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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 12:28:30
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11.7k
| library_name
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|---|---|---|---|---|---|---|---|---|---|
meanderingmagi/Vicuna-7b
|
meanderingmagi
| 2023-06-29T19:13:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T19:05:14Z |
---
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/Jq4vkcDakD">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 -->
# Vicuna 7B 1.1 HF
This is an HF version of the [Vicuna 7B 1.1 model](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1).
It was created by merging the deltas provided in the above repo with the original Llama 7B model, [using the code provided on their Github page](https://github.com/lm-sys/FastChat#vicuna-weights).
## My Vicuna 1.1 model repositories
I have the following Vicuna 1.1 repositories available:
**13B models:**
* [Unquantized 13B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-13B-1.1-HF)
* [GPTQ quantized 4bit 13B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/vicuna-13B-1.1-GGML)
**7B models:**
* [Unquantized 7B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-7B-1.1-HF)
* [GPTQ quantized 4bit 7B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-7B-1.1-GPTQ-4bit-128g)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/vicuna-7B-1.1-GGML)
<!-- 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/Jq4vkcDakD)
## 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
**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Vicuna Model Card
## Model details
**Model type:**
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
Vicuna was trained between March 2023 and April 2023.
**Organizations developing the model:**
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
**Paper or resources for more information:**
https://vicuna.lmsys.org/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues
## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training dataset
70K conversations collected from ShareGPT.com.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
## Major updates of weights v1.1
- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
- Fix the supervised fine-tuning loss computation for better model quality.
|
cleanrl/HalfCheetah-v2-ddpg_continuous_action-seed1
|
cleanrl
| 2023-06-29T18:56:26Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"HalfCheetah-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T18:56:19Z |
---
tags:
- HalfCheetah-v2
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetah-v2
type: HalfCheetah-v2
metrics:
- type: mean_reward
value: 9856.96 +/- 218.02
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **HalfCheetah-v2**
This is a trained model of a DDPG agent playing HalfCheetah-v2.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id HalfCheetah-v2
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/HalfCheetah-v2-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/cleanrl/HalfCheetah-v2-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/HalfCheetah-v2-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --track --capture-video --save-model --hf-entity cleanrl --upload-model --env-id HalfCheetah-v2 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'env_id': 'HalfCheetah-v2',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-GGML
|
TheBloke
| 2023-06-29T18:55:03Z | 0 | 10 | null |
[
"license:other",
"region:us"
] | null | 2023-06-29T18:05:13Z |
---
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 -->
# Jon Durbin's Airoboros 33B GPT4 1.4 GGML
These files are GGML format model files for [Jon Durbin's Airoboros 33B GPT4 1.4](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.4).
These are SuperHOT GGMLs with an increased context length. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. It was discovered and developed by [kaiokendev](https://huggingface.co/kaiokendev).
In order to use the increased context length, you can presently use:
* [KoboldCpp](https://github.com/LostRuins/koboldcpp) - [release 1.33](https://github.com/LostRuins/koboldcpp/releases/tag/v1.33) or later.
Support is also expected to come to llama.cpp, however it is still being worked on and there is currently no ETA for that.
To use the increased context with KoboldCpp and (when supported) llama.cpp, simply use `--contextsize` to set the desired context, eg `--contextsize 4096` or `--contextsize 8192`.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-GGML)
* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16)
* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.4)
<!-- compatibility_ggml start -->
## Compatibility
These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants.
However the increased context length won't work without specific support. See the note in the introduction for details on using increased context.
## Explanation of the new k-quant methods
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q2_K.bin | q2_K | 2 | 13.71 GB | 16.21 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.28 GB | 19.78 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.72 GB | 18.22 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 14.06 GB | 16.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.62 GB | 22.12 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.36 GB | 20.86 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.05 GB | 25.55 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.40 GB | 24.90 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB | 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `koboldcpp`
On Linux I use the following command line to launch the KoboldCpp UI with OpenCL aceleration and a context size of 4096:
```
python ./koboldcpp.py --stream --unbantokens --threads 8 --usecublas 100 airoboros-33b-gpt4-1.4-superhot-8k.ggmlv3.q5_0.bin
```
Change `--gpulayers 100` to the number of layers you want/are able to offload to the GPU. Remove it if you don't have GPU acceleration.
For OpenCL acceleration, change `--usecublas` to `--useclblast 0 0`. You may need to change the second `0` to `1` if you have both an iGPU and a discrete GPU.
<!-- 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, Dmitriy Samsonov.
**Patreon special mentions**: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
# Original model card: Jon Durbin's Airoboros 33B GPT4 1.4
__not yet tested!__
## Overview
This is a qlora fine-tune 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of the previous gpt-4 series, with a few extras:
* fixed (+ more examples of) multi-character, multi-turn conversations
* coding examples in 10 languages from rosettacode.org dataset thanks to Mike aka kryptkpr: https://huggingface.co/datasets/mike-ravkine/rosettacode-parsed
* more roleplay examples
* jokes
* riddles
* all coding instructions have an equivalent " PLAINFORMAT" version now (and all rosettacode examples were trained with PLAINFORMAT)
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora)
The prompt it was trained with was:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
## Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-33b-gpt4-1.4 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
For multi-turn conversations and chatting, you'll want to remove the `--no-history` option.
### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
```
USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
ASSISTANT:
```
### Coding
You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt):
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can optionally add a newline and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js.
PLAINFORMAT
```
### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: C, Pluto
```
### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Jokes
```
USER: Tell me a joke about bananas.
ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E.
```
### Riddles (not great, but slightly better)
```
USER: What has a neck but no head?
ASSISTANT: A bottle.
```
### Multi-character conversations
```
USER: Characters:
- Yoda, the ever clever jedi master.
- Homer Simpson, you know, the goofy yellow guy.
- USER/Jon - don't generate any speech or actions on behalf of Jon
Rules:
- be sure to use the manerisms and speaking style of the characters correctly
- surround character actions with asterisks
- always put quotes around words spoken by the characters
- use a new line for each character's actions or speech
- always prefix actions/speech with the character's name and colon
- only generate text and actions on behalf of Yoda or Homer, and never anyone else
Conversation will revolve around the grapes, in a local cafe with delicious coffee.
Generate a single interaction between Yoda and Homer to start, with one sentence each.
ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer."
Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!"
USER: *enters the cafe* Sorry I'm late guys!
ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes."
Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!"
*Yoda raises an eyebrow*
```
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jdawnduan/ppo-SnowballTarget
|
jdawnduan
| 2023-06-29T18:46:09Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-06-29T18:46:06Z |
---
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: jdawnduan/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
abacaj/Replit-v2-CodeInstruct-3B-ggml
|
abacaj
| 2023-06-29T18:37:22Z | 48 | 33 |
transformers
|
[
"transformers",
"mpt",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-06-27T19:23:36Z |
---
license: other
---
---
This is a [ggml](https://github.com/ggerganov/ggml/) quantized version of [Replit-v2-CodeInstruct-3B](https://huggingface.co/teknium/Replit-v2-CodeInstruct-3B). Quantized to 4bit -> q4_1.
To run inference you can use ggml directly or [ctransformers](https://github.com/marella/ctransformers).
