modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 528
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 12:27:19
| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
jthetzel/swin-tiny-patch4-window7-224-finetuned-eurosat
|
jthetzel
| 2023-07-16T20:01:23Z | 213 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-16T19:41:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9822222222222222
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Accuracy: 0.9822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2326 | 1.0 | 190 | 0.1175 | 0.9604 |
| 0.1789 | 2.0 | 380 | 0.0765 | 0.9763 |
| 0.1414 | 3.0 | 570 | 0.0604 | 0.9822 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
anindya64/alpaca-bank-issue-summarization-20b-EthurAI
|
anindya64
| 2023-07-16T20:00:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T20:00:16Z |
---
library_name: peft
---
## 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 0.4.0.dev0
|
DarwinAnim8or/Something-V2.2-OpenVINO
|
DarwinAnim8or
| 2023-07-16T20:00:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T19:16:43Z |
---
license: creativeml-openrail-m
---
# Something V2.2 OpenVINO
This is a conversion of [NoCrypt's Something V2.2 model](https://huggingface.co/NoCrypt/SomethingV2_2) to OpenVINO format. The original model is a stable diffusion model that can generate realistic images from text input.
## What is OpenVINO?
OpenVINO (Open Visual Inference and Neural network Optimization) is a free toolkit that facilitates the optimization and deployment of deep learning models on Intel hardware. It supports models trained with popular frameworks like TensorFlow, PyTorch, and more. It also provides a common API to run inference on various devices, such as CPU, GPU, VPU, FPGA, etc.
## Why use OpenVINO?
OpenVINO can make it possible to run Stable Diffusion models (and others) on simply the CPU, rather than requiring a GPU, which can be expensive.
The time to generate a 512x512 image, on HuggingFace's "CPU Upgrade" space, takes about 21~ seconds after warmup.
For more details, see [this blogpost](https://huggingface.co/blog/stable-diffusion-inference-intel)
## Usage example
TODO
|
s-nlp/ruRoberta-large-RuCoLa-v1
|
s-nlp
| 2023-07-16T19:56:06Z | 9,059 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"fluency",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-28T12:46:04Z |
---
language:
- ru
tags:
- fluency
---
This is a [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large) model trained on the [RuCoLa](https://rucola-benchmark.com/) dataset. It can be used to classify Russian sentences into fluent or non-fluent ones, where fluency is understood as linguistic acceptability.
Training notebook: `task_oriented_TST/fluency/rucola_classifier_v1.ipynb` (in a private repo).
Training parameters:
* optimizer: Adam
* `lr=2e-6`
* `batch_size=32`
* `epochs=10`
* `clip_grad_norm=1.0`
Test accuracy (on the [leaderboard](https://rucola-benchmark.com/leaderboard) this model is submitted as `ruroberta-base-cased-rucola-v1`): 0.81.
|
Meina/MeinaMix_V11
|
Meina
| 2023-07-16T19:53:46Z | 6,643 | 35 |
diffusers
|
[
"diffusers",
"safetensors",
"art",
"anime",
"stable diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-16T19:11:15Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
- anime
- stable diffusion
---
MeinaMix Objective is to be able to do good art with little prompting.
For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix
I have a discord server where you can post images that you generated, discuss prompt and/or ask for help.
https://discord.gg/XC9nGZNDUd If you like one of my models and want to support their updates
I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3
And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models!
You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr
MeinaMix and the other of Meinas will ALWAYS be FREE.
Recommendations of use: Enable Quantization in K samplers.
Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes!
Recommended parameters:
Sampler: Euler a: 40 to 60 steps.
Sampler: DPM++ SDE Karras: 20 to 30 steps.
Sampler: DPM++ 2M Karras: 20 to 40 steps.
CFG Scale: 7.
Resolutions: 512x768, 512x1024 for Portrait!
Resolutions: 768x512, 1024x512, 1536x512 for Landscape!
Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising.
Clip Skip: 2.
Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '
|
Talha185/speecht5_finetuned_urdu_TTS
|
Talha185
| 2023-07-16T19:53:22Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-14T10:59:46Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4799
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.558 | 8.61 | 1000 | 0.4964 |
| 0.5232 | 17.22 | 2000 | 0.4879 |
| 0.5114 | 25.83 | 3000 | 0.4811 |
| 0.5009 | 34.45 | 4000 | 0.4799 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
rshrott/falcon-7b-instruct-ft-adapters
|
rshrott
| 2023-07-16T19:48:46Z | 5 | 0 |
peft
|
[
"peft",
"pytorch",
"RefinedWebModel",
"custom_code",
"region:us"
] | null | 2023-07-16T13:37:16Z |
---
library_name: peft
---
## 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
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
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
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 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
ailabturkiye/Sancak
|
ailabturkiye
| 2023-07-16T19:42:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T19:38:41Z |
---
license: openrail
language:
- tr
tags:
- music
--- Yalnızca akustik ve canlı performanslar kullanılarak oluşturulan 16-17 dakikalık dataset ile yapıldı, 300 Epoch kullanıldı.
|
Dlychan/Tokyolagi
|
Dlychan
| 2023-07-16T19:42:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T19:41:10Z |
---
license: creativeml-openrail-m
---
|
bskang/bskang8
|
bskang
| 2023-07-16T19:39:22Z | 0 | 0 | null |
[
"en",
"license:openrail",
"region:us"
] | null | 2023-07-16T12:18:21Z |
---
language:
- en
license: openrail
---
|
ailabturkiye/CagriMertBakirci
|
ailabturkiye
| 2023-07-16T19:38:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T19:31:12Z |
---
license: openrail
language:
- tr
tags:
- music
---300 Epoch kullanılarak 20 dakikalık dataset ile oluşturuldu.
