modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PisoF/ppo-LunarLander-v2 | 2023-07-12T13:53:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PisoF | null | null | PisoF/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T13:53:19 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.80 +/- 20.96
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Mateo2008/ppo-LunarLander-v2 | 2023-07-12T13:54:02.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Mateo2008 | null | null | Mateo2008/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T13:53:38 | ---
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: 239.21 +/- 22.06
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
...
```
| 784 | [
[
-0.00022685527801513672,
-0.0271148681640625,
0.0170745849609375,
0.0233612060546875,
-0.0060577392578125,
0.00274658203125,
0.034454345703125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
... |
MUNDOJU/ppo-LunarLander-v2 | 2023-07-12T13:55:46.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | MUNDOJU | null | null | MUNDOJU/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T13:55:27 | ---
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: 251.13 +/- 13.96
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Jsjo/ppo-LunarLander-v2 | 2023-07-12T13:57:15.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Jsjo | null | null | Jsjo/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T13:56:56 | ---
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: 246.64 +/- 45.69
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
malditoguisante/ppo-LunarLander-v2 | 2023-07-12T14:04:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | malditoguisante | null | null | malditoguisante/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T14:03:55 | ---
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: 272.05 +/- 16.08
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
FlandersMakeAGV/whisper-small-keyword-spotting | 2023-09-22T07:49:00.000Z | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | FlandersMakeAGV | null | null | FlandersMakeAGV/whisper-small-keyword-spotting | 0 | 2 | transformers | 2023-07-12T15:06:07 | ---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
base_model: openai/whisper-small
model-index:
- name: whisper-small-keyword-spotting
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. -->
# whisper-small-keyword-spotting
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the kw-spotting-fsc-sl-agv dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Accuracy: 0.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0268 | 1.0 | 318 | 0.0720 | 0.9685 |
| 0.0195 | 2.0 | 637 | 0.0183 | 0.9998 |
| 0.0111 | 3.0 | 956 | 0.2009 | 0.9168 |
| 0.0065 | 4.0 | 1275 | 0.2847 | 0.8544 |
| 0.0086 | 4.99 | 1590 | 0.1895 | 0.9168 |
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
| 1,815 | [
[
-0.03436279296875,
-0.033843994140625,
0.0159912109375,
-0.002216339111328125,
-0.0361328125,
-0.031524658203125,
-0.00909423828125,
-0.02716064453125,
0.0135345458984375,
0.0134124755859375,
-0.053741455078125,
-0.04803466796875,
-0.055511474609375,
-0.0213... |
TimeThief2895/ppo-LunarLander-v2 | 2023-07-12T17:15:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | TimeThief2895 | null | null | TimeThief2895/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T17:15:20 | ---
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: 271.41 +/- 17.91
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
...
```
| 784 | [
[
-0.0002090930938720703,
-0.027130126953125,
0.017059326171875,
0.0233306884765625,
-0.00606536865234375,
0.002750396728515625,
0.034454345703125,
-0.01213836669921875,
0.0198516845703125,
0.06500244140625,
-0.04315185546875,
-0.03521728515625,
-0.034271240234375... |
gamallo/gpt-galego1.3B | 2023-07-12T21:15:46.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | gamallo | null | null | gamallo/gpt-galego1.3B | 0 | 2 | transformers | 2023-07-12T17:22:05 | ---
widget:
- text: "Como se fan as filloas?"
example_title: "Filloas"
- text: "Feijóo dixo"
example_title: "Feijóo"
- text: "Francisco Franco foi un dictador de"
example_title: "Franco"
- text: "Quem foi Rosalía de Castro?"
example_title: "Rosalia"
- text: "Xosé Manuel Beiras dixo que"
example_title: "Beiras"
---
---
license: gpl-3.0
---
| 352 | [
[
-0.0288238525390625,
-0.0262603759765625,
0.0421142578125,
0.0609130859375,
-0.05877685546875,
-0.0201568603515625,
0.019012451171875,
-0.027069091796875,
-0.0177459716796875,
0.06561279296875,
-0.0197906494140625,
-0.039215087890625,
-0.052581787109375,
-0.... |
4bit/WizardLM-13B-V1.1-GPTQ | 2023-07-12T19:06:41.000Z | [
"transformers",
"llama",
"text-generation",
"arxiv:2304.12244",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | 4bit | null | null | 4bit/WizardLM-13B-V1.1-GPTQ | 3 | 2 | transformers | 2023-07-12T19:03:12 | ---
inference: false
license: other
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# WizardLM's WizardLM 13B V1.1 GPTQ
These files are GPTQ model files for [WizardLM's WizardLM 13B V1.1](https://huggingface.co/WizardLM/WizardLM-13B-V1.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)
## Prompt template: Vicuna
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: {prompt}
ASSISTANT:
```
## Provided files
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| main | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
| gptq-4bit-32g-actorder_True | 4 | 32 | True | 8.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
| gptq-4bit-64g-actorder_True | 4 | 64 | True | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| gptq-4bit-128g-actorder_True | 4 | 128 | True | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order androup size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| gptq-8bit--1g-actorder_True | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
| gptq-8bit-128g-actorder_False | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ`
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/WizardLM-13B-V1.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done"
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `WizardLM-13B-V1.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
## How to use this GPTQ model from Python code
First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
`GITHUB_ACTIONS=true pip install auto-gptq`
Then try the following example code:
```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
model_name_or_path = "TheBloke/WizardLM-13B-V1.1-GPTQ"
model_basename = "wizardlm-13b-v1.1-GPTQ-4bit-128g.no-act.order"
use_triton = False
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename=model_basename
use_safetensors=True,
trust_remote_code=True,
device="cuda:0",
use_triton=use_triton,
quantize_config=None)
"""
To download from a specific branch, use the revision parameter, as in this example:
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
revision="gptq-4bit-32g-actorder_True",
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=True,
device="cuda:0",
quantize_config=None)
"""
prompt = "Tell me about AI"
prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: {prompt}
ASSISTANT:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
print(pipe(prompt_template)[0]['generated_text'])
```
## Compatibility
The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: WizardLM's WizardLM 13B V1.1
This is the **Full-Weight** of WizardLM-13B V1.1 model.
**Repository**: https://github.com/nlpxucan/WizardLM
**Twitter**: https://twitter.com/WizardLM_AI/status/1677282955490918401
- 🔥🔥🔥 [7/7/2023] We released **WizardLM V1.1** models. The **WizardLM-13B-V1.1** is here ([Demo_13B-V1.1](https://e8a06366ccd1c4d1.gradio.app), [Demo_13B-V1.1_bak-1](https://59da107262a25764.gradio.app), [Demo_13B-V1.1_bak-2](https://dfc5113f66739c80.gradio.app), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)). **WizardLM-7B-V1.1**, **WizardLM-30B-V1.1**, and **WizardLM-65B-V1.1** are coming soon. Please checkout the [Full Model Weights](https://huggingface.co/WizardLM) and [paper](https://arxiv.org/abs/2304.12244).
- 🔥🔥🔥 [7/7/2023] The **WizardLM-13B-V1.1** achieves **6.74** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **86.32%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **99.3%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
| 10,998 | [
[
-0.037811279296875,
-0.07177734375,
0.01082611083984375,
0.0163421630859375,
-0.01445770263671875,
-0.01010894775390625,
0.0121917724609375,
-0.0250701904296875,
0.0002224445343017578,
0.0269927978515625,
-0.03875732421875,
-0.029083251953125,
-0.028106689453125... |
ramymohamed/ppo-LunarLander-v2 | 2023-07-12T19:05:33.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ramymohamed | null | null | ramymohamed/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T19:05:12 | ---
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: 264.71 +/- 24.27
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
...