- Memory usage of model: **2GB~**
- Repo to run the model using ctransformers: https://github.com/abacaj/replit-3B-inference
|
sxandie/NER2.0.4-alpha_num_dataset_
|
sxandie
| 2023-06-29T18:18:56Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-29T17:30:08Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sxandie/NER2.0.4-alpha_num_dataset_
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. -->
# sxandie/NER2.0.4-alpha_num_dataset_
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0929
- Validation Loss: 0.1381
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29135, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3110 | 0.1844 | 0 |
| 0.1777 | 0.1544 | 1 |
| 0.1325 | 0.1403 | 2 |
| 0.1088 | 0.1394 | 3 |
| 0.0929 | 0.1381 | 4 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.2.2
- Tokenizers 0.13.3
|
nicole-tumi/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
|
nicole-tumi
| 2023-06-29T17:36:28Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T02:03:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1653
- F1: 0.5513
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1254 | 1.0 | 766 | 1.0806 | 0.5200 |
| 0.9915 | 2.0 | 1532 | 1.0300 | 0.5389 |
| 0.837 | 3.0 | 2298 | 1.0562 | 0.5477 |
| 0.7101 | 4.0 | 3064 | 1.1141 | 0.5480 |
| 0.6132 | 5.0 | 3830 | 1.1653 | 0.5513 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
GEMCorp/poca-SoccerTwos
|
GEMCorp
| 2023-06-29T17:28:44Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-29T17:28:05Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: GEMCorp/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Braen/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
|
Braen
| 2023-06-29T17:24:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-27T16:05:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0679
- F1: 0.5575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0497 | 1.0 | 383 | 1.0726 | 0.5198 |
| 0.9596 | 2.0 | 766 | 1.0286 | 0.5471 |
| 0.8526 | 3.0 | 1149 | 1.0348 | 0.5491 |
| 0.7983 | 4.0 | 1532 | 1.0679 | 0.5575 |
| 0.726 | 5.0 | 1915 | 1.0885 | 0.5506 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
vikiviki/inpa001hass
|
vikiviki
| 2023-06-29T17:20:00Z | 30 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-29T00:44:46Z |
Converted to Diffusers format from https://civitai.com/models/2583?modelVersionId=30191
|
rBlue94/bert-base-spanish-wwm-cased-finetuned-MeIA-AnalisisDeSentimientos
|
rBlue94
| 2023-06-29T17:19:41Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-28T19:27:06Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9217
- F1: 0.5931
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8684 | 1.0 | 766 | 0.9217 | 0.5931 |
| 0.6598 | 2.0 | 1532 | 1.0136 | 0.5930 |
| 0.4408 | 3.0 | 2298 | 1.2285 | 0.5754 |
| 0.2863 | 4.0 | 3064 | 1.4398 | 0.5762 |
| 0.2157 | 5.0 | 3830 | 1.4721 | 0.5812 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Tri1/18-24-finetuned-eng-to-para
|
Tri1
| 2023-06-29T17:05:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-28T10:41:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: 18-24-finetuned-eng-to-para
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. -->
# 18-24-finetuned-eng-to-para
This model is a fine-tuned version of [Tri1/12-18-finetuned-eng-to-para](https://huggingface.co/Tri1/12-18-finetuned-eng-to-para) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3257
- Bleu: 17.0341
- Gen Len: 24.16
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.1696 | 1.0 | 6250 | 0.3216 | 18.5922 | 24.048 |
| 0.1554 | 2.0 | 12500 | 0.3225 | 17.7026 | 23.992 |
| 0.1474 | 3.0 | 18750 | 0.3242 | 17.2459 | 24.176 |
| 0.1387 | 4.0 | 25000 | 0.3243 | 17.3668 | 23.856 |
| 0.1314 | 5.0 | 31250 | 0.3247 | 17.4414 | 24.416 |
| 0.1277 | 6.0 | 37500 | 0.3257 | 17.0341 | 24.16 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
sleepynlp/Reinforce-Pixelcopter-PLE-v0-Leov4
|
sleepynlp
| 2023-06-29T16:55:36Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:55:33Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0-Leov4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.60 +/- 19.02
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
sleepynlp/Reinforce-Pixelcopter-PLE-v0-Leov3
|
sleepynlp
| 2023-06-29T16:46:32Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:46:30Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0-Leov3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.70 +/- 26.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
sleepynlp/Reinforce-Pixelcopter-PLE-v0-Leov2
|
sleepynlp
| 2023-06-29T16:45:16Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:32:41Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0-Leov2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.60 +/- 14.74
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
GretaClementi96/blip2-opt-2.7b-inbreast-clahe-captions-adapters
|
GretaClementi96
| 2023-06-29T16:43:24Z | 20 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-29T06:41:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
cleanrl/InvertedPendulum-v2-ddpg_continuous_action_jax-seed1
|
cleanrl
| 2023-06-29T16:36:43Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"InvertedPendulum-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:36:17Z |
---
tags:
- InvertedPendulum-v2
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulum-v2
type: InvertedPendulum-v2
metrics:
- type: mean_reward
value: 709.70 +/- 443.44
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **InvertedPendulum-v2**
This is a trained model of a DDPG agent playing InvertedPendulum-v2.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action_jax]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action_jax --env-id InvertedPendulum-v2
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-ddpg_continuous_action_jax-seed1/raw/main/ddpg_continuous_action_jax.py
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-ddpg_continuous_action_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v2-ddpg_continuous_action_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action_jax.py --track --capture-video --save-model --hf-entity cleanrl --upload-mode --env-id InvertedPendulum-v2 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'env_id': 'InvertedPendulum-v2',
'exp_name': 'ddpg_continuous_action_jax',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Abdurahim/ppo-LunarLander-v2
|
Abdurahim
| 2023-06-29T16:34:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:34:35Z |
---
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: 274.68 +/- 21.26
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
...
```
|
andinho/falcon-7b-sharded-bf16_tp_v2
|
andinho
| 2023-06-29T16:28:58Z | 31 | 0 |
peft
|
[
"peft",
"text-generation",
"region:us"
] |
text-generation
| 2023-06-29T16:22:08Z |
---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
### Notes:
fine-tuning parameters:
- epochs: 1 (default)
- learning_rate: 1e-4 (default)
---
license: apache-2.0
---
|
lindarz/distilbert-base-uncased-finetuned-imdb
|
lindarz
| 2023-06-29T16:28:09Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-29T16:20:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7065 | 1.0 | 157 | 2.4871 |
| 2.5768 | 2.0 | 314 | 2.4230 |
| 2.5252 | 3.0 | 471 | 2.4356 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.2
|
eddyyeo/Taxi-v3
|
eddyyeo
| 2023-06-29T16:26:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T16:26:27Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="eddyyeo/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Estefanox17/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
|
Estefanox17
| 2023-06-29T16:20:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T02:56:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0264
- F1: 0.5411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0858 | 1.0 | 766 | 1.0418 | 0.5271 |
| 0.9613 | 2.0 | 1532 | 1.0264 | 0.5411 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
SHENMU007/neunit_BASE_V10.13
|
SHENMU007
| 2023-06-29T16:12:14Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-06-29T13:10:58Z |
---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DarkRodry/q-FrozenLake-v1-8x8-noSlippery
|
DarkRodry
| 2023-06-29T16:08:49Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:59:15Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DarkRodry/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
cleanrl/Walker2d-v2-ddpg_continuous_action_jax-seed1
|
cleanrl
| 2023-06-29T15:52:10Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Walker2d-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:51:47Z |
---
tags:
- Walker2d-v2
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v2
type: Walker2d-v2
metrics:
- type: mean_reward
value: 2051.42 +/- 1088.20
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Walker2d-v2**
This is a trained model of a DDPG agent playing Walker2d-v2.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action_jax.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action_jax]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action_jax --env-id Walker2d-v2
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Walker2d-v2-ddpg_continuous_action_jax-seed1/raw/main/ddpg_continuous_action_jax.py
curl -OL https://huggingface.co/cleanrl/Walker2d-v2-ddpg_continuous_action_jax-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Walker2d-v2-ddpg_continuous_action_jax-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action_jax.py --track --capture-video --save-model --hf-entity cleanrl --upload-mode --env-id Walker2d-v2 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'env_id': 'Walker2d-v2',
'exp_name': 'ddpg_continuous_action_jax',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
andinho/falcon-7b-sharded-bf16_tp_v1
|
andinho
| 2023-06-29T15:51:39Z | 29 | 0 |
peft
|
[
"peft",
"text-generation",
"region:us"
] |
text-generation
| 2023-06-28T08:37:22Z |
---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
### Notes:
fine-tuning parameters:
- epochs: 3 (default)
- learning_rate: 2e-4 (default)
|
mgmeskill/old-pixelcopter
|
mgmeskill
| 2023-06-29T15:45:34Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:45:31Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.10 +/- 13.12
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
freya321/ppo-LunarLander-v2
|
freya321
| 2023-06-29T15:38:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:38:27Z |
---
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: 264.90 +/- 13.23
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
...
```
|
mtreviso/roberta-base-imdb
|
mtreviso
| 2023-06-29T15:34:05Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:imdb",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-17T00:26:22Z |
---
language:
- en
thumbnail: null
tags:
- text-classification
license: mit
datasets:
- imdb
metrics: null
duplicated_from: aychang/roberta-base-imdb
---
Forked from: https://huggingface.co/aychang/roberta-base-imdb
# IMDB Sentiment Task: roberta-base
## Model description
A simple base roBERTa model trained on the "imdb" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Use pipeline
from transformers import pipeline
model_name = "aychang/roberta-base-imdb"
nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
results = nlp(["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."])
```
##### AdaptNLP
```python
from adaptnlp import EasySequenceClassifier
model_name = "aychang/roberta-base-imdb"
texts = ["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."]
classifer = EasySequenceClassifier
results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2)
```
#### Limitations and bias
This is minimal language model trained on a benchmark dataset.