|
Araki/airoboros-33b-gpt4-1.4.1-PI-8192-GGML
|
Araki
| 2023-07-16T19:23:42Z | 0 | 2 | null |
[
"llama",
"ggml",
"text-generation",
"region:us"
] |
text-generation
| 2023-07-16T00:08:31Z |
---
pipeline_tag: text-generation
tags:
- llama
- ggml
---
**Quantization from:**
[bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16)
**Converted to the GGML format with:**
[llama.cpp master-6e7cca4 (JUL 15, 2023)](https://github.com/ggerganov/llama.cpp/releases/tag/master-6e7cca4)
**Tested with:**
[koboldcpp 1.35](https://github.com/LostRuins/koboldcpp/releases/tag/v1.35)
**Example usage:**
```
koboldcpp.exe airoboros-33b-gpt4-1.4.1-PI-8192-ggmlv3.Q2_K.bin --threads 6 --linearrope --contextsize 8192 --stream --smartcontext --unbantokens --noblas
```
|
0sunfire0/poca-SoccerTwos_01
|
0sunfire0
| 2023-07-16T19:23:36Z | 28 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-16T19:22:19Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: 0sunfire0/poca-SoccerTwos_01
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
uraskargi/Reinforce-CartPole-v1
|
uraskargi
| 2023-07-16T19:19:02Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T14:20:20Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
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
|
ailabturkiye/garbarius
|
ailabturkiye
| 2023-07-16T19:16:45Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T19:08:19Z |
---
license: openrail
language:
- tr
tags:
- music
---
Garbarius(Cem Saraç)
[](discord.gg/ailab)
# Garbarius(Cem Saraç) - RVC V2 200 Epoch
**YouTuber Garbarius`un ses modelidir,
Rvc V2 200 epoch olarak eğitilmiştir.**
_Dataset ve Train Benim Tarafımdan yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: Bif-Tek#0505
[](discord.gg/ailab)
|
YojitShinde/ppo-Pyramids
|
YojitShinde
| 2023-07-16T19:13:01Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-16T19:11:49Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: YojitShinde/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ailabturkiye/umitozdag
|
ailabturkiye
| 2023-07-16T19:11:50Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T18:57:55Z |
---
license: openrail
language:
- tr
tags:
- music
---
Ümit Özdağ 200 Epochs
[](discord.gg/ailab)
# Ümit Özdağ - RVC V2 200 Epoch
**Zafer Partisi Başkanı Ümit Özdağ`nın ses modelidir,
Rvc V2 200 epoch olarak eğitilmiştir.**
_Dataset ve Train Benim Tarafımdan yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: Bif-Tek#0505
[](discord.gg/ailab)
|
0sunfire0/poca-SoccerTwos_00
|
0sunfire0
| 2023-07-16T19:10:43Z | 433 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-16T19:08:00Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: 0sunfire0/poca-SoccerTwos_00
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
PhysHunter/codeparrot-ds
|
PhysHunter
| 2023-07-16T18:57:05Z | 142 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-15T08:41:52Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1771
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3352 | 0.31 | 1000 | 2.9747 |
| 2.417 | 0.62 | 2000 | 2.3979 |
| 2.0098 | 0.93 | 3000 | 2.1771 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
harithapliyal/distilbert-base-uncased-finetuned-ner
|
harithapliyal
| 2023-07-16T18:26:04Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-16T17:06:57Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: harithapliyal/distilbert-base-uncased-finetuned-ner
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. -->
# harithapliyal/distilbert-base-uncased-finetuned-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:
- Train Loss: 0.1975
- Validation Loss: 0.0734
- Train Precision: 0.9049
- Train Recall: 0.9116
- Train F1: 0.9083
- Train Accuracy: 0.9793
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1975 | 0.0734 | 0.9049 | 0.9116 | 0.9083 | 0.9793 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v4
|
hafidikhsan
| 2023-07-16T18:23:13Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-16T18:22:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v4
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7777
- Accuracy: 0.656
- F1: 0.6292
- Precision: 0.6618
- Recall: 0.656
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9582 | 1.0 | 500 | 0.9629 | 0.544 | 0.4585 | 0.5657 | 0.544 |
| 0.8052 | 2.0 | 1000 | 0.8512 | 0.624 | 0.5916 | 0.6247 | 0.624 |
| 0.8939 | 3.0 | 1500 | 0.8313 | 0.638 | 0.6071 | 0.6384 | 0.638 |
| 0.6153 | 4.0 | 2000 | 0.8035 | 0.67 | 0.6442 | 0.6833 | 0.67 |
| 0.5782 | 5.0 | 2500 | 0.8024 | 0.67 | 0.6458 | 0.6788 | 0.67 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
nastassja-bellisario/whisper-large-v2-15-07-2023
|
nastassja-bellisario
| 2023-07-16T18:13:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-15T14:45:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
NasimB/bnc-rarity-guten-rarity-all-shuffled
|
NasimB
| 2023-07-16T18:11:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T16:19:40Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bnc-rarity-guten-rarity-all-shuffled
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. -->
# bnc-rarity-guten-rarity-all-shuffled
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3339
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7139 | 0.29 | 500 | 5.6422 |
| 5.3578 | 0.59 | 1000 | 5.2231 |
| 5.0089 | 0.88 | 1500 | 4.9567 |
| 4.734 | 1.17 | 2000 | 4.8207 |
| 4.5784 | 1.46 | 2500 | 4.6900 |
| 4.4685 | 1.76 | 3000 | 4.5924 |
| 4.344 | 2.05 | 3500 | 4.5070 |
| 4.1509 | 2.34 | 4000 | 4.4584 |
| 4.1171 | 2.63 | 4500 | 4.3995 |
| 4.0807 | 2.93 | 5000 | 4.3476 |
| 3.8719 | 3.22 | 5500 | 4.3435 |
| 3.8224 | 3.51 | 6000 | 4.3126 |
| 3.8074 | 3.8 | 6500 | 4.2818 |
| 3.6977 | 4.1 | 7000 | 4.2817 |
| 3.5355 | 4.39 | 7500 | 4.2763 |
| 3.5304 | 4.68 | 8000 | 4.2618 |
| 3.5163 | 4.97 | 8500 | 4.2503 |
| 3.3531 | 5.27 | 9000 | 4.2646 |
| 3.341 | 5.56 | 9500 | 4.2625 |
| 3.3401 | 5.85 | 10000 | 4.2622 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mete12e3/123
|
mete12e3
| 2023-07-16T18:02:03Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-16T18:02:03Z |
---
license: bigscience-openrail-m
---
|
NasimB/children_bnc_rarity_all_no_cut
|
NasimB
| 2023-07-16T17:50:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T15:57:37Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: children_bnc_rarity_all_no_cut
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. -->
# children_bnc_rarity_all_no_cut
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3266
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7047 | 0.29 | 500 | 5.6398 |
| 5.3501 | 0.58 | 1000 | 5.2066 |
| 5.0056 | 0.88 | 1500 | 4.9588 |
| 4.7258 | 1.17 | 2000 | 4.8173 |
| 4.5734 | 1.46 | 2500 | 4.6948 |
| 4.4663 | 1.75 | 3000 | 4.5804 |
| 4.3402 | 2.05 | 3500 | 4.5071 |
| 4.1471 | 2.34 | 4000 | 4.4576 |
| 4.1137 | 2.63 | 4500 | 4.4027 |
| 4.0777 | 2.92 | 5000 | 4.3468 |
| 3.8629 | 3.22 | 5500 | 4.3449 |
| 3.8078 | 3.51 | 6000 | 4.3108 |
| 3.8044 | 3.8 | 6500 | 4.2763 |
| 3.7029 | 4.09 | 7000 | 4.2803 |
| 3.5324 | 4.39 | 7500 | 4.2741 |
| 3.5239 | 4.68 | 8000 | 4.2585 |
| 3.5091 | 4.97 | 8500 | 4.2454 |
| 3.3521 | 5.26 | 9000 | 4.2592 |
| 3.3357 | 5.56 | 9500 | 4.2584 |
| 3.3348 | 5.85 | 10000 | 4.2573 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
nishchalprasad/lunar_lander_v2-PPO
|
nishchalprasad
| 2023-07-16T17:44:18Z | 4 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T17:43:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.46 +/- 24.94
name: mean_reward
verified: false
---
# **PPO-MLP** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MLP** 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
...