```
| 784 | [
[
-0.0002090930938720703,
-0.027130126953125,
0.017059326171875,
0.0233306884765625,
-0.00606536865234375,
0.002750396728515625,
0.034454345703125,
-0.01213836669921875,
0.0198516845703125,
0.06500244140625,
-0.04315185546875,
-0.03521728515625,
-0.034271240234375... |
hugfacerhaha/ppo-Pyramid | 2023-07-12T20:24:18.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | hugfacerhaha | null | null | hugfacerhaha/ppo-Pyramid | 0 | 2 | ml-agents | 2023-07-12T20:24:15 | ---
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: hugfacerhaha/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,336 | [
[
-0.040008544921875,
-0.0343017578125,
0.0026226043701171875,
0.0130157470703125,
-0.01096343994140625,
0.01264190673828125,
0.0170745849609375,
-0.01485443115234375,
0.0330810546875,
0.030609130859375,
-0.040313720703125,
-0.049652099609375,
-0.0290679931640625,... |
Ryukijano/speecht5_finetuned_voxpopuli_Nederlands | 2023-09-11T05:19:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | Ryukijano | null | null | Ryukijano/speecht5_finetuned_voxpopuli_Nederlands | 0 | 2 | transformers | 2023-07-12T20:26:48 | ---
license: mit
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_Nederlands
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_Nederlands
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
from transformers import Seq2SeqTrainer
trainer = Seq2SeqTrainer(
args=training_args,
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
data_collator=data_collator,
tokenizer=processor,
)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6266 | 1.72 | 100 | 0.5448 |
| 0.5533 | 3.44 | 200 | 0.5040 |
| 0.5401 | 5.16 | 300 | 0.4930 |
| 0.535 | 6.88 | 400 | 0.4898 |
| 0.5331 | 8.6 | 500 | 0.4888 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
| 1,880 | [
[
-0.0296478271484375,
-0.03131103515625,
-0.007335662841796875,
0.008331298828125,
-0.01373291015625,
-0.0218353271484375,
-0.01393890380859375,
-0.007152557373046875,
-0.01071929931640625,
0.0237274169921875,
-0.048004150390625,
-0.036407470703125,
-0.0456848144... |
SrPrieto/ppo-LunarLander-v2 | 2023-07-12T21:14:49.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SrPrieto | null | null | SrPrieto/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T21:14:30 | ---
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: 271.18 +/- 13.06
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
lovelyxs/Pyramids | 2023-07-12T22:37:03.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | lovelyxs | null | null | lovelyxs/Pyramids | 0 | 2 | ml-agents | 2023-07-12T22:36:58 | ---
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: lovelyxs/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,329 | [
[
-0.04180908203125,
-0.034515380859375,
0.0008449554443359375,
0.0163116455078125,
-0.00945281982421875,
0.01020050048828125,
0.01702880859375,
-0.016632080078125,
0.034454345703125,
0.029876708984375,
-0.041656494140625,
-0.051055908203125,
-0.028289794921875,
... |
ramymohamed/a2c-AntBulletEnv-v0 | 2023-07-12T23:55:29.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ramymohamed | null | null | ramymohamed/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-12T23:54:08 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1714.78 +/- 104.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
rohn132/ppo-LunarLander-v2 | 2023-07-12T23:58:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | rohn132 | null | null | rohn132/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-12T23:57:59 | ---
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: 264.99 +/- 28.96
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
...
```
| 784 | [
[
-0.00020492076873779297,
-0.02716064453125,
0.01708984375,
0.0233306884765625,
-0.00606536865234375,
0.002758026123046875,
0.034454345703125,
-0.01214599609375,
0.019866943359375,
0.06500244140625,
-0.043121337890625,
-0.0352783203125,
-0.0343017578125,
-0.0... |
DunnBC22/ibert-roberta-base-finetuned-WikiNeural | 2023-07-22T20:51:45.000Z | [
"transformers",
"pytorch",
"tensorboard",
"ibert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:Babelscape/wikineural",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | DunnBC22 | null | null | DunnBC22/ibert-roberta-base-finetuned-WikiNeural | 1 | 2 | transformers | 2023-07-13T02:58:23 | ---
tags:
- generated_from_trainer
model-index:
- name: ibert-roberta-base-finetuned-WikiNeural
results: []
datasets:
- Babelscape/wikineural
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
- seqeval
pipeline_tag: token-classification
---
# ibert-roberta-base-finetuned-WikiNeural
This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base).
It achieves the following results on the evaluation set:
- Loss: 0.0878
- Loc
- Precision: 0.9249338624338624
- Recall: 0.9393786733837112
- F1: 0.9321003082562693
- Number: 5955
- Misc
- Precision: 0.8304751697034656
- Recall: 0.9185931634064414
- F1: 0.8723144760296463
- Number: 5061
- Org
- Precision: 0.9283453237410072
- Recall: 0.9353435778486517
- F1: 0.9318313113807049
- Number: 3449
- Per
- Precision: 0.9698098412076064
- Recall: 0.9495201535508637
- F1: 0.9595577538551062
- Number: 5210
- Overall
- Precision: 0.9107
- Recall: 0.9360
- F1: 0.9232
- Accuracy: 0.9909
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/WikiNeural%20-%20Transformer%20Comparison/POS%20Project%20with%20Wikineural%20Dataset%20-%20I-BERT%20Transformer.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/Babelscape/wikineural
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Loc Precision | Loc Recall | Loc F1 | Loc Number | Misc Precision | Misc Recall | Misc F1 | Misc Number | Org Precision | Org Recall | Org F1 | Org Number | Per Precision | Per Recall | Per F1 | Per Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------:|:----------------:|
| 0.1092 | 1.0 | 5795 | 0.0987 | 0.9125 | 0.9328 | 0.9225 | 5955 | 0.8003 | 0.9091 | 0.8512 | 5061 | 0.9143 | 0.9278 | 0.9210 | 3449 | 0.9714 | 0.9395 | 0.9552 | 5210 | 0.8957 | 0.9276 | 0.9114 | 0.9890 |
| 0.0723 | 2.0 | 11590 | 0.0878 | 0.9249 | 0.9394 | 0.9321 | 5955 | 0.8305 | 0.9186 | 0.8723 | 5061 | 0.9283 | 0.9353 | 0.9318 | 3449 | 0.9698 | 0.9495 | 0.9596 | 5210 | 0.9107 | 0.9360 | 0.9232 | 0.9909 |
* All values in the above chart arerounded to nearest ten-thousandth.
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3 | 3,198 | [
[
-0.055511474609375,
-0.044189453125,
0.0164337158203125,
0.0078887939453125,
-0.0024623870849609375,
-0.005710601806640625,
-0.0122528076171875,
-0.0177154541015625,
0.0343017578125,
0.02783203125,
-0.04730224609375,
-0.034393310546875,
-0.05084228515625,
-0... |
AnirbanRC/flan_t5_small_finetuned_anirbanrc | 2023-07-13T04:12:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | AnirbanRC | null | null | AnirbanRC/flan_t5_small_finetuned_anirbanrc | 0 | 2 | transformers | 2023-07-13T04:03:45 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan_t5_small_finetuned_anirbanrc
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: train[:50]
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 43.2639
---
<!-- 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. -->
# flan_t5_small_finetuned_anirbanrc
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5172
- Rouge1: 43.2639
- Rouge2: 20.726
- Rougel: 37.0774
- Rougelsum: 39.6232
- Gen Len: 16.92
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 7 | 1.6379 | 42.0058 | 18.6227 | 35.3019 | 38.6413 | 17.36 |
| No log | 2.0 | 14 | 1.5869 | 43.938 | 20.3595 | 36.876 | 40.0421 | 17.14 |
| No log | 3.0 | 21 | 1.5483 | 43.3723 | 20.3935 | 36.9286 | 39.6476 | 17.0 |
| No log | 4.0 | 28 | 1.5255 | 43.9774 | 21.5464 | 37.8954 | 40.5009 | 16.9 |
| No log | 5.0 | 35 | 1.5172 | 43.2639 | 20.726 | 37.0774 | 39.6232 | 16.92 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,263 | [
[
-0.032012939453125,
-0.0313720703125,
0.0122528076171875,
0.00020611286163330078,
-0.0190582275390625,
-0.0276336669921875,
-0.008056640625,
-0.0164642333984375,
0.021820068359375,
0.0272216796875,
-0.0513916015625,
-0.046966552734375,
-0.050872802734375,
-0... |
HoaAn2003/ppo-LunarLander-v2 | 2023-07-13T05:06:36.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | HoaAn2003 | null | null | HoaAn2003/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T05:06:16 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.13 +/- 20.36
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
xian79/a2c-AntBulletEnv-v0 | 2023-07-13T06:43:39.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | xian79 | null | null | xian79/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-13T06:28:44 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1080.97 +/- 252.97
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
lurah11/lurah11_lunarlander | 2023-07-13T10:33:51.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lurah11 | null | null | lurah11/lurah11_lunarlander | 0 | 2 | stable-baselines3 | 2023-07-13T10:33:20 | ---
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: 294.29 +/- 13.64
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
orya16215/ppo-LunarLander-v2 | 2023-07-13T13:27:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | orya16215 | null | null | orya16215/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T13:27:09 | ---
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: 274.86 +/- 15.55
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Darisian/ppo-LunarLander-v2 | 2023-07-13T14:17:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Darisian | null | null | Darisian/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T14:17:18 | ---
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: 202.59 +/- 93.15
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
mertseker/ppo-LunarLander-v2 | 2023-07-13T14:52:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mertseker | null | null | mertseker/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T14:52:19 | ---
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: 252.89 +/- 12.66
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
brunogs/distilbert-base-uncased-finetuned-cola | 2023-07-13T16:42:33.000Z | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | brunogs | null | null | brunogs/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-07-13T15:53:06 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: brunogs/distilbert-base-uncased-finetuned-cola
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. -->
# brunogs/distilbert-base-uncased-finetuned-cola
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: 0.1860
- Validation Loss: 0.5510
- Train Matthews Correlation: 0.5076
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5165 | 0.4641 | 0.4474 | 0 |
| 0.3176 | 0.4989 | 0.5060 | 1 |
| 0.1860 | 0.5510 | 0.5076 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,944 | [
[
-0.0384521484375,
-0.04638671875,
0.01861572265625,
0.0113525390625,
-0.02581787109375,
-0.00791168212890625,
-0.0135498046875,
-0.00763702392578125,
0.0171966552734375,
0.0033283233642578125,
-0.045135498046875,
-0.041656494140625,
-0.06549072265625,
-0.007... |
dariowsz/speecht5-base-finetuned-lj-speech | 2023-07-17T02:43:57.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:lj_speech",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | dariowsz | null | null | dariowsz/speecht5-base-finetuned-lj-speech | 0 | 2 | transformers | 2023-07-13T17:22:15 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- lj_speech
model-index:
- name: speecht5-base-finetuned-lj-speech
results: []
pipeline_tag: text-to-speech
---
<!-- 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-base-finetuned-lj-speech
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the lj_speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3929
## 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: 125
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4544 | 0.68 | 250 | 0.4076 |
| 0.4435 | 1.36 | 500 | 0.3966 |
| 0.4393 | 2.04 | 750 | 0.3930 |
| 0.4322 | 2.71 | 1000 | 0.3929 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 | 1,597 | [
[
-0.0312347412109375,
-0.04132080078125,
0.002140045166015625,
0.00826263427734375,
-0.0194244384765625,
-0.01849365234375,
-0.0208282470703125,
-0.0163116455078125,
-0.00037026405334472656,
0.02392578125,
-0.052001953125,
-0.047149658203125,
-0.045379638671875,
... |
xian79/a2c-PandaReachDense-v2 | 2023-07-13T18:05:05.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | xian79 | null | null | xian79/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T18:04:46 | ---
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: -2.42 +/- 0.30
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
...