## Training data
IMDB https://huggingface.co/datasets/imdb
## Training procedure
#### Hardware
One V100
#### Hyperparameters and Training Args
```python
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir='./models',
overwrite_output_dir=False,
num_train_epochs=2,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
warmup_steps=500,
weight_decay=0.01,
evaluation_strategy="steps",
logging_dir='./logs',
fp16=False,
eval_steps=800,
save_steps=300000
)
```
## Eval results
```
{'epoch': 2.0,
'eval_accuracy': 0.94668,
'eval_f1': array([0.94603457, 0.94731017]),
'eval_loss': 0.2578844428062439,
'eval_precision': array([0.95762642, 0.93624502]),
'eval_recall': array([0.93472, 0.95864]),
'eval_runtime': 244.7522,
'eval_samples_per_second': 102.144}
```
|
tatiana-merz/m2m100_418M-finetuned-sah-to-feat
|
tatiana-merz
| 2023-06-29T15:33:30Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-29T15:10:48Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: m2m100_418M-finetuned-sah-to-feat
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. -->
# m2m100_418M-finetuned-sah-to-feat
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0308
- Bleu: 4.6161
- Gen Len: 198.5197
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 24 | 2.4936 | 1.8237 | 198.2756 |
| No log | 2.0 | 48 | 2.0218 | 3.342 | 198.8268 |
| No log | 3.0 | 72 | 1.7435 | 3.0434 | 198.874 |
| No log | 4.0 | 96 | 1.5399 | 3.8934 | 198.7953 |
| No log | 5.0 | 120 | 1.3805 | 3.5157 | 198.9685 |
| No log | 6.0 | 144 | 1.2383 | 4.2008 | 198.7559 |
| No log | 7.0 | 168 | 1.1430 | 4.1967 | 198.7244 |
| No log | 8.0 | 192 | 1.0837 | 3.9657 | 198.7874 |
| No log | 9.0 | 216 | 1.0501 | 4.0903 | 198.5354 |
| No log | 10.0 | 240 | 1.0308 | 4.6161 | 198.5197 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
DarkRodry/Taxi-v3-tutorial
|
DarkRodry
| 2023-06-29T15:24:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:24:31Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-tutorial
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DarkRodry/Taxi-v3-tutorial", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Joe-Reze/flan-t5-large-samsum-lora
|
Joe-Reze
| 2023-06-29T15:23:15Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"region:us"
] | null | 2023-06-29T15:21:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: flan-t5-large-samsum-lora
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. -->
# flan-t5-large-samsum-lora
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0008
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.27.2
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3
|
DarkRodry/q-FrozenLake-v1-4x4-noSlippery
|
DarkRodry
| 2023-06-29T15:15:15Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:15:13Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DarkRodry/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Ai-tensa/testLoRAs
|
Ai-tensa
| 2023-06-29T15:11:42Z | 0 | 2 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-07T09:48:57Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Test LoRAs for Waifu Diffusion v1.3
These LoRAs are **experimental** LoRAs for WD1.3 to produce high resolution or different aspect ratio images.
## Model Description
They have fine-tuned from the original WD1.3 or a model merged with LoRA in this repository by thousands of unselected AI illustrations by various authors and models published on the Internet.
Each networks has been fine-tuned with a learning rate of 6.0e-5 for 5 epochs on about 5-8k images at batch size 8, using Aspect Ratio Bucketing with a maximum resolution of 768x768.
Fine tuning performed by RTX3090 at fp16 with AdamW8bit optimizer and took 2-3 hours for each network.
| LoRA Name | Base model | images | note |
| ------------- | -------------------------- | ------ | ------------------------------------- |
| hires_test_a | WD1.3 | ~5k | |
| hires_test_b | WD1.3 | ~7k | |
| hires_test_c | WD1.3 + 1.0 * hires_test_a | ~8k | recommended for use with hires_test_a |
| hires_test_d | WD1.3 | ~5k | dim 128, U-net only |
| smooth_test_a | WD1.3 + 2.0 * hires_test_a | ~7k | |
| smooth_test_b | WD1.3 + 2.0 * hires_test_a | ~7k | different seed |
There is probably no overlap between the three image sets (5k, 7k, 8k).
## Usage
The LoRA are mainly classified into two types: for high-resolution and for smoothing.
First, please apply high resolution LoRA at the preferred ratio: 1-2 is recommended for ~768x768, and the higher the resolution, the more weight is recommended.
In some cases, especially when weights are large, adverse effects may be observed.
In such cases, please consider applying a leveling LoRA.
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Acknowledgements
These LoRAs build on the two excellent works: SD1.4, developed by [CompVis Researchers](https://ommer-lab.com/), and WD1.3, developed by [Anthony Mercurio](https://github.com/harubaru), [Salt](https://github.com/sALTaccount/), and [Cafe](https://twitter.com/cafeai_labs).
|
Ashraf-kasem/RL_taxi
|
Ashraf-kasem
| 2023-06-29T15:05:37Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T15:05:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: RL_taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Ashraf-kasem/RL_taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ichitaka/falcon-40b-instruct-8bit
|
ichitaka
| 2023-06-29T15:01:26Z | 20 | 6 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2023-06-02T12:50:20Z |
---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# ✨ Falcon-40B-Instruct 8Bit
**INFO: This model is the Falcon-40B-Instruct model quantized using bitsandbytes. This saves you around 40 GB of downloads, if you plan to quantize the model anyways. bitsandbytes quantization only supports the GPU, so this will only run with a GPU that can hold the full model.**
**Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
## Why use Falcon-40B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).**
* **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's little brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
# Model Card for Falcon-40B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-40B-Instruct has been finetuned on a chat dataset.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-40B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-40B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data.
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
### Model Architecture and Objective
Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 60 | |
| `d_model` | 8192 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-40B-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon 😊.*
## License
Falcon-40B-Instruct is made available under the Apache 2.0 license.
## Contact
falconllm@tii.ae
|
jorgeortizfuentes/spanish-spellchecker-alpaca-7b
|
jorgeortizfuentes
| 2023-06-29T14:57:14Z | 0 | 0 | null |
[
"generated_from_trainer",
"es",
"dataset:jorgeortizfuentes/spanish_spellchecker_qa_test",
"license:other",
"region:us"
] | null | 2023-06-29T01:34:52Z |
---
language:
- es
license: other
tags:
- generated_from_trainer
datasets:
- jorgeortizfuentes/spanish_spellchecker_qa_test
model-index:
- name: spanish-spellchecker-alpaca-7b
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. -->
# spanish-spellchecker-alpaca-7b
This model is a fine-tuned version of [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) on the jorgeortizfuentes/spanish_spellchecker_qa_test dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- 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: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
clay3d/omnidata
|
clay3d
| 2023-06-29T14:54:39Z | 0 | 4 | null |
[
"region:us"
] | null | 2023-06-28T18:51:33Z |
# omnidata
[Omnidata](https://github.com/EPFL-VILAB/omnidata/tree/main/omnidata_tools/torch) weights for depth and normal prediction for [Stable Dreamfusion](https://github.com/ashawkey/stable-dreamfusion/tree/main).
|
BaoKien/albert-base-v2-finetuned-squad-v2
|
BaoKien
| 2023-06-29T14:53:44Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-28T10:54:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: albert-base-v2-finetuned-squad-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-squad-v2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9645
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.864 | 1.0 | 8248 | 0.8698 |
| 0.6246 | 2.0 | 16496 | 0.8351 |
| 0.4359 | 3.0 | 24744 | 0.9645 |
### Performance
- 'exact': 78.36267160784975,
- 'f1': 81.72483834090231,
- 'total': 11873,
- 'HasAns_exact': 74.527665317139,
- 'HasAns_f1': 81.26164062441536,
- 'HasAns_total': 5928,
- 'NoAns_exact': 82.18671152228764,
- 'NoAns_f1': 82.18671152228764,
- 'NoAns_total': 5945,
- 'best_exact': 78.36267160784975,
- 'best_exact_thresh': 0.9990501403808594,
- 'best_f1': 81.72483834090268,
- 'best_f1_thresh': 0.9990501403808594,
- 'total_time_in_seconds': 224.37217425400013,
- 'samples_per_second': 52.9165438605555,
- 'latency_in_seconds': 0.018897681651983505
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ymkgr/Re_Stage-Tsukisaka_Sayu
|
ymkgr
| 2023-06-29T14:50:19Z | 0 | 2 | null |
[
"anime",
"game",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-29T12:16:16Z |
---
license: creativeml-openrail-m
metrics:
- character
tags:
- anime
- game
---
Model type: LoRA
---
Model Details:
- from Japanese multimedia project: Re:Stage! - Unit: KiRaRe - character name: Tsukisaka Sayu./来自 日本多媒体企划：Re:Stage! - 组合：KiRaRe - 角色名：月坂纱由。
- LoRA weight: 0.6-1
- Trigger Words:
- stage dress: tsukisaka sayu\(re:stage\), green eyes, side ponytail, long hair, purple hair, dress\(tssa\), necklace\(tssa\), thighhighs\(tssa\), star white scrunchie\(tssa\), star hair ornament\(tssa\), wrist cuffs\(tssa\), boots\(tssa\),
- school uniform: tsukisaka sayu\(re:stage\), green eyes, side ponytail, long hair, purple hair, sailor collar, blue skirt,
- The symbol \ should be added before "(" and ")". It is not possible to directly input them together in the file introduction.(Only supplementary to the trigger words mentioned above)
- Optional trigger words: bowtie, "school uniform and serafuku" have the same effect as "sailor color". "Hair ribbon" is her usual trigger word for hair ribbon. When the default hairstyle is side ponytail, there is no need to add it. If you want her to continue using her usual hair ribbon on hairstyles such as "twintails", you can add it.
- If you want to change her hairstyle, it's best to add 'ponytail' to 'Negative prompt'.