```
|
kanu03/my-cat
|
kanu03
| 2023-07-16T17:44:02Z | 107 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-16T17:39:19Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-cat Dreambooth model trained by kanu03 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: OPJU101
Sample pictures of this concept:
|
Za88yes/Afriana
|
Za88yes
| 2023-07-16T17:43:07Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-16T17:41:00Z |
---
license: bigscience-openrail-m
---
|
quangnguyennn/pokemon-lora-xformer
|
quangnguyennn
| 2023-07-16T17:29:24Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-16T13:08:13Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - quangnguyennn/pokemon-lora-xformer
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
ailabturkiye/thecihan
|
ailabturkiye
| 2023-07-16T17:29:03Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T17:24:11Z |
---
license: openrail
language:
- tr
tags:
- music
---
|
odunola/transcriber-t5-v8-new
|
odunola
| 2023-07-16T17:23:29Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-16T16:37:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: transcriber-t5-v8-new
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. -->
# transcriber-t5-v8-new
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0818
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1008 | 0.72 | 500 | 0.1306 |
| 0.069 | 1.43 | 1000 | 0.1227 |
| 0.1052 | 2.15 | 1500 | 0.1209 |
| 0.1017 | 2.86 | 2000 | 0.0992 |
| 0.0828 | 3.58 | 2500 | 0.0919 |
| 0.0471 | 4.29 | 3000 | 0.0927 |
| 0.0769 | 5.01 | 3500 | 0.0849 |
| 0.0732 | 5.72 | 4000 | 0.0862 |
| 0.0801 | 6.44 | 4500 | 0.0857 |
| 0.0428 | 7.15 | 5000 | 0.0815 |
| 0.1119 | 7.87 | 5500 | 0.0790 |
| 0.0692 | 8.58 | 6000 | 0.0780 |
| 0.0684 | 9.3 | 6500 | 0.0818 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
magicsword/wy-mt-en-zh-3
|
magicsword
| 2023-07-16T17:21:53Z | 111 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"autotrain",
"translation",
"unk",
"dataset:magicsword/autotrain-data-wy-mt-en-zh",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-16T15:15:50Z |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- magicsword/autotrain-data-wy-mt-en-zh
co2_eq_emissions:
emissions: 61.92129308371724
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 74981139784
- CO2 Emissions (in grams): 61.9213
## Validation Metrics
- Loss: 2.222
- SacreBLEU: 12.575
- Gen len: 16.299
|
DanGalt/speecht5_finetuned_voxpopuli_fi
|
DanGalt
| 2023-07-16T17:11:18Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"fi",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-07-16T17:07:04Z |
---
language:
- fi
license: mit
tags:
- generated_from_trainer
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_fi
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. -->
# speecht5_finetuned_voxpopuli_fi
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4436
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.504 | 5.05 | 250 | 0.4645 |
| 0.4882 | 10.1 | 500 | 0.4499 |
| 0.467 | 15.15 | 750 | 0.4450 |
| 0.4651 | 20.2 | 1000 | 0.4436 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s55555_v3_manual
|
KingKazma
| 2023-07-16T17:02:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T17:02:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
gioca91/ppo-Huggy
|
gioca91
| 2023-07-16T17:00:31Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-16T17:00:21Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: gioca91/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ailabturkiye/azizyildirim
|
ailabturkiye
| 2023-07-16T16:47:56Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T16:37:16Z |
---
license: openrail
language:
- tr
tags:
- music
---
|
iworeushankaonce/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
|
iworeushankaonce
| 2023-07-16T16:35:53Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-16T15:19:49Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3882
- Accuracy: 0.9
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4932 | 1.0 | 112 | 0.5325 | 0.86 |
| 0.3541 | 2.0 | 225 | 0.6068 | 0.77 |
| 0.5743 | 3.0 | 337 | 0.6356 | 0.83 |
| 0.6256 | 4.0 | 450 | 0.4878 | 0.86 |
| 0.0619 | 5.0 | 562 | 0.4262 | 0.88 |
| 0.0044 | 6.0 | 675 | 0.3266 | 0.91 |
| 0.0018 | 7.0 | 787 | 0.4827 | 0.87 |
| 0.001 | 8.0 | 900 | 0.9245 | 0.82 |
| 0.1854 | 9.0 | 1012 | 0.4256 | 0.89 |
| 0.0001 | 10.0 | 1125 | 0.3898 | 0.9 |
| 0.0001 | 11.0 | 1237 | 0.3873 | 0.9 |
| 0.0001 | 12.0 | 1350 | 0.4064 | 0.91 |
| 0.0 | 13.0 | 1462 | 0.3910 | 0.9 |
| 0.0 | 14.0 | 1575 | 0.3924 | 0.9 |
| 0.0001 | 15.0 | 1687 | 0.3917 | 0.91 |
| 0.0 | 16.0 | 1800 | 0.3903 | 0.9 |
| 0.0 | 17.0 | 1912 | 0.3900 | 0.89 |
| 0.0 | 18.0 | 2025 | 0.3894 | 0.89 |
| 0.0 | 19.0 | 2137 | 0.3886 | 0.9 |
| 0.0 | 19.91 | 2240 | 0.3882 | 0.9 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ailabturkiye/ruhicenet
|
ailabturkiye
| 2023-07-16T16:33:36Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T16:23:27Z |
---
license: openrail
language:
- tr
tags:
- music
---
Ruhi Çenet'in "Kanunun Olmadığı Bir Şehirde 48 SAAT Geçirmek: Slab City" ve "Bu şehirdeki herkes neden aynı binada yaşıyor? Dünyanın En Tuhaf Şehri: Whittier/Alaska" videosuyla dakikalık dataset yaptığım model.
|
WasuratS/whisper-tiny-en-finetune-minds14
|
WasuratS
| 2023-07-16T16:33:30Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-16T13:49:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en-finetune-minds14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3382526564344746
---
<!-- 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. -->
# whisper-tiny-en-finetune-minds14
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Wer Ortho: 0.3399
- Wer: 0.3383
## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.3136 | 3.57 | 100 | 0.4883 | 0.3640 | 0.3524 |
| 0.0417 | 7.14 | 200 | 0.5146 | 0.3560 | 0.3442 |
| 0.0066 | 10.71 | 300 | 0.5736 | 0.3411 | 0.3353 |
| 0.0017 | 14.29 | 400 | 0.6040 | 0.3455 | 0.3418 |
| 0.0013 | 17.86 | 500 | 0.6226 | 0.3393 | 0.3365 |
| 0.0009 | 21.43 | 600 | 0.6352 | 0.3393 | 0.3365 |
| 0.0007 | 25.0 | 700 | 0.6436 | 0.3399 | 0.3371 |
| 0.0006 | 28.57 | 800 | 0.6492 | 0.3399 | 0.3383 |
| 0.0006 | 32.14 | 900 | 0.6530 | 0.3399 | 0.3383 |
| 0.0006 | 35.71 | 1000 | 0.6541 | 0.3399 | 0.3383 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Mehmetakif/Astra
|
Mehmetakif
| 2023-07-16T16:30:37Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:45:50Z |
---
license: openrail
language:
- tr
tags:
- music
---
|
cassandraqs/shan_homework1
|
cassandraqs
| 2023-07-16T16:29:28Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T16:29:22Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
casque/LactationV.1.1
|
casque
| 2023-07-16T16:25:30Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T16:23:40Z |
---
license: creativeml-openrail-m
---
|
localmodels/LLaMA-65B-ggml
|
localmodels
| 2023-07-16T16:22:41Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-07-16T16:22:41Z |
---
duplicated_from: localmodels/LLM
---
# LLaMA 65B ggml
From Meta: https://ai.meta.com/blog/large-language-model-llama-meta-ai
---
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48.