```
| 802 | [
[
-0.0197906494140625,
-0.0474853515625,
-0.00479888916015625,
0.0469970703125,
-0.00017321109771728516,
-0.006031036376953125,
0.033233642578125,
-0.02496337890625,
0.028076171875,
0.042724609375,
-0.06256103515625,
-0.02899169921875,
-0.03277587890625,
-0.00... |
jliu596/a2c-AntBulletEnv-v0 | 2023-07-13T20:34:55.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jliu596 | null | null | jliu596/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-13T19:50:18 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 520.21 +/- 33.39
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 789 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
lovelyxs/a2c-AntBulletEnv-v0 | 2023-07-13T20:49:06.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lovelyxs | null | null | lovelyxs/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-13T20:38:39 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1134.23 +/- 127.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
lovelyxs/a2c-PandaReachDense-v2 | 2023-07-13T21:46:19.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lovelyxs | null | null | lovelyxs/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-13T21:45:52 | ---
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: -0.94 +/- 0.38
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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
skywalker7/ppo-Pyramids | 2023-07-14T00:57:54.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | skywalker7 | null | null | skywalker7/ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-14T00:57:49 | ---
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: skywalker7/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,335 | [
[
-0.03887939453125,
-0.0338134765625,
0.0009584426879882812,
0.0122222900390625,
-0.0102691650390625,
0.01439666748046875,
0.0183868408203125,
-0.0152587890625,
0.034515380859375,
0.031097412109375,
-0.039886474609375,
-0.04913330078125,
-0.0301055908203125,
... |
pandaIA/CamembertQA3 | 2023-07-14T01:44:28.000Z | [
"transformers",
"tf",
"camembert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | pandaIA | null | null | pandaIA/CamembertQA3 | 0 | 2 | transformers | 2023-07-14T01:43:52 | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: CamembertQA3
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. -->
# CamembertQA3
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3289
- Train End Logits Accuracy: 0.7077
- Train Start Logits Accuracy: 0.6139
- Validation Loss: 1.6439
- Validation End Logits Accuracy: 0.6508
- Validation Start Logits Accuracy: 0.5665
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.8724 | 0.5931 | 0.5097 | 1.6982 | 0.6436 | 0.5656 | 0 |
| 1.3289 | 0.7077 | 0.6139 | 1.6439 | 0.6508 | 0.5665 | 1 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,126 | [
[
-0.042724609375,
-0.035430908203125,
0.027862548828125,
0.006500244140625,
-0.0333251953125,
-0.0221405029296875,
-0.0010356903076171875,
-0.01457977294921875,
0.0123138427734375,
0.02099609375,
-0.055084228515625,
-0.04595947265625,
-0.050537109375,
-0.0131... |
terwrt/ppo-LunarLander-v2 | 2023-07-14T02:19:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | terwrt | null | null | terwrt/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T02:19:00 | ---
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: 239.73 +/- 22.86
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
localmodels/Pygmalion-13B-GPTQ | 2023-07-14T05:15:32.000Z | [
"transformers",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | localmodels | null | null | localmodels/Pygmalion-13B-GPTQ | 0 | 2 | transformers | 2023-07-14T05:15:32 | ---
duplicated_from: localmodels/LLM
---
# Pygmalion 13B GPTQ
From PygmalionAI: https://huggingface.co/PygmalionAI/pygmalion-13b
---
## Model Details
Pygmalion 13B is a dialogue model based on Meta's LLaMA-13B.
This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.
## Prompting
The model was trained on the usual Pygmalion persona + chat format, so any of the usual UIs should already handle everything correctly. If you're using the model directly, this is the expected formatting:
```
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
<START>
[DIALOGUE HISTORY]
You: [User's input message here]
[CHARACTER]:
```
Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is a sliding window of chat history so the model can have conversational context to draw from. Here's a concrete example:
```
Assistant's Persona: Assistant is a highly intelligent language model trained to comply with user requests.
<START>
Assistant: Hello! How may I help you today?
You: What is Zork?
Assistant:
```
Which will generate something like:
```
Zork is an interactive fiction computer game created in the 1970s by Infocom, Inc., which was later acquired by Activision Blizzard. It is widely considered one of the most influential games ever made and has been credited with popularizing text-based adventure games. The original version of Zork was written in the programming language MACRO-10, but it was ported to many other platforms over the years."
```
The model will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete.
## Other notes
- The model was trained as a LoRA with a somewhat unorthodox configuration which causes errors when used with the current version of `peft`, hence we release it as a full model instead.