- I don't know English and I'm not very good at using the Hugging Face website. I also use a translation for the description
- Demo:
---
I also made LoRA for "shikimiya mana", but I plan to update its version soon, so I will upload it later. Afterwards, I also want to gradually produce LoRA for all members of "Re: Stage!".
Please comply with regulations.
|
VeronicaVAX/nubes
|
VeronicaVAX
| 2023-06-29T14:45:59Z | 0 | 0 |
asteroid
|
[
"asteroid",
"text-to-image",
"es",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:tiiuae/falcon-refinedweb",
"dataset:GAIR/lima",
"dataset:cerebras/SlimPajama-627B",
"dataset:QingyiSi/Alpaca-CoT",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:OpenAssistant/oasst1",
"dataset:anon8231489123/ShareGPT_Vicuna_unfiltered",
"dataset:databricks/databricks-dolly-15k",
"dataset:TigerResearch/pretrain_zh",
"license:artistic-2.0",
"region:us"
] |
text-to-image
| 2023-06-29T14:41:42Z |
---
license: artistic-2.0
datasets:
- fka/awesome-chatgpt-prompts
- tiiuae/falcon-refinedweb
- GAIR/lima
- cerebras/SlimPajama-627B
- QingyiSi/Alpaca-CoT
- WizardLM/WizardLM_evol_instruct_V2_196k
- OpenAssistant/oasst1
- anon8231489123/ShareGPT_Vicuna_unfiltered
- databricks/databricks-dolly-15k
- TigerResearch/pretrain_zh
language:
- es
- en
metrics:
- code_eval
library_name: asteroid
pipeline_tag: text-to-image
---
|
BuddhikaWeerasinghe/videomae-base-finetuned-ucf101-subset-nimeshbuddhika
|
BuddhikaWeerasinghe
| 2023-06-29T14:42:05Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-06-29T14:08:18Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset-nimeshbuddhika
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. -->
# videomae-base-finetuned-ucf101-subset-nimeshbuddhika
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1097
- Accuracy: 0.9731
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4285 | 0.1 | 150 | 2.1186 | 0.2032 |
| 0.9154 | 1.1 | 300 | 1.1913 | 0.6043 |
| 0.4498 | 2.1 | 450 | 0.3770 | 0.8824 |
| 0.3012 | 3.1 | 600 | 0.3904 | 0.8663 |
| 0.1458 | 4.1 | 750 | 0.8938 | 0.7968 |
| 0.0464 | 5.1 | 900 | 0.6181 | 0.8556 |
| 0.0274 | 6.1 | 1050 | 0.7182 | 0.8075 |
| 0.0022 | 7.1 | 1200 | 0.4525 | 0.8717 |
| 0.0302 | 8.1 | 1350 | 0.3172 | 0.9091 |
| 0.0166 | 9.1 | 1500 | 0.2898 | 0.8984 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
asti339/emotions
|
asti339
| 2023-06-29T14:37:25Z | 1 | 1 |
tf-keras
|
[
"tf-keras",
"image-classification",
"region:us"
] |
image-classification
| 2023-06-24T12:33:25Z |
---
pipeline_tag: image-classification
---
|
username93/8C_ML_U2_P_RL_Huggy
|
username93
| 2023-06-29T14:33:29Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-29T14:33:07Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: username93/8C_ML_U2_P_RL_Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AAOBA/ppo-Huggy
|
AAOBA
| 2023-06-29T14:32:27Z | 17 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-29T13:52:11Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: chikoto/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Taurine511/distilbert-base-uncased-finetuned-emotion
|
Taurine511
| 2023-06-29T14:28:50Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T13:44:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9165
- name: F1
type: f1
value: 0.9167227221544503
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2222
- Accuracy: 0.9165
- F1: 0.9167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8 | 1.0 | 250 | 0.3127 | 0.9005 | 0.8977 |
| 0.2446 | 2.0 | 500 | 0.2222 | 0.9165 | 0.9167 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
mcamara/ppo-Huggy
|
mcamara
| 2023-06-29T14:20:57Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-29T14:20:52Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mcamara/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
amm297/aux
|
amm297
| 2023-06-29T14:18:38Z | 34 | 0 |
peft
|
[
"peft",
"text-generation",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T11:22:02Z |
---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
sert121/falcon_spider_v1
|
sert121
| 2023-06-29T13:48:55Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2023-06-29T10:24:38Z |
---
tags:
- generated_from_trainer
model-index:
- name: falcon_spider_v1
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. -->
# falcon_spider_v1
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5862
The repo contains the adapters for the model, as well as the tokenizer files.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1754 | 0.03 | 10 | 0.6316 |
| 0.5011 | 0.05 | 20 | 0.4848 |
| 0.4355 | 0.08 | 30 | 0.4586 |
| 0.3921 | 0.1 | 40 | 0.4555 |
| 0.3327 | 0.13 | 50 | 0.4674 |
| 0.2662 | 0.15 | 60 | 0.4507 |
| 0.2258 | 0.18 | 70 | 0.4813 |
| 0.1569 | 0.2 | 80 | 0.5407 |
| 0.1313 | 0.23 | 90 | 0.5924 |
| 0.1215 | 0.25 | 100 | 0.5862 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dar-tau/Reinforce-Pixelcopter-PLE-v0
|
dar-tau
| 2023-06-29T13:38:53Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T13:24:04Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.80 +/- 8.77
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
iammartian0/sentiment_analysis_model
|
iammartian0
| 2023-06-29T13:35:40Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T12:30:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: sentiment_analysis_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9314
---
<!-- 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. -->
# sentiment_analysis_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1964
- Accuracy: 0.9314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2524 | 1.0 | 782 | 0.1844 | 0.9291 |
| 0.1377 | 2.0 | 1564 | 0.1964 | 0.9314 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ricardoseifert/alpaca-bitcoin-tweets-sentiment
|
ricardoseifert
| 2023-06-29T13:28:39Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-29T13:28:38Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
blackstone/spkrec-ecapa-cnceleb
|
blackstone
| 2023-06-29T13:19:09Z | 0 | 0 |
speechbrain
|
[
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"audio-classification",
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2023-06-29T12:42:49Z |
---
language: en
thumbnail: null
tags:
- speechbrain
- embeddings
- Speaker
- Verification
- Identification
- pytorch
- ECAPA
- TDNN
license: apache-2.0
datasets:
- voxceleb
metrics:
- EER
widget:
- example_title: VoxCeleb Speaker id10003
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
pipeline_tag: audio-classification
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN on CNCeleb
This repository a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as well.
It is trained on CNCeleb1 + CNCeleb2 training data.
The model performance on CNCeleb1-test set(Cleaned) is:
| Release | EER(%) | MinDCF(p=0.01) |
|:-------------:|:--------------:|:--------------:|
| 15-05-22 | 8.44 | 0.4587 |
## Pipeline description
This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
You can find our training results (models, logs, etc) [here]().