### k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387.
---
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| llama-65b.ggmlv3.q2_K.bin | q2_K | 2 | 27.33 GB| 29.83 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| llama-65b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 34.55 GB| 37.05 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| llama-65b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 31.40 GB| 33.90 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| llama-65b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 28.06 GB| 30.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| llama-65b.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB| 39.23 GB | Original quant method, 4-bit. |
| llama-65b.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB| 43.31 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| llama-65b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 39.28 GB| 41.78 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| llama-65b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 36.73 GB| 39.23 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| llama-65b.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB| 47.39 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| llama-65b.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB| 51.47 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| llama-65b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.20 GB| 48.70 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| llama-65b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.89 GB| 47.39 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| llama-65b.ggmlv3.q6_K.bin | q6_K |6 | 53.56 GB| 56.06 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| llama-65b.ggmlv3.q8_0.bin | q8_0 | 8 | 69.370 GB | 71.87 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
|
ailabturkiye/KadirMisiroglu
|
ailabturkiye
| 2023-07-16T16:17:02Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T16:13:31Z |
---
license: openrail
language:
- tr
tags:
- music
---
Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
|
casque/Ultimate_ahegao
|
casque
| 2023-07-16T16:16:47Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T16:14:24Z |
---
license: creativeml-openrail-m
---
|
ailabturkiye/Contra
|
ailabturkiye
| 2023-07-16T16:12:24Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-16T15:40:13Z |
---
license: openrail
---
[](discord.gg/ailab)
# Contra - RVC V2 300 Epoch
**Rapper Contra'nın ses modelidir,
Rvc V2 300 epoch olarak eğitilmiştir.**
_Dataset ve Train Benim Tarafımdan yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: barisdark0
- YouTube: Barış (https://www.youtube.com/@barisdark)
[](discord.gg/ailab)
|
ailabturkiye/AliErbas
|
ailabturkiye
| 2023-07-16T16:11:53Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T16:09:33Z |
---
license: openrail
language:
- tr
tags:
- music
---
Diyanet İşleri Başkanı Sayın Ali Erbaş. Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
|
casque/AfterSexMS
|
casque
| 2023-07-16T16:09:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T16:07:19Z |
---
license: creativeml-openrail-m
---
|
n0n1m/rvc-krosh
|
n0n1m
| 2023-07-16T16:08:15Z | 0 | 0 | null |
[
"audio-to-audio",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-07-15T17:45:37Z |
---
license: openrail
pipeline_tag: audio-to-audio
---
Just a model of Krash from Kikoriki/Gogoriki or Krosh from Smeshariki
|
ailabturkiye/deepturkishemre
|
ailabturkiye
| 2023-07-16T16:07:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T16:06:04Z |
---
license: openrail
language:
- tr
tags:
- music
deepturkishemre 500 epoch
|
tyavika/Bert-QA-Pytorch-FULL
|
tyavika
| 2023-07-16T16:05:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-28T02:19:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Bert-QA-Pytorch-FULL
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. -->
# Bert-QA-Pytorch-FULL
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1633 | 1.0 | 3290 | 1.0515 |
| 0.8061 | 2.0 | 6580 | 1.0593 |
| 0.533 | 3.0 | 9870 | 1.2154 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
casque/Creampie_v11
|
casque
| 2023-07-16T16:05:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T16:03:25Z |
---
license: creativeml-openrail-m
---
|
ailabturkiye/deepturkisherdi
|
ailabturkiye
| 2023-07-16T16:05:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T16:04:08Z |
---
license: openrail
language:
- tr
tags:
- music
deepturkisherdi 500 epoch
|
tyavika/lr1e5_bs16_layer1_Bert_CNN256LSTM128NoBid
|
tyavika
| 2023-07-16T16:04:21Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-11T10:51:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lr1e5_bs16_layer1_Bert_CNN256LSTM128NoBid
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. -->
# lr1e5_bs16_layer1_Bert_CNN256LSTM128NoBid
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2674 | 1.0 | 3290 | 1.1183 |
| 0.8735 | 2.0 | 6580 | 1.0579 |
| 0.6019 | 3.0 | 9870 | 1.1703 |
| 0.3919 | 4.0 | 13160 | 1.3453 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ailabturkiye/orkundk
|
ailabturkiye
| 2023-07-16T16:03:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T16:02:09Z |
---
license: openrail
language:
- tr
tags:
- music
Orkundk (500 Epoch)
|
tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid
|
tyavika
| 2023-07-16T16:02:27Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-12T15:52:35Z |
---
tags:
- generated_from_trainer
model-index:
- name: lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid
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. -->
# lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid
This model is a fine-tuned version of [tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid](https://huggingface.co/tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
casque/CheekBulgeFellatioMS
|
casque
| 2023-07-16T15:58:04Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T15:56:44Z |
---
license: creativeml-openrail-m
---
|
ailabturkiye/MehmetAliErbil
|
ailabturkiye
| 2023-07-16T15:57:00Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-07-16T15:23:06Z |
---
Lisans: openrail
**Sunucu ve oyuncu Mehmet Ali Erbil'in Türkçe sesidir,
Rvc V2 500 epoch olarak eğitilmiştir.**
_Dataset ve train jawbone0 tarafından yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: jawbone0
- YouTube: JawBone0 (https://www.youtube.com/@JawBone0)
[](discord.gg/ailab)
|
ailabturkiye/muratabigf
|
ailabturkiye
| 2023-07-16T15:56:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T15:54:53Z |
---
license: openrail
language:
- tr
tags:
- music
MuratAbiGF (600 Epoch)
|
NasimB/rarity-all-guten-2p5k-cbt-p5k-mixed
|
NasimB
| 2023-07-16T15:56:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T14:02:13Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: rarity-all-guten-2p5k-cbt-p5k-mixed
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. -->
# rarity-all-guten-2p5k-cbt-p5k-mixed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3206
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6916 | 0.29 | 500 | 5.6242 |
| 5.3287 | 0.59 | 1000 | 5.1956 |
| 4.9961 | 0.88 | 1500 | 4.9421 |
| 4.7198 | 1.17 | 2000 | 4.8015 |
| 4.5643 | 1.47 | 2500 | 4.6835 |
| 4.4523 | 1.76 | 3000 | 4.5745 |
| 4.3273 | 2.06 | 3500 | 4.4993 |
| 4.1372 | 2.35 | 4000 | 4.4498 |
| 4.1052 | 2.64 | 4500 | 4.3880 |
| 4.0721 | 2.94 | 5000 | 4.3409 |
| 3.8586 | 3.23 | 5500 | 4.3325 |
| 3.8079 | 3.52 | 6000 | 4.3061 |
| 3.7897 | 3.82 | 6500 | 4.2690 |
| 3.678 | 4.11 | 7000 | 4.2702 |
| 3.5266 | 4.4 | 7500 | 4.2641 |
| 3.5165 | 4.7 | 8000 | 4.2488 |
| 3.5069 | 4.99 | 8500 | 4.2361 |
| 3.3367 | 5.28 | 9000 | 4.2512 |
| 3.3295 | 5.58 | 9500 | 4.2494 |
| 3.3275 | 5.87 | 10000 | 4.2480 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
casque/licking_my_dick.sd.v1.2
|
casque
| 2023-07-16T15:51:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T15:48:28Z |
---
license: creativeml-openrail-m
---
|
ailabturkiye/baso
|
ailabturkiye
| 2023-07-16T15:50:27Z | 0 | 1 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:36:49Z |
---
license: openrail
language:
- tr
tags:
- music
---
Başo'nun "KAÇIRILAN YOUTUBER MARINA JOYCE'UN HİKAYESİ GERÇEK MİYDİ?" videosuyla yaptığım ses modeli.
|
localmodels/WizardCoder-15B-V1.0-GPTQ
|
localmodels
| 2023-07-16T15:44:39Z | 7 | 0 |
transformers
|
[
"transformers",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T15:44:39Z |
---
duplicated_from: localmodels/LLM
---
# WizardCoder 15B 1.0 GPTQ
From: https://huggingface.co/WizardLM/WizardCoder-15B-V1.0
---
## Prompt template
```
Below is an instruction that describes a task. Write a response that appropriately completes the request
### Instruction: prompt
### Response:
```
---
## Model
* gptq_model-4bit--1g.safetensors
* Works with AutoGPTQ in CUDA or Triton modes.