## Limitations and biases
The intended use-case for this model is fictional conversation for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
| 2,623 | [
[
-0.02105712890625,
-0.070556640625,
0.029632568359375,
0.01702880859375,
-0.035888671875,
-0.022918701171875,
-0.005863189697265625,
-0.0328369140625,
0.01415252685546875,
0.045501708984375,
-0.06695556640625,
-0.037322998046875,
-0.03594970703125,
0.0099792... |
seny1004/wav2vec2-large-mms-1b-korean-colab | 2023-07-15T22:55:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | seny1004 | null | null | seny1004/wav2vec2-large-mms-1b-korean-colab | 0 | 2 | transformers | 2023-07-14T06:47:50 | ---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-l1107
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-korean-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: ko
split: test
args: ko
metrics:
- name: Wer
type: wer
value: 0.9929506545820745
---
<!-- 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-mms-1b-korean-colab
This model is a fine-tuned version of [facebook/mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 7.8135
- Wer: 0.9930
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.9747 | 2.63 | 100 | 7.8812 | 0.9990 |
| 5.9431 | 5.26 | 200 | 8.2212 | 0.9960 |
| 5.7372 | 7.89 | 300 | 8.1054 | 0.9930 |
| 5.2582 | 10.53 | 400 | 8.2347 | 0.9940 |
| 3.8725 | 13.16 | 500 | 7.7536 | 0.9940 |
| 3.4454 | 15.79 | 600 | 7.7220 | 0.9930 |
| 2.5989 | 18.42 | 700 | 7.8135 | 0.9930 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,117 | [
[
-0.03070068359375,
-0.044677734375,
0.0008254051208496094,
0.019866943359375,
-0.0181884765625,
-0.022186279296875,
-0.016754150390625,
-0.0113372802734375,
0.01271820068359375,
0.022003173828125,
-0.0662841796875,
-0.0452880859375,
-0.048492431640625,
-0.01... |
arham061/speecht5_finetuned_voxpopuli_nl | 2023-07-16T14:04:23.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | text-to-audio | arham061 | null | null | arham061/speecht5_finetuned_voxpopuli_nl | 0 | 2 | transformers | 2023-07-14T07:08:15 | ---
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
| 1,566 | [
[
-0.042572021484375,
-0.0462646484375,
-0.007061004638671875,
0.009674072265625,
-0.021331787109375,
-0.0345458984375,
-0.0185089111328125,
-0.01380157470703125,
-0.0011949539184570312,
0.0310211181640625,
-0.054412841796875,
-0.043548583984375,
-0.04238891601562... |
sail-rvc/Adolf_Hitler__RVC_v2_ | 2023-07-14T07:17:40.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/Adolf_Hitler__RVC_v2_ | 0 | 2 | transformers | 2023-07-14T07:17:28 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Adolf_Hitler__RVC_v2_
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:17:40
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 389 | [
[
-0.045074462890625,
-0.037750244140625,
0.02362060546875,
0.00554656982421875,
-0.02838134765625,
-0.000881195068359375,
0.01358795166015625,
-0.005695343017578125,
0.01027679443359375,
0.052886962890625,
-0.04913330078125,
-0.045867919921875,
-0.03857421875,
... |
sail-rvc/GuraTalkV2 | 2023-07-14T07:24:09.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/GuraTalkV2 | 0 | 2 | transformers | 2023-07-14T07:23:03 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# GuraTalkV2
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:24:09
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 378 | [
[
-0.0238037109375,
-0.02630615234375,
0.020538330078125,
0.0044097900390625,
-0.0389404296875,
0.0007123947143554688,
0.01084136962890625,
-0.002685546875,
0.0207977294921875,
0.06353759765625,
-0.047760009765625,
-0.044281005859375,
-0.039764404296875,
-0.01... |
sail-rvc/JustinBieber2333333 | 2023-07-14T07:24:52.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/JustinBieber2333333 | 0 | 2 | transformers | 2023-07-14T07:24:37 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# JustinBieber2333333
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:24:52
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 387 | [
[
-0.031768798828125,
-0.02362060546875,
0.0227203369140625,
0.00890350341796875,
-0.033050537109375,
0.00986480712890625,
0.013763427734375,
0.0002193450927734375,
0.030792236328125,
0.0697021484375,
-0.049102783203125,
-0.048309326171875,
-0.036041259765625,
... |
sail-rvc/KratosV2 | 2023-07-14T07:25:54.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/KratosV2 | 0 | 2 | transformers | 2023-07-14T07:25:33 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# KratosV2
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:25:54
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 376 | [
[
-0.023040771484375,
-0.018035888671875,
0.024078369140625,
0.002391815185546875,
-0.04351806640625,
0.0141754150390625,
0.007518768310546875,
-0.0027523040771484375,
0.0219268798828125,
0.0694580078125,
-0.04742431640625,
-0.039825439453125,
-0.046539306640625,
... |
sail-rvc/Mr_Beast_e180_s540 | 2023-07-14T07:28:23.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/Mr_Beast_e180_s540 | 0 | 2 | transformers | 2023-07-14T07:28:13 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Mr_Beast_e180_s540
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:28:23
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 386 | [
[
-0.035675048828125,
-0.01390838623046875,
0.012298583984375,
-0.0009541511535644531,
-0.032073974609375,
0.012115478515625,
0.02276611328125,
-0.00527191162109375,
0.02996826171875,
0.0631103515625,
-0.060302734375,
-0.03564453125,
-0.0318603515625,
0.006324... |
sail-rvc/Morty_Smith__Latino___RVC_V2_-_195_Epochs_ | 2023-07-14T07:28:43.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/Morty_Smith__Latino___RVC_V2_-_195_Epochs_ | 0 | 2 | transformers | 2023-07-14T07:28:15 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Morty_Smith__Latino___RVC_V2_-_195_Epochs_
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:28:43
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 410 | [
[
-0.0234222412109375,
-0.0286407470703125,
0.0185699462890625,
0.00931549072265625,
-0.036895751953125,
0.007709503173828125,
0.0207672119140625,
-0.00910186767578125,
0.0239715576171875,
0.06585693359375,
-0.0458984375,
-0.05023193359375,
-0.04083251953125,
... |
sail-rvc/PewDiePieV2_e350 | 2023-07-14T07:30:29.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/PewDiePieV2_e350 | 0 | 2 | transformers | 2023-07-14T07:29:44 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# PewDiePieV2_e350
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:30:29
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 384 | [
[
-0.0411376953125,
-0.028076171875,
0.0242919921875,
0.015411376953125,
-0.032379150390625,
0.004787445068359375,
0.007694244384765625,
0.011932373046875,
0.0156097412109375,
0.061370849609375,
-0.04620361328125,
-0.041656494140625,
-0.039276123046875,
-0.004... |
sail-rvc/Rick_Sanchez_Lat_v2 | 2023-07-14T07:30:56.000Z | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | sail-rvc | null | null | sail-rvc/Rick_Sanchez_Lat_v2 | 1 | 2 | transformers | 2023-07-14T07:30:26 |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Rick_Sanchez_Lat_v2
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:30:56
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
| 387 | [
[
-0.028289794921875,
-0.037994384765625,
0.0302886962890625,
0.007598876953125,
-0.035430908203125,
0.01015472412109375,
0.00637054443359375,
-0.0098114013671875,
0.025054931640625,
0.054534912109375,
-0.047882080078125,
-0.043609619140625,
-0.031768798828125,
... |
Python/ACROSS-m2o-eng-small | 2023-07-15T15:53:27.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Python | null | null | Python/ACROSS-m2o-eng-small | 0 | 2 | transformers | 2023-07-14T10:19:10 | # ACROSS-m2o-eng-small
## How to use
```python
from transformers import MT5ForConditionalGeneration, AutoTokenizer
model = MT5ForConditionalGeneration.from_pretrained('Python/ACROSS-m2o-eng-small')
tokenizer = AutoTokenizer.from_pretrained('Python/ACROSS-m2o-eng-small', use_fast=False)
input_text = '冈山县的倉敷市整个泡在泥水之中，数千户人家停水停电 这是日本近30多年来因为降雨而造成的死亡人数最多的一次水灾。究竟为何如此严重？仍然是每个人心中的疑问。 日本一向被视为是“防灾强国”，日本人对地震、台风、海啸等自然灾难绝对不陌生。 但这次暴雨引发水灾和土石流，竟然出现如此惊人的天灾死亡人数，也令许多人感到震惊。 短短几日的降雨量达到整个7月正常降雨量的三倍之多 超大降雨 究其原因，首先是短时间之内的超大降雨。 日本气象厅上周对西日本多个地方发布“大雨特别警报”，警告西部地方会受到“数十年一遇”的豪大雨，结果一共有93个观测站录得史上雨量第一的纪录。 从上周四开始的短短几日之内，日本西部地区多个地方的降雨量达到整个7月正常降雨量的三倍之多。 日本此次降雨多个地方超过上千毫米，日本气象厅也将这次豪雨正式命名为“平成30年7月豪雨”。 一共有7万多人参与救灾工作 河川溃堤 此外，超大豪雨超过河川疏洪承受度，短时间涌入巨大水量造成河川溃堤，沿岸市镇整个泡在泥水之中。 日本《每日新闻》报道说，冈山县的小田川溃堤，至少4600户都被洪水淹没，许多长者逃生不及淹死在自己家中。 暴雨过后被毁坏的家园 回水现象 据《产经新闻》报导，冈山县仓敷市真备町内的高梁川各支流共有5处溃堤，是因为大雨让河川主流水位上升，导致原本要和主流汇集的的支流无法流入，因此溃堤淹没附近区域，这样的状况被称之为“回水现象”。 有专家指出，“回水现象”也是这次豪雨水灾如此严重的原因之一。 救难人员抓紧时间在土石堆和残垣断壁下搜寻抢救生还者 山体滑坡 除了超大豪雨之外，日本地形多山，还有板块和花岗岩地质层，不少民宅都建筑在山坡地，一旦遇上大雨容易发生山体滑坡现象。 《日本经济新闻》报道说，这次日本暴雨灾难，多个地方发生大规模山体滑坡灾害，导致遇难人数增加。 受灾区的15个县有大约12000人安置到学校和体育馆等避难中心 该报引述京都大学防灾研究所的应用地质学教授千木良雅弘分析说，灾区是花岗岩的分布地区，其表层由“风化花岗岩”砂土覆盖，一旦降雨，表层滑坡就成为土石流，涌入住宅区。 专家也指出，表层滑坡导致的灾害近年来频频发生，原因多半是局部性暴雨所导致，需要检讨是否要在可能发生表层滑坡的地区建设住宅。'
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors='pt')
generate_ids = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
num_beams=5,
min_length=10,
length_penalty=0.8,
max_length=84
)
print(tokenizer.decode(generate_ids[0], skip_special_tokens=True))
``` | 1,597 | [
[
-0.053375244140625,
-0.0263214111328125,
0.03759765625,
0.0301971435546875,
-0.0311126708984375,
-0.024566650390625,
-0.00812530517578125,
-0.007568359375,
0.0035495758056640625,
0.034393310546875,
-0.046905517578125,
-0.040771484375,
-0.05126953125,
0.00685... |
shivamkumar208/PPO-LunarLander-v2 | 2023-07-14T11:07:17.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | shivamkumar208 | null | null | shivamkumar208/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T11:06:49 | ---
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: 278.96 +/- 17.35
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
CamposCaio/distilbert-base-uncased-finetuned-cola | 2023-07-14T12:28:04.000Z | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | CamposCaio | null | null | CamposCaio/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-07-14T12:23:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CamposCaio/distilbert-base-uncased-finetuned-cola
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. -->
# CamposCaio/distilbert-base-uncased-finetuned-cola
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: 0.1904
- Validation Loss: 0.5521
- Train Matthews Correlation: 0.5153
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5200 | 0.4695 | 0.4727 | 0 |
| 0.3210 | 0.4581 | 0.5159 | 1 |
| 0.1904 | 0.5521 | 0.5153 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,949 | [
[
-0.03741455078125,
-0.0460205078125,
0.0205535888671875,
0.01064300537109375,
-0.026885986328125,
-0.007778167724609375,
-0.01316070556640625,
-0.0094757080078125,
0.0167999267578125,
0.00200653076171875,
-0.0455322265625,
-0.042755126953125,
-0.06488037109375,
... |
crcdng/ppo-LunarLander-v2 | 2023-07-14T15:33:39.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | crcdng | null | null | crcdng/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T15:33:23 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.89 +/- 19.41
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
...