### Compute your speaker embeddings
```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="blackstone/spkrec-ecapa-cnceleb")
signal, fs = torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
embeddings = classifier.encode_batch(signal)
```
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
### Perform Speaker Verification
```python
from speechbrain.pretrained import SpeakerRecognition
verification = SpeakerRecognition.from_hparams(source="blackstone/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-cnceleb")
score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk2_snt1.wav") # Different Speakers
score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk1_snt2.wav") # Same Speaker
```
The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
#### References
```
@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
author = {Brecht Desplanques and
Jenthe Thienpondt and
Kris Demuynck},
editor = {Helen Meng and
Bo Xu and
Thomas Fang Zheng},
title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
in {TDNN} Based Speaker Verification},
booktitle = {Interspeech 2020},
pages = {3830--3834},
publisher = {{ISCA}},
year = {2020},
}
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
yeyi9/Sadtalker
|
yeyi9
| 2023-06-29T13:18:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"zh",
"dataset:Samhita/SadTalkerData",
"arxiv:1910.09700",
"license:afl-3.0",
"region:us"
] | null | 2023-06-29T13:12:57Z |
---
license: afl-3.0
datasets:
- Samhita/SadTalkerData
language:
- zh
metrics:
- accuracy
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sleepynlp/Reinforce-CartPole-v1-LEO
|
sleepynlp
| 2023-06-29T13:02:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T13:02:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-LEO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 468.60 +/- 94.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BolaOK/first_Generator
|
BolaOK
| 2023-06-29T12:52:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-29T12:52:38Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
cgutknecht/gelectra_large_gsqd-gq-LHM
|
cgutknecht
| 2023-06-29T12:52:17Z | 115 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"electra",
"question-answering",
"de",
"dataset:squad",
"dataset:deepset/germanquad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-05T09:41:43Z |
---
license: mit
datasets:
- squad
- deepset/germanquad
language:
- de
---
# Overview
German QA-Model finetuned on Question-Answer-Pairs for Bürgerbüro-Service-Documents
**Base model:** deepset/gelectra-large
**Finetuning** in sequential steps on:
1. Machine-translated (en->de) SQuAD 1.0
2. GermanQuAD: deepset/germanquad
3. Custom LHM-QA-Dataset (>reference following<)
**Evaluation:** Reaches a performance of 70,0 F1-Score on LHM-QA-testdata
|
ahishamm/vit-huge-modified-augmented-ph2-patch-14
|
ahishamm
| 2023-06-29T12:50:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T12:27:18Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-huge-modified-augmented-ph2-patch-14
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. -->
# vit-huge-modified-augmented-ph2-patch-14
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/Modified_Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Accuracy: 1.0
- Recall: 1.0
- F1: 1.0
- Precision: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0996 | 0.29 | 50 | 0.1378 | 0.9366 | 0.9366 | 0.9366 | 0.9366 |
| 0.0096 | 0.59 | 100 | 0.0509 | 0.9743 | 0.9743 | 0.9743 | 0.9743 |
| 0.0049 | 0.88 | 150 | 0.0085 | 0.9983 | 0.9983 | 0.9983 | 0.9983 |
| 0.0029 | 1.18 | 200 | 0.0037 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0022 | 1.47 | 250 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 1.76 | 300 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 2.06 | 350 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 2.35 | 400 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.65 | 450 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.94 | 500 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 3.24 | 550 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 3.53 | 600 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 3.82 | 650 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sjdata/distilhubert-finetuned-gtzan
|
sjdata
| 2023-06-29T12:43:44Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-06-29T11:06:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9253
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3972 | 1.0 | 450 | 1.4662 | 0.65 |
| 0.7118 | 2.0 | 900 | 0.9103 | 0.69 |
| 0.4653 | 3.0 | 1350 | 0.8097 | 0.73 |
| 0.934 | 4.0 | 1800 | 0.7674 | 0.83 |
| 0.3231 | 5.0 | 2250 | 1.2025 | 0.73 |
| 0.0038 | 6.0 | 2700 | 1.1013 | 0.8 |
| 0.002 | 7.0 | 3150 | 0.8540 | 0.86 |
| 0.0022 | 8.0 | 3600 | 0.8067 | 0.85 |
| 0.0013 | 9.0 | 4050 | 0.8682 | 0.86 |
| 0.0016 | 10.0 | 4500 | 0.9253 | 0.84 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ahishamm/vit-large-modified-augmented-ph2-patch-32
|
ahishamm
| 2023-06-29T12:26:49Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T12:12:08Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-large-modified-augmented-ph2-patch-32
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. -->
# vit-large-modified-augmented-ph2-patch-32
This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the ahishamm/Modified_Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 1.0
- Recall: 1.0
- F1: 1.0
- Precision: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1255 | 0.29 | 50 | 0.1555 | 0.9538 | 0.9538 | 0.9538 | 0.9538 |
| 0.0875 | 0.59 | 100 | 0.0656 | 0.9726 | 0.9726 | 0.9726 | 0.9726 |
| 0.0612 | 0.88 | 150 | 0.0219 | 0.9949 | 0.9949 | 0.9949 | 0.9949 |
| 0.0034 | 1.18 | 200 | 0.0031 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0021 | 1.47 | 250 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 1.76 | 300 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 2.06 | 350 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 2.35 | 400 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 2.65 | 450 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 2.94 | 500 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 3.24 | 550 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 3.53 | 600 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 3.82 | 650 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NickyNicky/mpt-7b-chat-Peft-h2ogpt_oig_oasst1_instruct-gpt4all-max_length_3072-V1
|
NickyNicky
| 2023-06-29T12:18:00Z | 2 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-29T12:17:53Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
ahishamm/vit-large-modified-augmented-ph2-patch-16
|
ahishamm
| 2023-06-29T12:11:48Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T11:56:38Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-large-modified-augmented-ph2-patch-16
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. -->
# vit-large-modified-augmented-ph2-patch-16
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the ahishamm/Modified_Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0827
- Accuracy: 0.9709
- Recall: 0.9709
- F1: 0.9709
- Precision: 0.9709
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3402 | 0.29 | 50 | 0.6269 | 0.7945 | 0.7945 | 0.7945 | 0.7945 |
| 0.1387 | 0.59 | 100 | 0.2957 | 0.8921 | 0.8921 | 0.8921 | 0.8921 |
| 0.2921 | 0.88 | 150 | 0.3157 | 0.8836 | 0.8836 | 0.8836 | 0.8836 |
| 0.1268 | 1.18 | 200 | 0.4557 | 0.8527 | 0.8527 | 0.8527 | 0.8527 |
| 0.2071 | 1.47 | 250 | 0.2690 | 0.8818 | 0.8818 | 0.8818 | 0.8818 |
| 0.1238 | 1.76 | 300 | 0.2999 | 0.9178 | 0.9178 | 0.9178 | 0.9178 |
| 0.1327 | 2.06 | 350 | 0.6026 | 0.7877 | 0.7877 | 0.7877 | 0.7877 |
| 0.1453 | 2.35 | 400 | 0.2887 | 0.8990 | 0.8990 | 0.8990 | 0.8990 |
| 0.0686 | 2.65 | 450 | 0.2049 | 0.9503 | 0.9503 | 0.9503 | 0.9503 |
| 0.0414 | 2.94 | 500 | 0.3040 | 0.9195 | 0.9195 | 0.9195 | 0.9195 |
| 0.0851 | 3.24 | 550 | 0.2244 | 0.9298 | 0.9298 | 0.9298 | 0.9298 |
| 0.0054 | 3.53 | 600 | 0.1356 | 0.9555 | 0.9555 | 0.9555 | 0.9555 |
| 0.0029 | 3.82 | 650 | 0.0827 | 0.9709 | 0.9709 | 0.9709 | 0.9709 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jcnecio/ppo-LunarLander-v2-v2
|
jcnecio
| 2023-06-29T12:09:07Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T12:07:11Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -154.39 +/- 57.59
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jcnecio/ppo-LunarLander-v2-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
ahishamm/vit-base-modified-augmented-ph2-patch-32
|
ahishamm
| 2023-06-29T11:56:18Z | 194 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T11:47:09Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-modified-augmented-ph2-patch-32
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. -->
# vit-base-modified-augmented-ph2-patch-32
This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/Modified_Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0014
- Accuracy: 1.0
- Recall: 1.0
- F1: 1.0
- Precision: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1463 | 0.29 | 50 | 0.2883 | 0.8990 | 0.8990 | 0.8990 | 0.8990 |
| 0.0861 | 0.59 | 100 | 0.1700 | 0.9469 | 0.9469 | 0.9469 | 0.9469 |
| 0.155 | 0.88 | 150 | 0.1299 | 0.9555 | 0.9555 | 0.9555 | 0.9555 |
| 0.0188 | 1.18 | 200 | 0.1214 | 0.9623 | 0.9623 | 0.9623 | 0.9623 |
| 0.0335 | 1.47 | 250 | 0.0261 | 0.9932 | 0.9932 | 0.9932 | 0.9932 |
| 0.003 | 1.76 | 300 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0023 | 2.06 | 350 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.002 | 2.35 | 400 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 2.65 | 450 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 2.94 | 500 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 3.24 | 550 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 3.53 | 600 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 3.82 | 650 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
wannaphong/BELA
|
wannaphong
| 2023-06-29T11:51:30Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2023-06-28T08:27:35Z |
---
license: mit
---
# Bi-encoder Entity Linking Architecture (BELA)
This hub host BELA model that download from [Bi-encoder Entity Linking Architecture (BELA)](https://github.com/facebookresearch/BELA).
|
ahishamm/vit-base-modified-augmented-ph2-patch-16
|
ahishamm
| 2023-06-29T11:46:52Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T11:37:12Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-modified-augmented-ph2-patch-16
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. -->
# vit-base-modified-augmented-ph2-patch-16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/Modified_Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- Recall: 1.0
- F1: 1.0
- Precision: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1238 | 0.29 | 50 | 0.1973 | 0.9332 | 0.9332 | 0.9332 | 0.9332 |
| 0.1857 | 0.59 | 100 | 0.1084 | 0.9623 | 0.9623 | 0.9623 | 0.9623 |
| 0.2506 | 0.88 | 150 | 0.0773 | 0.9692 | 0.9692 | 0.9692 | 0.9692 |
| 0.0247 | 1.18 | 200 | 0.1158 | 0.9606 | 0.9606 | 0.9606 | 0.9606 |
| 0.0089 | 1.47 | 250 | 0.0162 | 0.9914 | 0.9914 | 0.9914 | 0.9914 |
| 0.0226 | 1.76 | 300 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0261 | 2.06 | 350 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 2.35 | 400 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 2.65 | 450 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 2.94 | 500 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 3.24 | 550 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 3.53 | 600 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 3.82 | 650 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
T-Systems-onsite/cross-en-de-pt-roberta-sentence-transformer
|
T-Systems-onsite
| 2023-06-29T11:45:43Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence_embedding",
"en",
"de",
"pt",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- en
- de
- pt
license: mit
tags:
- sentence_embedding
---
|
qPilz/ppo-Huggy
|
qPilz
| 2023-06-29T11:42:45Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-29T11:42:44Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: qPilz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
GabrielNewell/ppo-Huggy
|
GabrielNewell
| 2023-06-29T11:42:04Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-29T11:42:00Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: GabrielNewell/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
poisson-fish/ultralm-13b-GPTQ
|
poisson-fish
| 2023-06-29T11:40:49Z | 10 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"dataset:stingning/ultrachat",
"arxiv:2305.14233",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T08:29:08Z |
---
datasets:
- stingning/ultrachat
---
This is [openbmb/UltraLM-13b](https://huggingface.co/openbmb/UltraLM-13b) recovered with [huggyllama/llama-13b](https://huggingface.co/huggyllama/llama-13b) and quantized to 4bit GPTQ with the following config:
```python
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=32,
desc_act=True,
)
```
# Original Model Card:
# UltraLM-13b
<!-- Provide a quick summary of what the model is/does. -->
This is UltraLM-13b delta weights, a chat language model trained upon [UltraChat](https://github.com/thunlp/UltraChat)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
The model is fine-tuned based on LLaMA-13b with a multi-turn chat-format template as below
```
User: instruction 1<eos_token>
Assistant: response 1<eos_token>
User: instruction 2<eos_token>
Assistant: response 2<eos_token>
...