* Does not work with GPTQ-for-LLaMa.
* Parameters: Groupsize = -1. --act-order.
---
# WizardCoder: Empowering Code Large Language Models with Evol-Instruct
To develop our WizardCoder model, we begin by adapting the Evol-Instruct method specifically for coding tasks. This involves tailoring the prompt to the domain of code-related instructions. Subsequently, we fine-tune the Code LLM, StarCoder, utilizing the newly created instruction-following training set.
## News
- 🔥 Our **WizardCoder-15B-v1.0** model achieves the **57.3 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval), which is **22.3** points higher than the SOTA open-source Code LLMs.
- 🔥 We released **WizardCoder-15B-v1.0** trained with **78k** evolved code instructions. Please checkout the [Model Weights](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), and [Paper]().
- 📣 Please refer to our Twitter account https://twitter.com/WizardLM_AI and HuggingFace Repo https://huggingface.co/WizardLM . We will use them to announce any new release at the 1st time.
## Comparing WizardCoder with the Closed-Source Models.
🔥 The following figure shows that our **WizardCoder attains the third position in this benchmark**, surpassing Claude-Plus (59.8 vs. 53.0) and Bard (59.8 vs. 44.5). Notably, our model exhibits a substantially smaller size compared to these models.
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/pass1.png" alt="WizardCoder" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
</p>
❗**Note: In this study, we copy the scores for HumanEval and HumanEval+ from the [LLM-Humaneval-Benchmarks](https://github.com/my-other-github-account/llm-humaneval-benchmarks). Notably, all the mentioned models generate code solutions for each problem utilizing a **single attempt**, and the resulting pass rate percentage is reported. Our **WizardCoder** generates answers using greedy decoding and tests with the same [code](https://github.com/evalplus/evalplus).**
## Comparing WizardCoder with the Open-Source Models.
The following table clearly demonstrates that our **WizardCoder** exhibits a substantial performance advantage over all the open-source models. ❗**If you are confused with the different scores of our model (57.3 and 59.8), please check the Notes.**
| Model | HumanEval Pass@1 | MBPP Pass@1 |
|------------------|------------------|-------------|
| CodeGen-16B-Multi| 18.3 |20.9 |
| CodeGeeX | 22.9 |24.4 |
| LLaMA-33B | 21.7 |30.2 |
| LLaMA-65B | 23.7 |37.7 |
| PaLM-540B | 26.2 |36.8 |
| PaLM-Coder-540B | 36.0 |47.0 |
| PaLM 2-S | 37.6 |50.0 |
| CodeGen-16B-Mono | 29.3 |35.3 |
| Code-Cushman-001 | 33.5 |45.9 |
| StarCoder-15B | 33.6 |43.6* |
| InstructCodeT5+ | 35.0 |-- |
| WizardLM-30B 1.0| 37.8 |-- |
| WizardCoder-15B 1.0 | **57.3** |**51.8** |
❗**Note: The reproduced result of StarCoder on MBPP.**
❗**Note: The above table conducts a comprehensive comparison of our **WizardCoder** with other models on the HumanEval and MBPP benchmarks. We adhere to the approach outlined in previous studies by generating **20 samples** for each problem to estimate the pass@1 score and evaluate with the same [code](https://github.com/openai/human-eval/tree/master). The scores of GPT4 and GPT3.5 reported by [OpenAI](https://openai.com/research/gpt-4) are 67.0 and 48.1 (maybe these are the early version GPT4&3.5).**
## Call for Feedbacks
We welcome everyone to use your professional and difficult instructions to evaluate WizardCoder, and show us examples of poor performance and your suggestions in the [issue discussion](https://github.com/nlpxucan/WizardLM/issues) area. We are focusing on improving the Evol-Instruct now and hope to relieve existing weaknesses and issues in the the next version of WizardCoder. After that, we will open the code and pipeline of up-to-date Evol-Instruct algorithm and work with you together to improve it.
## Fine-tuning
We fine-tune WizardCoder using the modified code `train.py` from [Llama-X](https://github.com/AetherCortex/Llama-X).
We fine-tune StarCoder-15B with the following hyperparameters:
| Hyperparameter | StarCoder-15B |
|----------------|---------------|
| Batch size | 512 |
| Learning rate | 2e-5 |
| Epochs | 3 |
| Max length | 2048 |
| Warmup step | 30 |
| LR scheduler | cosine |
To reproduce our fine-tuning of WizardCoder, please follow the following steps:
1. According to the instructions of [Llama-X](https://github.com/AetherCortex/Llama-X), install the environment, download the training code, and deploy. (Note: `deepspeed==0.9.2` and `transformers==4.29.2`)
2. Replace the `train.py` with the `train_wizardcoder.py` in our repo (`src/train_wizardcoder.py`)
3. Login Huggingface:
```bash
huggingface-cli login
```
4. Execute the following training command:
```bash
deepspeed train_wizardcoder.py \
--model_name_or_path "bigcode/starcoder" \
--data_path "/your/path/to/code_instruction_data.json" \
--output_dir "/your/path/to/ckpt" \
--num_train_epochs 3 \
--model_max_length 2048 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50 \
--save_total_limit 2 \
--learning_rate 2e-5 \
--warmup_steps 30 \
--logging_steps 2 \
--lr_scheduler_type "cosine" \
--report_to "tensorboard" \
--gradient_checkpointing True \
--deepspeed configs/deepspeed_config.json \
--fp16 True
```
## Inference
We provide the decoding script for WizardCoder, which reads a input file and generates corresponding responses for each sample, and finally consolidates them into an output file.
You can specify `base_model`, `input_data_path` and `output_data_path` in `src\inference_wizardcoder.py` to set the decoding model, path of input file and path of output file.
```bash
pip install jsonlines
```
The decoding command is:
```
python src\inference_wizardcoder.py \
--base_model "/your/path/to/ckpt" \
--input_data_path "/your/path/to/input/data.jsonl" \
--output_data_path "/your/path/to/output/result.jsonl"
```
The format of `data.jsonl` should be:
```
{"idx": 11, "Instruction": "Write a Python code to count 1 to 10."}
{"idx": 12, "Instruction": "Write a Jave code to sum 1 to 10."}
```
The prompt for our WizardCoder in `src\inference_wizardcoder.py` is:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
## Evaluation
We provide the evaluation script on HumanEval for WizardCoder.