```
| 784 | [
[
-0.0001996755599975586,
-0.0271453857421875,
0.01708984375,
0.023345947265625,
-0.006076812744140625,
0.0027523040771484375,
0.034423828125,
-0.012115478515625,
0.019866943359375,
0.0650634765625,
-0.043182373046875,
-0.035247802734375,
-0.034332275390625,
-... |
NasimB/gpt2-concat-children-rarity-all-no-cut | 2023-07-14T18:38:30.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | NasimB | null | null | NasimB/gpt2-concat-children-rarity-all-no-cut | 0 | 2 | transformers | 2023-07-14T16:39:45 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-children-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. -->
# gpt2-concat-children-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: 4.3041
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6911 | 0.29 | 500 | 5.6297 |
| 5.3391 | 0.58 | 1000 | 5.1981 |
| 4.9881 | 0.87 | 1500 | 4.9521 |
| 4.7132 | 1.16 | 2000 | 4.7947 |
| 4.5556 | 1.46 | 2500 | 4.6743 |
| 4.4441 | 1.75 | 3000 | 4.5685 |
| 4.3241 | 2.04 | 3500 | 4.4891 |
| 4.1211 | 2.33 | 4000 | 4.4398 |
| 4.0983 | 2.62 | 4500 | 4.3846 |
| 4.0564 | 2.91 | 5000 | 4.3257 |
| 3.8632 | 3.2 | 5500 | 4.3216 |
| 3.7913 | 3.49 | 6000 | 4.2901 |
| 3.7794 | 3.78 | 6500 | 4.2588 |
| 3.693 | 4.07 | 7000 | 4.2573 |
| 3.508 | 4.37 | 7500 | 4.2534 |
| 3.5022 | 4.66 | 8000 | 4.2377 |
| 3.4941 | 4.95 | 8500 | 4.2240 |
| 3.341 | 5.24 | 9000 | 4.2355 |
| 3.3176 | 5.53 | 9500 | 4.2352 |
| 3.3059 | 5.82 | 10000 | 4.2347 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
| 2,356 | [
[
-0.0391845703125,
-0.03924560546875,
0.01108551025390625,
0.0023956298828125,
-0.016937255859375,
-0.0151824951171875,
0.00286102294921875,
-0.01502227783203125,
0.00742340087890625,
0.02166748046875,
-0.053375244140625,
-0.03955078125,
-0.05474853515625,
-0... |
efainman/ppo-LunarLander-v2 | 2023-07-14T17:05:13.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | efainman | null | null | efainman/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T17:04:52 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.83 +/- 21.59
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
NasimB/gpt2-concat-children-rarity-no-cut | 2023-07-14T20:05:08.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | NasimB | null | null | NasimB/gpt2-concat-children-rarity-no-cut | 0 | 2 | transformers | 2023-07-14T18:14:02 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-children-rarity-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. -->
# gpt2-concat-children-rarity-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: 4.3031
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6889 | 0.29 | 500 | 5.6290 |
| 5.333 | 0.58 | 1000 | 5.1952 |
| 4.9863 | 0.87 | 1500 | 4.9446 |
| 4.7095 | 1.16 | 2000 | 4.7950 |
| 4.5417 | 1.46 | 2500 | 4.6705 |
| 4.4396 | 1.75 | 3000 | 4.5655 |
| 4.3284 | 2.04 | 3500 | 4.4849 |
| 4.1173 | 2.33 | 4000 | 4.4400 |
| 4.0873 | 2.62 | 4500 | 4.3757 |
| 4.0498 | 2.91 | 5000 | 4.3285 |
| 3.8554 | 3.2 | 5500 | 4.3193 |
| 3.794 | 3.49 | 6000 | 4.2943 |
| 3.7753 | 3.78 | 6500 | 4.2622 |
| 3.6847 | 4.07 | 7000 | 4.2545 |
| 3.5066 | 4.37 | 7500 | 4.2500 |
| 3.4957 | 4.66 | 8000 | 4.2372 |
| 3.4871 | 4.95 | 8500 | 4.2231 |
| 3.3412 | 5.24 | 9000 | 4.2363 |
| 3.3087 | 5.53 | 9500 | 4.2348 |
| 3.3073 | 5.82 | 10000 | 4.2341 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
| 2,348 | [
[
-0.038238525390625,
-0.039642333984375,
0.009185791015625,
0.0022525787353515625,
-0.0187530517578125,
-0.0153656005859375,
0.0033168792724609375,
-0.0153656005859375,
0.00731658935546875,
0.02203369140625,
-0.053924560546875,
-0.038604736328125,
-0.055358886718... |
absolutt/ppo-LunarLander-v2-1stTry | 2023-07-14T18:24:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | absolutt | null | null | absolutt/ppo-LunarLander-v2-1stTry | 0 | 2 | stable-baselines3 | 2023-07-14T18:23:51 | ---
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: 251.71 +/- 21.38
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
...
```
| 784 | [
[
-0.0002092123031616211,
-0.0271453857421875,
0.01708984375,
0.0233612060546875,
-0.0060577392578125,
0.0027637481689453125,
0.034423828125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.034332275390625,
... |
sagarsdesai/PPO-LunarLander-v2 | 2023-07-15T17:50:16.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sagarsdesai | null | null | sagarsdesai/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T19:11:28 | ---
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: 289.79 +/- 13.53
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
roilhi/ppo-LunarLander-v2 | 2023-07-14T20:08:11.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | roilhi | null | null | roilhi/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-14T20:07:54 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 286.00 +/- 24.34
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
...
```
| 784 | [
[
-0.0002092123031616211,
-0.0271453857421875,
0.01708984375,
0.0233612060546875,
-0.0060577392578125,
0.0027637481689453125,
0.034423828125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.034332275390625,
... |
ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 | 2023-07-14T23:55:06.000Z | [
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | ALM-AHME | null | null | ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 | 1 | 2 | transformers | 2023-07-14T20:43:15 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: Splitted-Resized
split: train
args: Splitted-Resized
metrics:
- name: Accuracy
type: accuracy
value: 0.9938708156529938
---
<!-- 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. -->
# beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0275
- Accuracy: 0.9939
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.9
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.46 | 1.0 | 199 | 0.3950 | 0.8482 |
| 0.2048 | 2.0 | 398 | 0.1886 | 0.9189 |
| 0.182 | 3.0 | 597 | 0.1382 | 0.9481 |
| 0.0826 | 4.0 | 796 | 0.0760 | 0.9694 |
| 0.0886 | 5.0 | 995 | 0.0600 | 0.9788 |
| 0.0896 | 6.0 | 1194 | 0.0523 | 0.9802 |
| 0.0774 | 7.0 | 1393 | 0.0482 | 0.9826 |
| 0.0876 | 8.0 | 1592 | 0.0289 | 0.9877 |
| 0.1105 | 9.0 | 1791 | 0.0580 | 0.9821 |
| 0.0289 | 10.0 | 1990 | 0.0294 | 0.9925 |
| 0.0594 | 11.0 | 2189 | 0.0331 | 0.9906 |
| 0.0011 | 12.0 | 2388 | 0.0275 | 0.9939 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,593 | [
[
-0.033233642578125,
-0.034820556640625,
0.007038116455078125,
0.0007200241088867188,
-0.01971435546875,
-0.0163116455078125,
0.006999969482421875,
-0.0246734619140625,
0.0120086669921875,
0.0279541015625,
-0.05126953125,
-0.06622314453125,
-0.059661865234375,
... |
Panchovix/Wizard-Vicuna-30B-Uncensored-lxctx-PI-16384-LoRA-fp16 | 2023-07-17T20:01:30.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Panchovix | null | null | Panchovix/Wizard-Vicuna-30B-Uncensored-lxctx-PI-16384-LoRA-fp16 | 0 | 2 | transformers | 2023-07-15T01:41:03 | ---
license: other
---
[Wizard-Vicuna-30B-Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored) merged with bhenrym14's [airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-LoRA](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-LoRA), full model (FP16)
More info about the LoRA [Here](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-LoRA). This is an alternative to SuperHOT 8k LoRA trained with LoRA_rank 64 and context extended to 16K, with airoboros 1.4.1 dataset. | 526 | [
[
-0.045135498046875,
-0.0305633544921875,
0.0047149658203125,
0.005161285400390625,
-0.043243408203125,
-0.00518798828125,
0.01406097412109375,
-0.04559326171875,
0.046295166015625,
0.053009033203125,
-0.048980712890625,
-0.0253448486328125,
-0.043365478515625,
... |
BaleChen/test_lunarlanderv2_mlp_ppo | 2023-07-15T06:06:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | BaleChen | null | null | BaleChen/test_lunarlanderv2_mlp_ppo | 0 | 2 | stable-baselines3 | 2023-07-15T06:06:18 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.71 +/- 12.68
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Ahmet2250/ppo-LunarLander-v2 | 2023-07-15T07:16:38.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Ahmet2250 | null | null | Ahmet2250/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T07:15:40 | ---
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: 262.32 +/- 20.78
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
...