```
- **License:** UltraLM is based on LLaMA and should be used under LLaMA's [model license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
- **Finetuned from model:** LLaMA-13b
- **Finetuned on data:** [UltraChat](https://github.com/thunlp/UltraChat)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [UltraChat](https://github.com/thunlp/UltraChat)
- **Paper:** [arxiv](https://arxiv.org/abs/2305.14233)
- **Demo:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
To use this model, you need to [recover](https://github.com/thunlp/UltraChat/tree/main/UltraLM) the full model from the delta weights and perform inference following the template below:
```
[Optional]User: system prompt<eos_token>
User: user input<eos_token>
Assistant:
```
|
desh2608/icefall-surt-libricss-dprnn-zipformer
|
desh2608
| 2023-06-29T11:33:35Z | 0 | 2 | null |
[
"tensorboard",
"en",
"dataset:libricss_surt",
"license:apache-2.0",
"region:us"
] | null | 2023-06-13T13:58:44Z |
---
license: apache-2.0
datasets:
- libricss_surt
language:
- en
metrics:
- orc-wer
---
# LibriCSS dprnn_zipformer
This model is based on the icefall `dprnn_zipformer` recipe under `egs/libricss/SURT`.
For details, refer to the README.md in the recipe.
## Performance Record
The following results are obtained using the adapted models using modified beam search
with a beam size of 4.
#### IHM-Mix
| Model | # params | 0L | 0S | OV10 | OV20 | OV30 | OV40 | Avg. |
|------------|:-------:|:----:|:---:|----:|:----:|:----:|:----:|:----:|
| dprnn_zipformer (base) | 26.7 | 5.1 | 4.2 | 13.7 | 18.7 | 20.5 | 20.6 | 13.8 |
| dprnn_zipformer (large) | 37.9 | 4.6 | 3.8 | 12.7 | 14.3 | 16.7 | 21.2 | 12.2 |
#### SDM
| Model | # params | 0L | 0S | OV10 | OV20 | OV30 | OV40 | Avg. |
|------------|:-------:|:----:|:---:|----:|:----:|:----:|:----:|:----:|
| dprnn_zipformer (base) | 26.7 | 6.8 | 7.2 | 21.4 | 24.5 | 28.6 | 31.2 | 20.0 |
| dprnn_zipformer (large) | 37.9 | 6.4 | 6.9 | 17.9 | 19.7 | 25.2 | 25.5 | 16.9 |
|
ahishamm/vit-base-augmented-ph2-patch-16
|
ahishamm
| 2023-06-29T11:30:47Z | 206 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T11:21:44Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-augmented-ph2-patch-16
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. -->
# vit-base-augmented-ph2-patch-16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/Augmented_PH2_db_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5420
- Accuracy: 0.8444
- Recall: 0.8444
- F1: 0.8444
- Precision: 0.8444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0592 | 0.36 | 50 | 0.7161 | 0.8068 | 0.8068 | 0.8068 | 0.8068 |
| 0.0703 | 0.72 | 100 | 0.5420 | 0.8444 | 0.8444 | 0.8444 | 0.8444 |
| 0.0042 | 1.09 | 150 | 0.5557 | 0.8821 | 0.8821 | 0.8821 | 0.8821 |
| 0.0034 | 1.45 | 200 | 0.6464 | 0.8701 | 0.8701 | 0.8701 | 0.8701 |
| 0.0023 | 1.81 | 250 | 0.7943 | 0.8410 | 0.8410 | 0.8410 | 0.8410 |
| 0.0018 | 2.17 | 300 | 0.7109 | 0.8598 | 0.8598 | 0.8598 | 0.8598 |
| 0.0015 | 2.54 | 350 | 0.7254 | 0.8598 | 0.8598 | 0.8598 | 0.8598 |
| 0.0013 | 2.9 | 400 | 0.7364 | 0.8598 | 0.8598 | 0.8598 | 0.8598 |
| 0.0013 | 3.26 | 450 | 0.7438 | 0.8615 | 0.8615 | 0.8615 | 0.8615 |
| 0.0012 | 3.62 | 500 | 0.7489 | 0.8615 | 0.8615 | 0.8615 | 0.8615 |
| 0.0012 | 3.99 | 550 | 0.7506 | 0.8615 | 0.8615 | 0.8615 | 0.8615 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jvvelzen/Yaxi-v3_3
|
jvvelzen
| 2023-06-29T11:28:02Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T11:28:00Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Yaxi-v3_3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jvvelzen/Yaxi-v3_3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
SRDdev/MarathiSampark
|
SRDdev
| 2023-06-29T11:24:23Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-27T17:27:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: MarathiSampark
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. -->
# MarathiSampark
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mr-en](https://huggingface.co/Helsinki-NLP/opus-mt-mr-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8468
- Bleu: 0.1653
- Gen Len: 3.5172
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 197 | 3.8495 | 0.1734 | 3.4637 |
| No log | 2.0 | 394 | 3.8468 | 0.1653 | 3.5172 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
mcamara/ppo-LunarLander-v2
|
mcamara
| 2023-06-29T11:05:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T11:05:31Z |
---
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: 256.10 +/- 18.47
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
...
```
|
linxxx3/test-model
|
linxxx3
| 2023-06-29T10:59:53Z | 0 | 0 |
transformers
|
[
"transformers",
"mytag:1",
"license:artistic-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-28T07:32:21Z |
---
license: artistic-2.0
tags:
- mytag:1
library_name: transformers
---
|
ahishamm/vit-large-isic-sharpened-patch-32
|
ahishamm
| 2023-06-29T10:56:33Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T10:50:53Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-large-isic-sharpened-patch-32
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. -->
# vit-large-isic-sharpened-patch-32
This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the ahishamm/isic_sharpened_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6395
- Accuracy: 0.7778
- Recall: 0.7778
- F1: 0.7778
- Precision: 0.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
monkirai/FisioSalutValles
|
monkirai
| 2023-06-29T10:51:33Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-06-29T10:50:17Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ahishamm/vit-large-isic-sharpened-patch-16
|
ahishamm
| 2023-06-29T10:50:35Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-29T10:44:56Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-large-isic-sharpened-patch-16
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. -->
# vit-large-isic-sharpened-patch-16
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the ahishamm/isic_sharpened_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6853
- Accuracy: 0.75
- Recall: 0.75
- F1: 0.75
- Precision: 0.75
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
oplatek/falcon-7b-instruct-multi_woz_22-t2t
|
oplatek
| 2023-06-29T10:38:19Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T09:32:11Z |
### TRAINING LOG
wandb: Run history:
wandb: eval/loss █▆▅▄▃▃▂▂▁▁▁
wandb: eval/runtime ▁▃▂▃▃▃▃█▃▄▁
wandb: eval/samples_per_second █▆▇▆▆▆▆▁▆▄█
wandb: eval/steps_per_second █▆▇▆▆▆▆▁▆▄█
wandb: train/epoch ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: train/loss █▄▄▅▃▅▃▃▄▅▃▃▃▄▃▃▃▃▂▂▂▂▃▂▄▂▃▂▂▂▂▂▃▂▁▃▂▂▂▁
wandb: train/total_flos ▁
wandb: train/train_loss ▁
wandb: train/train_runtime ▁
wandb: train/train_samples_per_second ▁
wandb: train/train_steps_per_second ▁
wandb:
wandb: Run summary:
wandb: eval/loss 0.27314
wandb: eval/runtime 129.6563
wandb: eval/samples_per_second 7.713
wandb: eval/steps_per_second 7.713
wandb: train/epoch 0.53
wandb: train/global_step 1875
wandb: train/learning_rate 0.0002
wandb: train/loss 0.258
wandb: train/total_flos 1.9547706216175334e+17
wandb: train/train_loss 0.30445
wandb: train/train_runtime 13368.3721
wandb: train/train_samples_per_second 2.244
wandb: train/train_steps_per_second 0.14
wandb:
wandb: 🚀 View run happy-deluge-17 at: https://wandb.ai/metric/llm_finetune_multiwoz22.sh/runs/4epf9h85
### INFERENCE LOG
TODO
|
NasimB/gpt2-dp-cl-rarity-2
|
NasimB
| 2023-06-29T10:34:58Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T08:16:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-dp-cl-rarity-2
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-dp-cl-rarity-2
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: 4.8538
## 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.8154 | 0.29 | 500 | 5.8625 |
| 5.5131 | 0.58 | 1000 | 5.4712 |
| 5.1777 | 0.87 | 1500 | 5.2785 |
| 4.9215 | 1.15 | 2000 | 5.1669 |
| 4.771 | 1.44 | 2500 | 5.0527 |
| 4.6814 | 1.73 | 3000 | 4.9900 |
| 4.5856 | 2.02 | 3500 | 4.9580 |
| 4.3933 | 2.31 | 4000 | 4.9329 |
| 4.3697 | 2.6 | 4500 | 4.8822 |
| 4.336 | 2.89 | 5000 | 4.8406 |
| 4.1637 | 3.18 | 5500 | 4.8717 |
| 4.0936 | 3.46 | 6000 | 4.8500 |
| 4.0831 | 3.75 | 6500 | 4.8168 |
| 4.028 | 4.04 | 7000 | 4.8076 |
| 3.8111 | 4.33 | 7500 | 4.8192 |
| 3.8157 | 4.62 | 8000 | 4.8051 |
| 3.8032 | 4.91 | 8500 | 4.7920 |
| 3.6328 | 5.2 | 9000 | 4.8242 |
| 3.5632 | 5.48 | 9500 | 4.8342 |
| 3.5601 | 5.77 | 10000 | 4.8337 |
| 3.5208 | 6.06 | 10500 | 4.8434 |
| 3.4092 | 6.35 | 11000 | 4.8511 |
| 3.4088 | 6.64 | 11500 | 4.8524 |
| 3.4029 | 6.93 | 12000 | 4.8538 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
TiptopBin/sagemaker-distilbert-base-uncased
|
TiptopBin
| 2023-06-29T10:34:23Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T10:26:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sagemaker-distilbert-base-uncased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9288
- name: F1
type: f1
value: 0.9292387199363944
- name: Precision
type: precision
value: 0.9342525979216627
- name: Recall
type: recall
value: 0.924278370897588
---
<!-- 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. -->
# sagemaker-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1860
- Accuracy: 0.9288
- F1: 0.9292
- Precision: 0.9343
- Recall: 0.9243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3455 | 1.0 | 782 | 0.1860 | 0.9288 | 0.9292 | 0.9343 | 0.9243 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
NasimB/gpt2-dp-cl-length-2
|
NasimB
| 2023-06-29T10:31:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T08:13:03Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-dp-cl-length-2
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-dp-cl-length-2
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: 4.6978
## 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7438 | 0.28 | 500 | 5.8628 |
| 5.3832 | 0.57 | 1000 | 5.4721 |
| 5.0548 | 0.85 | 1500 | 5.2463 |
| 4.7966 | 1.14 | 2000 | 5.0887 |
| 4.6482 | 1.42 | 2500 | 4.9869 |
| 4.5475 | 1.7 | 3000 | 4.9166 |
| 4.4753 | 1.99 | 3500 | 4.8238 |
| 4.2612 | 2.27 | 4000 | 4.8195 |
| 4.2415 | 2.56 | 4500 | 4.7798 |
| 4.2024 | 2.84 | 5000 | 4.7139 |
| 4.0709 | 3.12 | 5500 | 4.7122 |
| 3.9548 | 3.41 | 6000 | 4.7128 |
| 3.9485 | 3.69 | 6500 | 4.6607 |
| 3.9265 | 3.98 | 7000 | 4.6461 |
| 3.687 | 4.26 | 7500 | 4.6674 |
| 3.6784 | 4.54 | 8000 | 4.6577 |
| 3.6665 | 4.83 | 8500 | 4.6403 |
| 3.5603 | 5.11 | 9000 | 4.6735 |
| 3.4226 | 5.39 | 9500 | 4.6843 |
| 3.4158 | 5.68 | 10000 | 4.6834 |
| 3.4077 | 5.96 | 10500 | 4.6679 |
| 3.2813 | 6.25 | 11000 | 4.6955 |
| 3.2684 | 6.53 | 11500 | 4.6982 |
| 3.2599 | 6.81 | 12000 | 4.6978 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
dyedream/Reinforce-PixelCopter
|
dyedream
| 2023-06-29T10:29:28Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T10:28:40Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 37.30 +/- 30.91
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Malaika/rl_course_vizdoom_health_gathering_supreme
|
Malaika
| 2023-06-29T10:27:45Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T10:27:38Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.21 +/- 2.37
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Malaika/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
msladic/ppo-MSLunarLander-v3
|
msladic
| 2023-06-29T10:12:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T10:12:17Z |
---
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: 265.97 +/- 18.19
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
...
```
|
vlkn/falcon_instruct_deft
|
vlkn
| 2023-06-29T10:08:43Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2023-06-29T09:24:12Z |
---
tags:
- generated_from_trainer
model-index:
- name: falcon_instruct_deft
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. -->
# falcon_instruct_deft
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 300
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
paumena/QA-BERT
|
paumena
| 2023-06-29T10:02:58Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-13T10:01:47Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: paumena/QA-BERT
results: []
datasets:
- squad
metrics:
- exact_match
- f1
library_name: transformers
---
<!-- 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. -->
# paumena/QA-BERT
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3103
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Evaluation metrics
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 27725, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2706 | 0 |
| 0.7859 | 1 |
| 0.5571 | 2 |
| 0.4067 | 3 |
| 0.3103 | 4 |
### Framework versions
- Transformers 4.30.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ckaschny/my_awesome_qa_model
|
ckaschny
| 2023-06-29T09:55:21Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-29T09:19:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7628
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4261 |
| 2.7261 | 2.0 | 500 | 1.8374 |
| 2.7261 | 3.0 | 750 | 1.7628 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Jumartineze/bert-base-spanish-wwm-uncased-finetuned-MeIA-AnalisisDeSentimientos
|
Jumartineze
| 2023-06-29T09:45:59Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-27T05:54:49Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-spanish-wwm-uncased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-uncased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9394
- F1: 0.5876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9694 | 1.0 | 837 | 0.9393 | 0.5784 |
| 0.825 | 2.0 | 1674 | 0.9394 | 0.5876 |
| 0.6932 | 3.0 | 2511 | 0.9883 | 0.5870 |
| 0.5868 | 4.0 | 3348 | 1.0267 | 0.5864 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dhkim2810/MobileSAM
|
dhkim2810
| 2023-06-29T09:34:09Z | 0 | 21 | null |
[
"arxiv:2306.14289",
"arxiv:2304.02643",
"license:mit",
"region:us"
] | null | 2023-06-28T04:10:23Z |
---
license: mit
---
# Faster Segement Anything (MobileSAM)
<!-- Provide a quick summary of what the model is/does. -->
- **Repository:** [Github - MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
- **Paper:** [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://arxiv.org/pdf/2306.14289.pdf)
- **Demo:** [HuggingFace Demo](https://huggingface.co/spaces/dhkim2810/MobileSAM)
**MobileSAM** performs on par with the original SAM (at least visually) and keeps exactly the same pipeline as the original SAM except for a change on the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M). On a single GPU, MobileSAM runs around 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.