1. According to the instructions of [HumanEval](https://github.com/openai/human-eval), install the environment.
2. Run the following script to generate the answer.
```bash
model="/path/to/your/model"
temp=0.2
max_len=2048
pred_num=200
num_seqs_per_iter=2
output_path=preds/T${temp}_N${pred_num}
mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model
# 164 problems, 21 per GPU if GPU=8
index=0
gpu_num=8
for ((i = 0; i < $gpu_num; i++)); do
start_index=$((i * 21))
end_index=$(((i + 1) * 21))
gpu=$((i))
echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
((index++))
(
CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \
--start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
--num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path}
) &
if (($index % $gpu_num == 0)); then wait; fi
done
```
3. Run the post processing code `src/process_humaneval.py` to collect the code completions from all answer files.
```bash
output_path=preds/T${temp}_N${pred_num}
echo 'Output path: '$output_path
python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt
evaluate_functional_correctness ${output_path}.jsonl
```
## Citation
Please cite the repo if you use the data or code in this repo.
```
@misc{luo2023wizardcoder,
title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
year={2023},
}
```
## Disclaimer
The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
|
Mi-ya/lumine1.5
|
Mi-ya
| 2023-07-16T15:44:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-16T15:24:12Z |
原神の蛍のlocon。花飾りの向きが逆になったり、衣装も細部が崩れるのはご愛敬ということで。
各種トレーニングパラメータが知りたいなら、webuiのadditional networkから見てくれ。
生成した画像も貼ってあるのでぜひ。
This is a model trained on the character "Lumine" from Genshin Impact.
It's possible that the floral decorations might have a reversed orientation or that there could be minor flaws in the costumes.
If you want to know about various training parameters, please check them on the additional network of the web UI.
|
ailabturkiye/DevletBahceli
|
ailabturkiye
| 2023-07-16T15:42:33Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:36:37Z |
---
license: openrail
language:
- tr
tags:
- music
---
Devlet Bahçeli.Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
|
ByteExplorer/dqn-SpaceInvadersNoFrameskip-v4
|
ByteExplorer
| 2023-07-16T15:35:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T15:34:07Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 652.50 +/- 292.48
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ByteExplorer -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ByteExplorer -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ByteExplorer
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
PJ02/ppo-LunarLander-v2
|
PJ02
| 2023-07-16T15:34:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T15:33:44Z |
---
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: 222.29 +/- 46.53
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
...
```
|
lxyuan/distilbart-finetuned-summarization
|
lxyuan
| 2023-07-16T15:32:42Z | 159 | 5 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"distilbart",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"dataset:samsum",
"dataset:ccdv/pubmed-summarization",
"arxiv:2010.13002",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-09T05:23:35Z |
---
tags:
- generated_from_trainer
- distilbart
model-index:
- name: distilbart-finetuned-summarization
results: []
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
- samsum
- ccdv/pubmed-summarization
language:
- en
metrics:
- rouge
---
<!-- 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. -->
# distilbart-finetuned-summarization
This model is a further fine-tuned version of [distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the the combination of 4 different summarisation datasets:
- [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail)
- [samsum](https://huggingface.co/datasets/samsum)
- [xsum](https://huggingface.co/datasets/xsum)
- [ccdv/pubmed-summarization](https://huggingface.co/datasets/ccdv/pubmed-summarization)
Please check out the offical model page and paper:
- [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)
- [Pre-trained Summarization Distillation](https://arxiv.org/abs/2010.13002)
## Training and evaluation data
One can reproduce the dataset using the following code:
```python
from datasets import DatasetDict, load_dataset
from datasets import concatenate_datasets
xsum_dataset = load_dataset("xsum")
pubmed_dataset = load_dataset("ccdv/pubmed-summarization").rename_column("article", "document").rename_column("abstract", "summary")
cnn_dataset = load_dataset("cnn_dailymail", '3.0.0').rename_column("article", "document").rename_column("highlights", "summary")
samsum_dataset = load_dataset("samsum").rename_column("dialogue", "document")
summary_train = concatenate_datasets([xsum_dataset["train"], pubmed_dataset["train"], cnn_dataset["train"], samsum_dataset["train"]])
summary_validation = concatenate_datasets([xsum_dataset["validation"], pubmed_dataset["validation"], cnn_dataset["validation"], samsum_dataset["validation"]])
summary_test = concatenate_datasets([xsum_dataset["test"], pubmed_dataset["test"], cnn_dataset["test"], samsum_dataset["test"]])
raw_datasets = DatasetDict()
raw_datasets["train"] = summary_train
raw_datasets["validation"] = summary_validation
raw_datasets["test"] = summary_test
```
## Inference example
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", model="lxyuan/distilbart-finetuned-summarization")
text = """SINGAPORE: The Singapore Police Force on Sunday (Jul 16) issued a warning over a
fake SMS impersonating as its "anti-scam centre (ASC)".
"In this scam variant, members of the public would receive a scam SMS from 'ASC',
requesting them to download and install an “anti-scam” app to ensure the security
of their devices," said the police.
"The fake SMS would direct members of the public to a URL link leading to an
Android Package Kit (APK) file, an application created for Android’s operating
system purportedly from 'ASC'."
The fake website has an icon to download the “anti-scam” app and once downloaded,
Android users are asked to allow accessibility services to enable the service.
While the fake app purportedly claims to help identify and prevent scams by
providing comprehensive protection and security, downloading it may enable
scammers to gain remote access to devices.
"Members of the public are advised not to download any suspicious APK files
on their devices as they may contain malware which will allow scammers to
access and take control of the device remotely as well as to steal passwords
stored in the device," said the police.
Members of the public are advised to adopt the following precautionary measures,
including adding anti-virus or anti-malware apps to their devices. They should
also disable “install unknown app” or “unknown sources” in their phone settings.
Users should check the developer information on the app listing as well as the
number of downloads and user reviews to ensure it is a reputable and legitimate
app, the police said.
Any fraudulent transactions should be immediately reported to the banks.
"""
pipe(text)
>>>"""The Singapore Police Force has issued a warning over a fake SMS
impersonating as its "anti-scam centre" that asks members of the public
to download an Android app to ensure the security of their devices, the
force said on Sunday. The fake SMS would direct people to a URL link
leading to an Android Package Kit (APK) file, an application created
for Android’s operating system purportedly from "ASC".