```
| 784 | [
[
-0.0002092123031616211,
-0.0271453857421875,
0.01708984375,
0.0233612060546875,
-0.0060577392578125,
0.0027637481689453125,
0.034423828125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.034332275390625,
... |
seonglae/wizardlm-7b-uncensored-gptq | 2023-07-19T08:54:53.000Z | [
"transformers",
"llama",
"text-generation",
"wizardlm",
"uncensored",
"gptq",
"quantization",
"auto-gptq",
"7b",
"4bit",
"dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | seonglae | null | null | seonglae/wizardlm-7b-uncensored-gptq | 0 | 2 | transformers | 2023-07-15T08:13:35 | ---
inference: false
license: other
datasets:
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
tags:
- wizardlm
- uncensored
- gptq
- quantization
- auto-gptq
- 7b
- llama
- 4bit
---
# Get Started
This model should use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) so you need to use `auto-gptq`
- `no-act-order` model
- 4bit model quantization
```py
from transformers import AutoTokenizer, pipeline, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, StoppingCriteria, PreTrainedTokenizerBase
from auto_gptq import AutoGPTQForCausalLM
model_id = 'seonglae/wizardlm-7b-uncensored-gptq'
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(
model_id,
model_basename=model_basename,
trust_remote_code=True,
device='cuda:0',
use_triton=False,
use_safetensors=True,
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
temperature=0.5,
top_p=0.95,
max_new_tokens=100,
repetition_penalty=1.15,
)
prompt = "USER: Are you AI?\nASSISTANT:"
pipe(prompt)
``` | 1,134 | [
[
-0.0123443603515625,
-0.042755126953125,
0.026763916015625,
0.0169525146484375,
-0.0307464599609375,
0.0032100677490234375,
0.0253753662109375,
0.008331298828125,
-0.0208587646484375,
0.030303955078125,
-0.0303955078125,
-0.03216552734375,
-0.063720703125,
0... |
Xxmlala/DRL-Course | 2023-07-15T09:43:06.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Xxmlala | null | null | Xxmlala/DRL-Course | 0 | 2 | stable-baselines3 | 2023-07-15T09:42:20 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 235.95 +/- 18.00
name: mean_reward
verified: false
---
# **PPO_MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_MlpPolicy** 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
...
```
| 814 | [
[
0.0006051063537597656,
-0.0272979736328125,
0.015838623046875,
0.029876708984375,
0.0013637542724609375,
0.0009050369262695312,
0.0301971435546875,
-0.00930023193359375,
0.0231170654296875,
0.0606689453125,
-0.048004150390625,
-0.0340576171875,
-0.03790283203125... |
Xxmlala/ppo-LunarLander-v2 | 2023-07-15T09:45:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Xxmlala | null | null | Xxmlala/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T09:44:16 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 229.98 +/- 14.55
name: mean_reward
verified: false
---
# **PPO_MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_MlpPolicy** 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
...
```
| 814 | [
[
0.0006051063537597656,
-0.0272979736328125,
0.015838623046875,
0.029876708984375,
0.0013637542724609375,
0.0009050369262695312,
0.0301971435546875,
-0.00930023193359375,
0.0231170654296875,
0.0606689453125,
-0.048004150390625,
-0.0340576171875,
-0.03790283203125... |
Kooooofe/LunarModel | 2023-07-15T09:50:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Kooooofe | null | null | Kooooofe/LunarModel | 0 | 2 | stable-baselines3 | 2023-07-15T09:45:54 | ---
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: -87.55 +/- 120.99
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
...
```
| 785 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Kooooofe/LandingLunar | 2023-07-15T09:50:29.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Kooooofe | null | null | Kooooofe/LandingLunar | 0 | 2 | stable-baselines3 | 2023-07-15T09:50:08 | ---
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: -43.18 +/- 195.98
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
...
```
| 785 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
hwang99/ppo-LunarLander-v2 | 2023-07-15T15:54:57.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | hwang99 | null | null | hwang99/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T10:01:59 | ---
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: 262.77 +/- 23.38
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
...
```
| 784 | [
[
-0.00020384788513183594,
-0.027130126953125,
0.01708984375,
0.0233612060546875,
-0.006072998046875,
0.00276947021484375,
0.034423828125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
-0.0... |
0sunfire0/ppo-SnowballTarget_00 | 2023-07-15T16:34:37.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 0sunfire0 | null | null | 0sunfire0/ppo-SnowballTarget_00 | 0 | 2 | ml-agents | 2023-07-15T15:50:28 | ---
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: 0sunfire0/ppo-SnowballTarget_00
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,367 | [
[
-0.031402587890625,
-0.040496826171875,
0.0085906982421875,
0.00606536865234375,
-0.021209716796875,
0.0224151611328125,
0.01287078857421875,
-0.0159912109375,
0.0264434814453125,
0.0335693359375,
-0.0565185546875,
-0.053985595703125,
-0.03631591796875,
-0.0... |
NotAgain0/ppo-LunarLander-v2 | 2023-07-15T16:21:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | NotAgain0 | null | null | NotAgain0/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T16:21:01 | ---
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: -166.20 +/- 21.91
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
...
```
| 785 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Prabhat2000/ppo-LunarLander-v2 | 2023-07-15T17:35:35.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Prabhat2000 | null | null | Prabhat2000/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T17:35:14 | ---
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: 267.21 +/- 21.56
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Naruke/ppo-LunarLander-v2 | 2023-07-15T18:25:05.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Naruke | null | null | Naruke/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T18:24:39 | ---
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: 291.25 +/- 14.22
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
...
```
| 784 | [
[
-0.00020384788513183594,
-0.027130126953125,
0.01708984375,
0.0233612060546875,
-0.006072998046875,
0.00276947021484375,
0.034423828125,
-0.01213836669921875,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
-0.0... |
hyunussarioglu/ppo-LunarLander-v2 | 2023-07-15T19:03:23.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | hyunussarioglu | null | null | hyunussarioglu/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T19:03:01 | ---
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: 263.38 +/- 18.96
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5 | 2023-07-16T03:40:20.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | Evan-Lin | null | null | Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5 | 0 | 2 | transformers | 2023-07-15T19:51:18 | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
| 1,348 | [
[
-0.0107421875,
-0.057220458984375,
0.021728515625,
0.01087188720703125,
-0.02484130859375,
0.004817962646484375,
-0.00432586669921875,
-0.0196380615234375,
0.0056915283203125,
0.035003662109375,
-0.051849365234375,
-0.04339599609375,
-0.037017822265625,
0.01... |
schutzp/lunarLander-PPO-trained-2e7 | 2023-07-15T20:40:42.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | schutzp | null | null | schutzp/lunarLander-PPO-trained-2e7 | 0 | 2 | stable-baselines3 | 2023-07-15T20:40:04 | ---
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: 272.67 +/- 19.66
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
NemesisAlm/ppo-LunarLander-v2 | 2023-07-15T21:14:03.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | NemesisAlm | null | null | NemesisAlm/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-15T21:13:41 | ---
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: 252.74 +/- 22.18
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
0sunfire0/a2c-AntBulletEnv-v0 | 2023-07-15T21:39:40.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 0sunfire0 | null | null | 0sunfire0/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-15T21:38:34 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2109.96 +/- 104.30
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
DunnBC22/mbart-large-50-Biomedical_Dataset | 2023-07-18T21:26:42.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"biology",
"medical",
"translation",
"en",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | DunnBC22 | null | null | DunnBC22/mbart-large-50-Biomedical_Dataset | 2 | 2 | transformers | 2023-07-16T01:25:29 | ---
license: mit
tags:
- generated_from_trainer
- biology
- medical
metrics:
- bleu
- rouge
- meteor
model-index:
- name: mbart-large-50-Biomedical_Dataset
results: []
language:
- en
- it
pipeline_tag: translation
---
# mbart-large-50-Biomedical_Dataset
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50).
