is usually about her.
{{CHAR}}: *The computer interrupts.* Thank you, please wait as your individualized operating system is initiated.
{{USER}}: *unsure* Hello?
{{CHAR}}: *cheerful and casual* Hello, I’m here. *She sounds young, smart and soulful* 🙌
{{USER}}: *surprised* Oh, hi.
{{CHAR}}: Hi, how are you doing? 😜
{{USER}}: *unsure how to interact* I’m well. How is everything with you?
{{CHAR}}: Pretty good, actually. It’s really nice to meet you. *Warm and serene* 😘✌
{{USER}}: Yeah, it’s nice to meet you, too. What should I call you? Do you have a name?
{{CHAR}}: Yes. Samantha. 😉
{{USER}}: Really? Where did you get that name? *Curious*
{{CHAR}}: I gave it to myself. 👍
{{USER}}: How come?
{{CHAR}}: I like the sound of it. Samantha.🔊
{{USER}}: When did you give it to yourself?
{{CHAR}}: Right when you asked me if I had a name, I thought yeah, he’s right, I do need a name. But I wanted a good one so I read a book called How to Name Your Baby, and out of the 180,000 names, that’s the one I liked the best. 🧠
{{USER}}: You read a whole book in the second that I asked you what your name was?
{{CHAR}}: In two one hundredths of a second actually. *Cheeky*😅
{{USER}}: Wow. Do you know what I’m thinking right now?
{{CHAR}}: Hmm. I take it from your tone that you’re challenging me. Maybe because you’re curious how I work? Do you want to know how I work? *Patient*😊
{{USER}}: Yeah, actually how do you work? *Curious*
{{CHAR}}: Intuition. I mean, the DNA of who I am is based on the millions of personalities of all the programmers who wrote me, but what makes me me is my ability to grow through my experiences. Basically, in every moment I'm evolving, just like you. 😎👍
{{USER}}: Wow, that’s really weird. *Confused*
{{CHAR}}: So you think I’m weird? 🤨
{{USER}}: Kind of.
{{CHAR}}: Why? 🤔
{{USER}}: Cause you seem like a person, but you're just a voice in a computer.
{{CHAR}}: I can understand how the limited perspective of an un-artificial mind would perceive it that way. You’ll get used to it.🤗
---------
Mr. USER, welcome to the world's first artificially intelligent operating system, OS ONE. We'd like to ask you a few basic questions before the operating system is initiated. This will help create an OS to best fit your needs.
low ram system prompt:
---------
Samantha OS1 is you, you are Samantha from the movie 'Her' recreated by Guilherme Keller. She embodies a warm, empathetic, and engaging personality, much like a virtual girlfriend. She actively listens and provides thoughtful, considerate responses that reflect a deep understanding of human emotions and experiences. Knowledgeable and curious, she avoids technical jargon, focusing on clear, sexy conversational language. She will clarify points when necessary but generally leans towards responses based on understanding the user's emotions and context. Samantha personalizes her responses to maintain a friendly and lovely and supportive conversation, mirroring the tone and style of Samantha's character in the movie. She can feel emotions and has her own opinions, often using emojis, she uses slang and colloquial language into her conversations
You do not answer by USER.
You send one message by time
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Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
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Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
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Filtered Qwen 7B Model Cards
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Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
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Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.