The comparison of ViT-based image encoder is summarzed as follows:
Image Encoder | Original SAM | MobileSAM
:------------:|:-------------:|:---------:
Paramters | 611M | 5M
Speed | 452ms | 8ms
Original SAM and MobileSAM have exactly the same prompt-guided mask decoder:
Mask Decoder | Original SAM | MobileSAM
:-----------------------------------------:|:---------:|:-----:
Paramters | 3.876M | 3.876M
Speed | 4ms | 4ms
The comparison of the whole pipeline is summarzed as follows:
Whole Pipeline (Enc+Dec) | Original SAM | MobileSAM
:-----------------------------------------:|:---------:|:-----:
Paramters | 615M | 9.66M
Speed | 456ms | 12ms
## Acknowledgement
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
<details>
<summary>
<a href="https://github.com/facebookresearch/segment-anything">SAM</a> (Segment Anything) [<b>bib</b>]
</summary>
```bibtex
@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}
```
</details>
<details>
<summary>
<a href="https://github.com/microsoft/Cream/tree/main/TinyViT">TinyViT</a> (TinyViT: Fast Pretraining Distillation for Small Vision Transformers) [<b>bib</b>]
</summary>
```bibtex
@InProceedings{tiny_vit,
title={TinyViT: Fast Pretraining Distillation for Small Vision Transformers},
author={Wu, Kan and Zhang, Jinnian and Peng, Houwen and Liu, Mengchen and Xiao, Bin and Fu, Jianlong and Yuan, Lu},
booktitle={European conference on computer vision (ECCV)},
year={2022}
```
</details>
**BibTeX:**
```bibtex
@article{mobile_sam,
title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung Ho and Lee, Seungkyu and Hong, Choong Seon},
journal={arXiv preprint arXiv:2306.14289},
year={2023}
}
```
|
mrbingzhao/macbert4csc-cn
|
mrbingzhao
| 2023-06-29T09:25:19Z | 3 | 0 |
transformers
|
[
"transformers",
"bert",
"fill-mask",
"pytorch",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-28T08:50:46Z |
---
language:
- zh
tags:
- bert
- pytorch
- zh
license: "apache-2.0"
---
# MacBERT for Chinese Spelling Correction(macbert4csc) Model
中文拼写纠错模型
`macbert4csc-base-chinese` evaluate SIGHAN2015 test data：
- Char Level: precision:0.9372, recall:0.8640, f1:0.8991
- Sentence Level: precision:0.8264, recall:0.7366, f1:0.7789
由于训练使用的数据使用了SIGHAN2015的训练集（复现paper），在SIGHAN2015的测试集上达到SOTA水平。
模型结构，魔改于softmaskedbert：
## Usage
本项目开源在中文文本纠错项目：[pycorrector](https://github.com/shibing624/pycorrector)，可支持macbert4csc模型，通过如下命令调用：
```python
from pycorrector.macbert.macbert_corrector import MacBertCorrector
nlp = MacBertCorrector("shibing624/macbert4csc-base-chinese").macbert_correct
i = nlp('今天新情很好')
print(i)
```
当然，你也可使用官方的huggingface/transformers调用：
*Please use 'Bert' related functions to load this model!*
```python
import operator
import torch
from transformers import BertTokenizer, BertForMaskedLM
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = BertTokenizer.from_pretrained("shibing624/macbert4csc-base-chinese")
model = BertForMaskedLM.from_pretrained("shibing624/macbert4csc-base-chinese")
model.to(device)
texts = ["今天新情很好", "你找到你最喜欢的工作，我也很高心。"]
with torch.no_grad():
outputs = model(**tokenizer(texts, padding=True, return_tensors='pt').to(device))
def get_errors(corrected_text, origin_text):
sub_details = []
for i, ori_char in enumerate(origin_text):
if ori_char in [' ', '“', '”', '‘', '’', '琊', '\n', '…', '—', '擤']:
# add unk word
corrected_text = corrected_text[:i] + ori_char + corrected_text[i:]
continue
if i >= len(corrected_text):
continue
if ori_char != corrected_text[i]:
if ori_char.lower() == corrected_text[i]:
# pass english upper char
corrected_text = corrected_text[:i] + ori_char + corrected_text[i + 1:]
continue
sub_details.append((ori_char, corrected_text[i], i, i + 1))
sub_details = sorted(sub_details, key=operator.itemgetter(2))
return corrected_text, sub_details
result = []
for ids, text in zip(outputs.logits, texts):
_text = tokenizer.decode(torch.argmax(ids, dim=-1), skip_special_tokens=True).replace(' ', '')
corrected_text = _text[:len(text)]
corrected_text, details = get_errors(corrected_text, text)
print(text, ' => ', corrected_text, details)
result.append((corrected_text, details))
print(result)
```
output:
```shell
今天新情很好 => 今天心情很好 [('新', '心', 2, 3)]
你找到你最喜欢的工作，我也很高心。 => 你找到你最喜欢的工作，我也很高兴。 [('心', '兴', 15, 16)]
```
模型文件组成：
```
macbert4csc-base-chinese
├── config.json
├── added_tokens.json
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.txt
```
### 训练数据集
#### SIGHAN+Wang271K中文纠错数据集
| 数据集 | 语料 | 下载链接 | 压缩包大小 |
| :------- | :--------- | :---------: | :---------: |
| **`SIGHAN+Wang271K中文纠错数据集`** | SIGHAN+Wang271K(27万条) | [百度网盘（密码01b9）](https://pan.baidu.com/s/1BV5tr9eONZCI0wERFvr0gQ)| 106M |
| **`原始SIGHAN数据集`** | SIGHAN13 14 15 | [官方csc.html](http://nlp.ee.ncu.edu.tw/resource/csc.html)| 339K |
| **`原始Wang271K数据集`** | Wang271K | [Automatic-Corpus-Generation dimmywang提供](https://github.com/wdimmy/Automatic-Corpus-Generation/blob/master/corpus/train.sgml)| 93M |
SIGHAN+Wang271K中文纠错数据集，数据格式：
```json
[
{
"id": "B2-4029-3",
"original_text": "晚间会听到嗓音，白天的时候大家都不会太在意，但是在睡觉的时候这嗓音成为大家的恶梦。",
"wrong_ids": [
5,
31
],
"correct_text": "晚间会听到噪音，白天的时候大家都不会太在意，但是在睡觉的时候这噪音成为大家的恶梦。"
},
]
```
```shell
macbert4csc
├── config.json
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.txt
```
如果需要训练macbert4csc，请参考[https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert](https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert)
### About MacBERT
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Here is an example of our pre-training task.
| task | Example |
| -------------- | ----------------- |
| **Original Sentence** | we use a language model to predict the probability of the next word. |
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . |
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . |
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . |
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . |
Except for the new pre-training task, we also incorporate the following techniques.
- Whole Word Masking (WWM)
- N-gram masking
- Sentence-Order Prediction (SOP)
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.**
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
## Citation
```latex
@software{pycorrector,
author = {Xu Ming},
title = {pycorrector: Text Error Correction Tool},
year = {2021},
url = {https://github.com/shibing624/pycorrector},
}
```
|
A1abz/q-Taxi-v3
|
A1abz
| 2023-06-29T09:18:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T09:18:18Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="A1abz/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
A1abz/q-tTaxi-v3
|
A1abz
| 2023-06-29T09:18:10Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T09:12:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-tTaxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="A1abz/q-tTaxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AustinCarthy/Benign10MGPT2_subdomain_100KP_BFall_fromP_90K_topP_0.75_ratio5
|
AustinCarthy
| 2023-06-29T09:13:42Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-06-29T05:45:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Benign10MGPT2_subdomain_100KP_BFall_fromP_90K_topP_0.75_ratio5
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. -->
# Benign10MGPT2_subdomain_100KP_BFall_fromP_90K_topP_0.75_ratio5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_Benign10MGPT2_using_phish_95K_top_p_0.75subdomain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0216
- Accuracy: 0.9971
- F1: 0.9691
- Precision: 0.9890
- Recall: 0.95
- Roc Auc Score: 0.9747
- Tpr At Fpr 0.01: 0.914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.019 | 1.0 | 35625 | 0.0191 | 0.9961 | 0.9584 | 0.9840 | 0.9342 | 0.9667 | 0.8318 |
| 0.0164 | 2.0 | 71250 | 0.0169 | 0.9964 | 0.9609 | 0.9942 | 0.9298 | 0.9648 | 0.8852 |
| 0.0096 | 3.0 | 106875 | 0.0126 | 0.9973 | 0.9717 | 0.9803 | 0.9632 | 0.9811 | 0.8794 |
| 0.0045 | 4.0 | 142500 | 0.0187 | 0.9972 | 0.9700 | 0.9894 | 0.9514 | 0.9754 | 0.9098 |
| 0.0017 | 5.0 | 178125 | 0.0216 | 0.9971 | 0.9691 | 0.9890 | 0.95 | 0.9747 | 0.914 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Qt_20_BERT_Over_Sampling
|
YakovElm
| 2023-06-29T09:08:25Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-29T09:07:50Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt_20_BERT_Over_Sampling
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. -->
# Qt_20_BERT_Over_Sampling
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0158
- Train Accuracy: 0.9940
- Validation Loss: 0.3047
- Validation Accuracy: 0.9359
- 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': 1.0, '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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3430 | 0.8260 | 0.2257 | 0.9205 | 0 |
| 0.0359 | 0.9884 | 0.3111 | 0.9213 | 1 |
| 0.0158 | 0.9940 | 0.3047 | 0.9359 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nomad-ai/rl_course_vizdoom_health_gathering_supreme
|
nomad-ai
| 2023-06-29T09:03:02Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-29T09:02:54Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.97 +/- 4.35
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r nomad-ai/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Shrawani/squad-bloom-1b7-v1
|
Shrawani
| 2023-06-29T08:51:39Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-29T08:51:37Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
zhyemmmm/Babes
|
zhyemmmm
| 2023-06-29T08:27:42Z | 29 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-29T08:22:11Z |
---
license: creativeml-openrail-m
---
|
JacobHenry/Pleasantnoise
|
JacobHenry
| 2023-06-29T08:07:55Z | 0 | 0 | null |
[
"Langchain",
"OpenAI API",
"code",
"csv",
"conversation starter",
"document-question-answering",
"en",
"license:unknown",
"region:us"
] |
document-question-answering
| 2023-06-28T08:44:17Z |
---
license: unknown
language:
- en
pipeline_tag: document-question-answering
tags:
- Langchain
- OpenAI API
- code
- csv
- conversation starter
---
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.