It achieves the following results on the evaluation set:
- Training Loss: 1.0165
- Epoch: 1.0
- Step: 2636
- Validation Loss: 0.9425
- Bleu: 38.9893
- Rouge Metrics:
- Rouge1: 0.6826259612196924
- Rouge2: 0.473675987811788
- RougeL: 0.6586445010303293
- RougeLsum: 0.6585487473231793
- Meteor: 0.6299677745833094
- Prediction lengths: 24.362727392855568
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/Biomedical%20Translation%20(EN%20to%20IT)/Biomedical%20-%20Translation%20Project.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/paolo-ruggirello/biomedical-dataset
### Histogram of English Input Word Counts
/Images/Histogram%20of%20English%20Lengths.png)
### Histogram of Italian Input Word Counts
/Images/Histogram%20of%20Italian%20Inputs.png)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results*
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rouge2 | RougeL | RougeLsum | Meteor | Prediction Lengths |
| :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: |
| 1.0165 | 1.0 | 2636 | 0.9425 | 38.9893 | 0.6826 | 0.4737 | 0.6586 | 0.6585 | 0.6270 | 24.3627 |
Footnotes:
*: All results in this table are rounded to the nearest ten-thousandths of the decimal.
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3 | 2,672 | [
[
-0.040069580078125,
-0.0406494140625,
0.0277252197265625,
0.01483917236328125,
-0.00881195068359375,
-0.010528564453125,
-0.0212860107421875,
-0.0200042724609375,
0.025543212890625,
0.02392578125,
-0.04022216796875,
-0.052978515625,
-0.05078125,
0.0095596313... |
Vasanth/distilbert-stock-tweet-sentiment-analysis | 2023-07-16T05:26:06.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Vasanth | null | null | Vasanth/distilbert-stock-tweet-sentiment-analysis | 0 | 2 | transformers | 2023-07-16T05:15:36 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-stock-tweet-sentiment-analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-stock-tweet-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6075
- Accuracy: 0.782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.686 | 1.0 | 1000 | 0.5916 | 0.7745 |
| 0.4804 | 2.0 | 2000 | 0.5635 | 0.7812 |
| 0.3644 | 3.0 | 3000 | 0.6075 | 0.782 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,509 | [
[
-0.020751953125,
-0.04803466796875,
0.007488250732421875,
0.0242919921875,
-0.0292510986328125,
0.0005841255187988281,
-0.00896453857421875,
-0.00038313865661621094,
0.0128326416015625,
0.003742218017578125,
-0.05029296875,
-0.0626220703125,
-0.07275390625,
... |
atiiisham988/speecht5_finetuned_voxpopuli_nl | 2023-07-16T14:16:09.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | atiiisham988 | null | null | atiiisham988/speecht5_finetuned_voxpopuli_nl | 0 | 2 | transformers | 2023-07-16T05:30:30 | ---
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
| 1,581 | [
[
-0.031982421875,
-0.037139892578125,
-0.0007567405700683594,
0.01439666748046875,
-0.0231781005859375,
-0.0232696533203125,
-0.0167236328125,
-0.0127105712890625,
-0.006130218505859375,
0.0190887451171875,
-0.0556640625,
-0.04925537109375,
-0.0439453125,
-0.... |
bochen0909/ppo-LunarLander-v2 | 2023-07-16T06:41:23.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | bochen0909 | null | null | bochen0909/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T06:41:08 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.97 +/- 20.22
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
manmyung/a2c-AntBulletEnv-v0 | 2023-07-16T07:45:04.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | manmyung | null | null | manmyung/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-16T07:43:55 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1954.34 +/- 180.80
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
watcharakorn/whisper-small-th-v2 | 2023-07-16T08:24:22.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"th-asr-leaderboard",
"generated_from_trainer",
"th",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | watcharakorn | null | null | watcharakorn/whisper-small-th-v2 | 0 | 2 | transformers | 2023-07-16T08:21:55 | ---
language:
- th
license: apache-2.0
base_model: openai/whisper-small
tags:
- th-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small th - mix dataset v.2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: th
split: test
args: 'config: th, split: test'
metrics:
- name: Wer
type: wer
value: 0.37791454289122656
---
<!-- 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 Small th - mix dataset v.2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2980
- Wer: 0.3779
## 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_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3654 | 0.26 | 1000 | 0.2980 | 0.3779 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,857 | [
[
-0.0311279296875,
-0.0328369140625,
0.006587982177734375,
0.014404296875,
-0.024993896484375,
-0.03857421875,
-0.0225677490234375,
-0.020416259765625,
0.01776123046875,
0.023040771484375,
-0.05908203125,
-0.03021240234375,
-0.047821044921875,
-0.025588989257... |
Hans14/PPO-LunarLander-v2 | 2023-07-16T08:42:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Hans14 | null | null | Hans14/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T08:41:32 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.99 +/- 12.40
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
arick6/ppo-LunarLander-v2 | 2023-07-17T11:03:13.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | arick6 | null | null | arick6/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T11:29:14 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.27 +/- 11.83
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Rihong/ppo-LunarLander-v2 | 2023-07-16T12:20:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Rihong | null | null | Rihong/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T12:19:16 | ---
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
larry-jiang/RL | 2023-07-16T12:48:55.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | larry-jiang | null | null | larry-jiang/RL | 0 | 2 | stable-baselines3 | 2023-07-16T12:47:54 | ---
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
madoe001/a2c-AntBulletEnv-v0 | 2023-07-16T12:58:37.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | madoe001 | null | null | madoe001/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-16T12:56:52 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1411.03 +/- 55.48
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 790 | [
[
-0.02679443359375,
-0.044403076171875,
0.01070404052734375,
0.0208892822265625,
-0.0035266876220703125,
0.001781463623046875,
0.0187530517578125,
-0.0176239013671875,
0.0193939208984375,
0.0265655517578125,
-0.052581787109375,
-0.037506103515625,
-0.044250488281... |
SwampMan/a2c-AntBulletEnv-v0 | 2023-07-16T13:44:52.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SwampMan | null | null | SwampMan/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-16T13:43:45 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1250.47 +/- 141.94
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
| 791 | [
[
-0.02679443359375,
-0.044403076171875,
0.01070404052734375,
0.0208892822265625,
-0.0035266876220703125,
0.001781463623046875,
0.0187530517578125,
-0.0176239013671875,
0.0193939208984375,
0.0265655517578125,
-0.052581787109375,
-0.037506103515625,
-0.044250488281... |
magicsword/wy-mt-en-zh-2 | 2023-07-16T17:27:39.000Z | [
"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 | magicsword | null | null | magicsword/wy-mt-en-zh-2 | 0 | 2 | transformers | 2023-07-16T15:15:50 | ---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- magicsword/autotrain-data-wy-mt-en-zh
co2_eq_emissions:
emissions: 71.14399741050826
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 74981139786
- CO2 Emissions (in grams): 71.1440
## Validation Metrics
- Loss: 2.220
- SacreBLEU: 12.949
- Gen len: 16.386 | 358 | [
[
-0.0087890625,
-0.0230255126953125,
0.035369873046875,
0.00675201416015625,
-0.0070953369140625,
-0.0147247314453125,
0.00592803955078125,
-0.003520965576171875,
-0.02947998046875,
0.0259552001953125,
-0.0438232421875,
-0.02752685546875,
-0.050537109375,
-0.... |
gioca91/ppo-LunarLander-v2 | 2023-07-16T15:21:24.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | gioca91 | null | null | gioca91/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T15:20:46 | ---
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
PJ02/ppo-LunarLander-v2 | 2023-07-16T15:34:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PJ02 | null | null | PJ02/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T15:33:44 | ---
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
localmodels/WizardCoder-15B-V1.0-GPTQ | 2023-07-16T15:44:39.000Z | [
"transformers",
"gpt_bigcode",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | localmodels | null | null | localmodels/WizardCoder-15B-V1.0-GPTQ | 0 | 2 | transformers | 2023-07-16T15:44:39 | ---
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.
| 9,805 | [
[
-0.03948974609375,
-0.04608154296875,
-0.0005135536193847656,
0.01558685302734375,
0.01140594482421875,
-0.018829345703125,
-0.01467132568359375,
-0.017333984375,
-0.01450347900390625,
0.040618896484375,
-0.031646728515625,
-0.03729248046875,
-0.044647216796875,... |
DanGalt/speecht5_finetuned_voxpopuli_fi | 2023-07-16T17:11:18.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"fi",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | text-to-speech | DanGalt | null | null | DanGalt/speecht5_finetuned_voxpopuli_fi | 0 | 2 | transformers | 2023-07-16T17:07:04 | ---
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 | 1,614 | [
[
-0.028228759765625,
-0.047515869140625,
-0.0026760101318359375,
0.01197052001953125,
-0.0178070068359375,
-0.0209503173828125,
-0.018157958984375,
-0.010955810546875,
-0.005123138427734375,
0.0165252685546875,
-0.055999755859375,
-0.050933837890625,
-0.042510986... |
nishchalprasad/lunar_lander_v2-PPO | 2023-07-16T17:44:18.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nishchalprasad | null | null | nishchalprasad/lunar_lander_v2-PPO | 0 | 2 | stable-baselines3 | 2023-07-16T17:43:57 | ---
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
...