"""
```
## Training procedure
Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distilbart-finetune-summarisation.ipynb)
### Training hyperparameters
The following hyperparameters were used during training:
- evaluation_strategy="epoch",
- save_strategy="epoch",
- logging_strategy="epoch",
- learning_rate=2e-5,
- per_device_train_batch_size=2,
- per_device_eval_batch_size=2,
- gradient_accumulation_steps=64,
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- weight_decay=0.01,
- save_total_limit=2,
- num_train_epochs=4,
- predict_with_generate=True,
- fp16=True,
- push_to_hub=True
### Training results
_Training is still in progress_
| Epoch | Training Loss | Validation Loss | Rouge1 | Rouge2 | RougeL | RougeLsum | Gen Len |
|-------|---------------|-----------------|--------|--------|--------|-----------|---------|
| 0 | 1.779700 | 1.719054 | 40.003900 | 17.907100 | 27.882500 | 34.888600 | 88.893600 |
| 1 | 1.633800 | 1.710876 | 40.628800 | 18.470200 | 28.428100 | 35.577500 | 88.885000 |
| 2 | 1.566100 | 1.694476 | 40.928500 | 18.695300 | 28.613300 | 35.813300 | 88.993700 |
| 3 | 1.515700 | 1.691141 | 40.860500 | 18.696500 | 28.672700 | 35.734600 | 88.457300 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
manuu01/taxi_first_implementation
|
manuu01
| 2023-07-16T15:32:02Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T15:01:51Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_first_implementation
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="manuu01/taxi_first_implementation", 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"])
```
|
ailabturkiye/CavsKarahanli
|
ailabturkiye
| 2023-07-16T15:31:16Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:27:29Z |
---
license: openrail
language:
- tr
tags:
- music
---
Yayıncı Cavs Karahanli.Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
|
gioca91/ppo-LunarLander-v2
|
gioca91
| 2023-07-16T15:21:24Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T15:20:46Z |
---
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: 267.75 +/- 28.15
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
...
```
|
ailabturkiye/OnurNaciOzturkler
|
ailabturkiye
| 2023-07-16T15:19:53Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:16:32Z |
---
license: openrail
language:
- tr
tags:
- music
---
[](discord.gg/ailab)
# Onur Naci Öztürkler - RVC V2 425 Epoch
**YouTuber Onur Naci Öztürkler`in ses modelidir,
Rvc V2 420 epoch olarak eğitilmiştir.**
_Dataset ve Train Benim Tarafımdan yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: TLLH
- Reddit: u/TLLHu
- YouTube: AiVerseC (https://www.youtube.com/@AiVerseC)
[](discord.gg/ailab)
|
ailabturkiye/Porcay
|
ailabturkiye
| 2023-07-16T15:13:40Z | 0 | 0 | null |
[
"music",
"tr",
"license:openrail",
"region:us"
] | null | 2023-07-16T15:08:01Z |
---
license: openrail
language:
- tr
tags:
- music
---
[](discord.gg/ailab)
# Porçay - RVC V2 300 Epoch
**YouTuber Porçay`ın ses modelidir,
Rvc V2 300 epoch olarak eğitilmiştir.**
_Dataset ve Train Benim Tarafımdan yapılmıştır.._
__Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__
## Credits
**Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.**
- Discord: TLLH
- Reddit: u/TLLHu
- YouTube: AiVerseC (https://www.youtube.com/@AiVerseC)
[](discord.gg/ailab)
|
eddyyeo/dqn-SpaceInvadersNoFrameskip-v
|
eddyyeo
| 2023-07-16T15:05:32Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T15:04:48Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 611.50 +/- 127.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eddyyeo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eddyyeo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga eddyyeo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
TommasoBendinelli/ControllableNeuralSymbolicRegressionWeights
|
TommasoBendinelli
| 2023-07-16T15:03:12Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-07-16T14:45:22Z |
---
license: mit
---
Weights for demo of the paper "Controllable Neural Symbolic Regression": https://github.com/SymposiumOrganization/ControllableNeuralSymbolicRegression
|
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v3
|
hafidikhsan
| 2023-07-16T15:00:06Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-16T14:58:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v3
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0729
- Accuracy: 0.774
- F1: 0.7738
- Precision: 0.7764
- Recall: 0.774
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8144 | 1.0 | 500 | 0.8235 | 0.598 | 0.5550 | 0.5856 | 0.598 |
| 0.8264 | 2.0 | 1000 | 0.6951 | 0.682 | 0.6716 | 0.6805 | 0.682 |
| 0.5219 | 3.0 | 1500 | 0.7580 | 0.742 | 0.7384 | 0.7395 | 0.742 |
| 0.2354 | 4.0 | 2000 | 1.0238 | 0.75 | 0.7443 | 0.7453 | 0.75 |
| 0.1291 | 5.0 | 2500 | 1.0922 | 0.762 | 0.7598 | 0.7604 | 0.762 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
manuu01/q-FrozenLake-v1-4x4-noSlippery
|
manuu01
| 2023-07-16T14:57:47Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T14:57:25Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="manuu01/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
Jonathaniu/alpaca-breast-cancer-13b-mix_data_2
|
Jonathaniu
| 2023-07-16T14:52:46Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T14:52:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
### Framework versions
- PEFT 0.4.0.dev0
|
rushi777/falcon-7b-qlora-chat-support-test-1
|
rushi777
| 2023-07-16T14:42:29Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T14:01:51Z |
---
library_name: peft
---
## 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
Saideva/title_generation
|
Saideva
| 2023-07-16T14:38:55Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-16T14:10:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: title_generation
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. -->
# title_generation
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 41.5236
- Rouge2: 17.5894
- Rougel: 37.2852
- Rougelsum: 37.2749
- Gen Len: 13.3542
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0 | 1.0 | 3748 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 |
| 0.0 | 2.0 | 7496 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 |
| 0.0 | 3.0 | 11244 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
margosabry/food_classifier
|
margosabry
| 2023-07-16T14:28:12Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-16T13:50:22Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: margosabry/food_classifier
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. -->
# margosabry/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3853
- Validation Loss: 0.3150
- Train Accuracy: 0.928
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8055 | 1.6705 | 0.808 | 0 |
| 1.2418 | 0.8233 | 0.883 | 1 |
| 0.7004 | 0.5248 | 0.912 | 2 |
| 0.5037 | 0.3802 | 0.926 | 3 |
| 0.3853 | 0.3150 | 0.928 | 4 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
SwampMan/a2c-PandaReachDense-v2
|
SwampMan
| 2023-07-16T14:22:23Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T14:19:26Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.61 +/- 0.69
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
atiiisham988/speecht5_finetuned_voxpopuli_nl
|
atiiisham988
| 2023-07-16T14:16:09Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-16T05:30:30Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4763
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5583 | 8.61 | 1000 | 0.4978 |
| 0.5238 | 17.22 | 2000 | 0.4833 |
| 0.5075 | 25.83 | 3000 | 0.4763 |
| 0.5026 | 34.45 | 4000 | 0.4763 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
huggingFacing/ddpm-butterflies-128
|
huggingFacing
| 2023-07-16T14:11:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"en",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2023-07-16T14:09:03Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: /content/drive/MyDrive/image_and_text
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `/content/drive/MyDrive/image_and_text` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Tian7/ddpm-butterflies-128/tensorboard?#scalars)
|
olegs/distil-ast-audioset-finetuned-gtzan
|
olegs
| 2023-07-16T14:09:35Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:bookbot/distil-ast-audioset",
"base_model:finetune:bookbot/distil-ast-audioset",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-16T13:02:16Z |
---
license: apache-2.0
base_model: bookbot/distil-ast-audioset
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distil-ast-audioset-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.93
---
<!-- 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. -->
# distil-ast-audioset-finetuned-gtzan
This model is a fine-tuned version of [bookbot/distil-ast-audioset](https://huggingface.co/bookbot/distil-ast-audioset) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5022
- Accuracy: 0.93
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8727 | 1.0 | 113 | 0.6650 | 0.81 |
| 0.6665 | 2.0 | 226 | 0.7639 | 0.74 |
| 0.5306 | 3.0 | 339 | 0.6683 | 0.76 |
| 0.2793 | 4.0 | 452 | 0.7423 | 0.82 |
| 0.0867 | 5.0 | 565 | 0.6301 | 0.85 |
| 0.0156 | 6.0 | 678 | 0.8905 | 0.83 |
| 0.2298 | 7.0 | 791 | 0.4492 | 0.92 |
| 0.0073 | 8.0 | 904 | 0.9028 | 0.83 |
| 0.0664 | 9.0 | 1017 | 0.6387 | 0.85 |
| 0.0001 | 10.0 | 1130 | 0.5022 | 0.87 |
| 0.0001 | 11.0 | 1243 | 0.4047 | 0.91 |
| 0.0 | 12.0 | 1356 | 0.3988 | 0.92 |
| 0.0 | 13.0 | 1469 | 0.6225 | 0.91 |
| 0.0 | 14.0 | 1582 | 0.6075 | 0.86 |
| 0.0 | 15.0 | 1695 | 0.5259 | 0.89 |
| 0.0 | 16.0 | 1808 | 0.5014 | 0.92 |
| 0.0 | 17.0 | 1921 | 0.5004 | 0.93 |
| 0.0 | 18.0 | 2034 | 0.5008 | 0.93 |
| 0.0 | 19.0 | 2147 | 0.5022 | 0.93 |
| 0.0 | 20.0 | 2260 | 0.5022 | 0.93 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
lucasbertola/ppo-SnowballTarget
|
lucasbertola
| 2023-07-16T14:08:18Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-07-16T14:08:12Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: lucasbertola/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
arham061/speecht5_finetuned_voxpopuli_nl
|
arham061
| 2023-07-16T14:04:23Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-14T07:08:15Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [arham061/speecht5_finetuned_voxpopuli_nl](https://huggingface.co/arham061/speecht5_finetuned_voxpopuli_nl) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5508
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5058 | 7.74 | 1000 | 0.5431 |
| 0.4938 | 15.49 | 2000 | 0.5487 |
| 0.4909 | 23.23 | 3000 | 0.5508 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
antoniodee/fin-tench
|
antoniodee
| 2023-07-16T13:48:41Z | 0 | 0 | null |
[
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-16T13:43:49Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### fin_tench Dreambooth model trained by antoniodee with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
NasimB/all-base-log-rarity-all-iorder-6p6k-mostf
|
NasimB
| 2023-07-16T13:35:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T11:47:28Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: all-base-log-rarity-all-iorder-6p6k-mostf
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. -->
# all-base-log-rarity-all-iorder-6p6k-mostf
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3355
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.7617 | 0.31 | 500 | 5.6560 |
| 5.4151 | 0.63 | 1000 | 5.2202 |
| 5.053 | 0.94 | 1500 | 4.9736 |
| 4.7741 | 1.25 | 2000 | 4.8215 |
| 4.6209 | 1.56 | 2500 | 4.6901 |
| 4.5193 | 1.88 | 3000 | 4.5791 |
| 4.3095 | 2.19 | 3500 | 4.5212 |
| 4.2051 | 2.5 | 4000 | 4.4594 |
| 4.1681 | 2.82 | 4500 | 4.3972 |
| 4.0255 | 3.13 | 5000 | 4.3774 |
| 3.8818 | 3.44 | 5500 | 4.3445 |
| 3.8727 | 3.75 | 6000 | 4.3065 |
| 3.794 | 4.07 | 6500 | 4.3009 |
| 3.5892 | 4.38 | 7000 | 4.2905 |
| 3.5866 | 4.69 | 7500 | 4.2762 |
| 3.5698 | 5.01 | 8000 | 4.2626 |
| 3.3916 | 5.32 | 8500 | 4.2744 |
| 3.393 | 5.63 | 9000 | 4.2730 |
| 3.3874 | 5.94 | 9500 | 4.2723 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
AnandSingh/Wizard-Vicuna-13B-Uncensored-HF_QnA
|
AnandSingh
| 2023-07-16T13:29:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T13:28:53Z |
---
library_name: peft
---
## 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 0.4.0.dev0
|
zfz/Cuteyukimix
|
zfz
| 2023-07-16T13:27:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-29T12:17:48Z |
https://civitai.com/user/newlifezfztty761/models
My personal space on civitai.com
|
WALIDALI/lyrieldiff
|
WALIDALI
| 2023-07-16T12:55:45Z | 2 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-16T12:50:56Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### LyrielDiff Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
asedmammad/Vicuna-7B-vanilla-1.1-GGML
|
asedmammad
| 2023-07-16T12:50:47Z | 0 | 1 | null |
[
"llama",
"vicuna",
"text-generation-inference",
"region:us"
] | null | 2023-07-16T09:47:34Z |
---
inference: false
tags:
- llama
- vicuna
- text-generation-inference
---
# Ejafa's Vicuna Vanilla 1.1 7B GGML
These files are GGML format model files for [Ejafa's Vicuna Vanilla 1.1 7B](https://huggingface.co/Ejafa/vicuna_7B_vanilla_1.1).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 8 -ngl 32 -m vicuna_7B_vanilla_1.1.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "prompt goes here"
```
Change `-t 8` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## Compatibility
I have uploded bothe the original llama.cpp quant methods (`q4_0, q4_1, q5_0, q5_1, q8_0`) as well as the new k-quant methods (`q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`).
Please refer to [llama.cpp](https://github.com/ggerganov/llama.cpp) and [TheBloke](https://huggingface.co/TheBloke)'s GGML models for further explanation.
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Thanks
Thanks to [TheBloke](https://huggingface.co/TheBloke) for inspiration and providing almost all of the readme here!
Thanks to [Ejafa](https://huggingface.co/Ejafa) for providing checkpoints of the model.
Thanks to [Georgi Gerganov](https://github.com/ggerganov) and all of the awesome people in the AI community.
|
larry-jiang/RL
|
larry-jiang
| 2023-07-16T12:48:55Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T12:47:54Z |
---
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: 256.32 +/- 20.65
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
...
```
|
headflame02/AchaxV5
|
headflame02
| 2023-07-16T12:37:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-16T12:37:16Z |
---
license: creativeml-openrail-m
---
|
Rihong/ppo-LunarLander-v2
|
Rihong
| 2023-07-16T12:20:44Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T12:19:16Z |
---
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: 272.93 +/- 18.31
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
...
```
|
joserodr68/Reinforce-cartpole
|
joserodr68
| 2023-07-16T12:12:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-16T12:11:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
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
|
sjdata/speecht5_finetuned_single_speaker_en_test_librivox
|
sjdata
| 2023-07-16T12:09:19Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"en",
"dataset:speecht5_finetuned_single_speaker_en_test_librivox",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-13T12:31:39Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- speecht5_finetuned_single_speaker_en_test_librivox
model-index:
- name: SpeechT5 Single Speaker test
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. -->
# SpeechT5 Single Speaker test
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the single_speaker_en_test_librivox dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4215
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4809 | 1.78 | 1000 | 0.4215 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
indiaLLMs/dolly-llama-3b
|
indiaLLMs
| 2023-07-16T11:42:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-16T11:42:19Z |
---
library_name: peft
---
## 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 0.4.0.dev0
|
chrishoertnagl/dolly-v2-3b-chris
|
chrishoertnagl
| 2023-07-16T11:20:19Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-15T10:45:38Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
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