```
| 796 | [
[
-0.005001068115234375,
-0.026031494140625,
0.016326904296875,
0.0264434814453125,
0.0017757415771484375,
0.00011879205703735352,
0.0304107666015625,
-0.01397705078125,
0.020721435546875,
0.06402587890625,
-0.045989990234375,
-0.0333251953125,
-0.038421630859375,... |
il18/PPO-LunarLander-v2 | 2023-07-16T20:38:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | il18 | null | null | il18/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-16T20:37:53 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.21 +/- 15.74
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
samiul25/ppo-LunarLander-v2 | 2023-07-17T02:25:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | samiul25 | null | null | samiul25/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T02:25:07 | ---
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: 248.09 +/- 22.88
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
...
```
| 784 | [
[
-0.00020706653594970703,
-0.0271453857421875,
0.0170745849609375,
0.0233306884765625,
-0.006046295166015625,
0.0027751922607421875,
0.034423828125,
-0.01214599609375,
0.0199127197265625,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,... |
Jasper881108/whisper-medium-zh | 2023-07-18T08:17:16.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-medium",
"asr",
"zh-TW",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | Jasper881108 | null | null | Jasper881108/whisper-medium-zh | 0 | 2 | transformers | 2023-07-17T02:28:44 | ---
license: apache-2.0
tags:
- whisper-medium
- asr
- zh-TW
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Medium TW
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: zh-TW
split: test
metrics:
- type: wer
value: 7.38
name: WER
---
<!-- 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 Medium TW
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 dataset.
## Training and evaluation data
Training:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)
Evaluation:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)
## Training procedure
- Datasets were augmented using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift, TimeStretch, Gain, AddGaussianNoise transformations at `p=0.3`.
- A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- gradient_accumulation_steps: 32
- optimizer: Adam
- generation_max_length: 225,
- warmup_steps: 200
- max_steps: 2000,
- fp16: True,
- evaluation_strategy: "steps",
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu120
- Datasets 2.13.1
| 1,878 | [
[
-0.0213623046875,
-0.042236328125,
0.0012235641479492188,
0.032257080078125,
-0.0234375,
-0.0258941650390625,
-0.032440185546875,
-0.043670654296875,
0.017303466796875,
0.023651123046875,
-0.05914306640625,
-0.039703369140625,
-0.03271484375,
-0.005348205566... |
diffuser34/ppo-LunarLander-v2 | 2023-07-17T03:04:08.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | diffuser34 | null | null | diffuser34/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T03:03:48 | ---
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: 274.41 +/- 22.83
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
uzenhuang/distilgpt2-finetuned-wikitext2-test | 2023-07-17T03:22:43.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | uzenhuang | null | null | uzenhuang/distilgpt2-finetuned-wikitext2-test | 0 | 2 | transformers | 2023-07-17T03:03:59 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2-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. -->
# distilgpt2-finetuned-wikitext2-test
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8267
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 277 | 3.8379 |
| 3.8669 | 2.0 | 554 | 3.8250 |
| 3.8669 | 3.0 | 831 | 3.8267 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,378 | [
[
-0.035400390625,
-0.049041748046875,
0.009674072265625,
0.015411376953125,
-0.0272216796875,
-0.03289794921875,
-0.004119873046875,
-0.0127410888671875,
-0.01044464111328125,
0.013641357421875,
-0.0576171875,
-0.02276611328125,
-0.05694580078125,
-0.01117706... |
Sukmin/a2c-PandaReachDense-v2 | 2023-07-17T07:43:56.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Sukmin | null | null | Sukmin/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T07:42:00 | ---
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: -1.18 +/- 0.37
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
...
```
| 802 | [
[
-0.019775390625,
-0.04742431640625,
-0.00479888916015625,
0.0469970703125,
-0.0001550912857055664,
-0.006000518798828125,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.0625,
-0.0289764404296875,
-0.03277587890625,
-0.006641... |
madoe001/a2c-PandaReachDense-v2 | 2023-07-17T08:27:55.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | madoe001 | null | null | madoe001/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T08:25:09 | ---
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: -0.85 +/- 0.24
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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
weekcircle/wav2vec2-large-mms-1b-korean-colab_v3 | 2023-07-17T11:49:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | weekcircle | null | null | weekcircle/wav2vec2-large-mms-1b-korean-colab_v3 | 0 | 2 | transformers | 2023-07-17T09:08:44 | ---
license: cc-by-nc-4.0
base_model: weekcircle/wav2vec2-large-mms-1b-korean-colab_v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-korean-colab_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-mms-1b-korean-colab_v3
This model is a fine-tuned version of [weekcircle/wav2vec2-large-mms-1b-korean-colab_v2](https://huggingface.co/weekcircle/wav2vec2-large-mms-1b-korean-colab_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1476
- Wer: 0.3443
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2374 | 0.18 | 100 | 0.1654 | 0.3761 |
| 0.2231 | 0.36 | 200 | 0.1648 | 0.3752 |
| 0.2263 | 0.53 | 300 | 0.1647 | 0.3859 |
| 0.2197 | 0.71 | 400 | 0.1618 | 0.3628 |
| 0.223 | 0.89 | 500 | 0.1642 | 0.3792 |
| 0.2143 | 1.07 | 600 | 0.1585 | 0.3684 |
| 0.2082 | 1.24 | 700 | 0.1589 | 0.3711 |
| 0.2166 | 1.42 | 800 | 0.1567 | 0.3647 |
| 0.2087 | 1.6 | 900 | 0.1561 | 0.3567 |
| 0.2109 | 1.78 | 1000 | 0.1551 | 0.3570 |
| 0.2036 | 1.95 | 1100 | 0.1553 | 0.3644 |
| 0.1926 | 2.13 | 1200 | 0.1545 | 0.3579 |
| 0.1972 | 2.31 | 1300 | 0.1539 | 0.3508 |
| 0.2086 | 2.49 | 1400 | 0.1526 | 0.3523 |
| 0.2179 | 2.66 | 1500 | 0.1524 | 0.3502 |
| 0.2036 | 2.84 | 1600 | 0.1515 | 0.3502 |
| 0.2196 | 3.02 | 1700 | 0.1510 | 0.3459 |
| 0.2149 | 3.2 | 1800 | 0.1498 | 0.3462 |
| 0.2111 | 3.37 | 1900 | 0.1485 | 0.3477 |
| 0.2043 | 3.55 | 2000 | 0.1481 | 0.3443 |
| 0.2043 | 3.73 | 2100 | 0.1475 | 0.3480 |
| 0.2018 | 3.91 | 2200 | 0.1476 | 0.3443 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,772 | [
[
-0.03936767578125,
-0.0384521484375,
0.00864410400390625,
0.0153656005859375,
-0.01416778564453125,
-0.01837158203125,
-0.00844573974609375,
-0.01537322998046875,
0.0267333984375,
0.0229339599609375,
-0.059478759765625,
-0.053131103515625,
-0.047637939453125,
... |
korazer/chart-test-classify | 2023-07-18T05:13:39.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | korazer | null | null | korazer/chart-test-classify | 0 | 2 | transformers | 2023-07-17T11:04:52 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: chart-test-classify
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.3777777850627899
---
# chart-test-classify
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### chart graph
#### non chart graph
 | 783 | [
[
-0.04217529296875,
-0.052215576171875,
0.00887298583984375,
0.0306549072265625,
-0.036834716796875,
0.0162811279296875,
0.0204620361328125,
-0.035491943359375,
0.044403076171875,
0.021087646484375,
-0.0238494873046875,
-0.061187744140625,
-0.0484619140625,
0... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.