modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 528
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
TheRains/cv9-special-batch4-tiny
|
TheRains
| 2023-08-06T13:11:46Z | 83 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T05:18:36Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 32.55118472509777
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4997
- Wer: 32.5512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7055 | 0.48 | 1000 | 0.6329 | 42.1072 |
| 0.5685 | 0.97 | 2000 | 0.5515 | 35.8638 |
| 0.3807 | 1.45 | 3000 | 0.5232 | 34.0189 |
| 0.3766 | 1.94 | 4000 | 0.4993 | 32.6708 |
| 0.3567 | 2.42 | 5000 | 0.4997 | 32.5512 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
RIOLITE/products_matching_aumet_fine_tune_2023-08-06
|
RIOLITE
| 2023-08-06T13:00:37Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-06T13:00:13Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
RIOLITE/products_matching_aumet_scratch_2023-08-06
|
RIOLITE
| 2023-08-06T13:00:12Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-06T12:59:48Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
CyberHarem/makima_nikke
|
CyberHarem
| 2023-08-06T12:55:11Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/makima_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T12:50:36Z |
---
license: mit
datasets:
- CyberHarem/makima_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of makima_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/makima_nikke.pt` as the embedding and `1500/makima_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `makima_nikke`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | pattern_3 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/makima_nikke.zip) |
| 1400 |  |  |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/makima_nikke.zip) |
| 1300 |  |  |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/makima_nikke.zip) |
| 1200 |  |  |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/makima_nikke.zip) |
| 1100 |  |  |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/makima_nikke.zip) |
| 1000 |  |  |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/makima_nikke.zip) |
| 900 |  |  |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/makima_nikke.zip) |
| 800 |  |  |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/makima_nikke.zip) |
| 700 |  |  |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/makima_nikke.zip) |
| 600 |  |  |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/makima_nikke.zip) |
| 500 |  |  |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/makima_nikke.zip) |
| 400 |  |  |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/makima_nikke.zip) |
| 300 |  |  |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/makima_nikke.zip) |
| 200 |  |  |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/makima_nikke.zip) |
| 100 |  |  |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/makima_nikke.zip) |
|
s3nh/chinese-alpaca-2-7b-GGML
|
s3nh
| 2023-08-06T12:44:54Z | 0 | 7 |
transformers
|
[
"transformers",
"text-generation",
"zh",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-31T07:58:43Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/ziqingyang/chinese-alpaca-2-7b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
**This is the full Chinese-Alpaca-2-7B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/ziqingyang/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/ziqingyang/chinese-llama-2-lora-7b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/ziqingyang/chinese-alpaca-2-lora-7b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
nokotin/a2c-PandaReachDense-v2
|
nokotin
| 2023-08-06T12:42:22Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T12:40:06Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.85 +/- 0.23
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
voxxer/Lunar_Lander_v2_PPO
|
voxxer
| 2023-08-06T12:16:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T12:15:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.82 +/- 15.57
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
...
```
|
sartmis1/starcoder-finetune-oasst1
|
sartmis1
| 2023-08-06T12:14:00Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"gpt_bigcode",
"en",
"dataset:HuggingFaceH4/oasst1_en",
"base_model:bigcode/starcoder",
"base_model:adapter:bigcode/starcoder",
"region:us"
] | null | 2023-08-04T11:04:01Z |
---
base_model: bigcode/starcoder
model-index:
- name: starcoder-finetune-oasst1
results: []
library_name: peft
datasets:
- HuggingFaceH4/oasst1_en
language:
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
### Model Description
Starcoder Model fine-tuned on HuggingFaceH4/oasst1_en dataset.
|
fromhell01/Reinforce-PixelCopter-3
|
fromhell01
| 2023-08-06T12:05:27Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T12:05:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.00 +/- 17.53
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
|
SmellyKat/ppo-SnowballTarget
|
SmellyKat
| 2023-08-06T11:51:26Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-03T07:50:56Z |
---
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: SmellyKat/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/crow_nikke
|
CyberHarem
| 2023-08-06T11:46:05Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/crow_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T11:40:59Z |
---
license: mit
datasets:
- CyberHarem/crow_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of crow_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/crow_nikke.pt` as the embedding and `1500/crow_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `crow_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:--------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/crow_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/crow_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/crow_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/crow_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/crow_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/crow_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/crow_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/crow_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/crow_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/crow_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/crow_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/crow_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/crow_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/crow_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/crow_nikke.zip) |
|
sarinrajesh/my-pet-dog
|
sarinrajesh
| 2023-08-06T11:37:50Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T11:34:00Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by sarinrajesh following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: -AJCE133
Sample pictures of this concept:
|
TheRains/yt-special-batch4-small
|
TheRains
| 2023-08-06T11:37:05Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:yt",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T09:20:53Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- yt
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: yt id
type: yt
metrics:
- name: Wer
type: wer
value: 48.22644445885481
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the yt id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7390
- Wer: 48.2264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.0296 | 0.09 | 1000 | 0.9364 | 69.1330 |
| 0.8092 | 0.17 | 2000 | 0.8503 | 59.1401 |
| 0.9109 | 0.26 | 3000 | 0.8034 | 50.4247 |
| 0.7291 | 0.34 | 4000 | 0.7616 | 48.3821 |
| 0.7631 | 0.43 | 5000 | 0.7390 | 48.2264 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
JapGuy/DaliborJanda_v1_730Epochs_RVC_v2
|
JapGuy
| 2023-08-06T11:35:21Z | 0 | 0 | null |
[
"music",
"rvc",
"dalibor",
"janda",
"model",
"audio-to-audio",
"cs",
"sk",
"en",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-08-05T14:23:24Z |
---
license: openrail
language:
- cs
- sk
- en
pipeline_tag: audio-to-audio
tags:
- music
- rvc
- dalibor
- janda
- model
---
# Dalibor Janda [CZ]
# 730 Epochs - RVC V2 - mangio-creep - 64 Hop Length
Trained on 31,5 minutes of isolated acapellas using UVR (Voc FT + Reverb HQ) + Audacity to remove parts with double vocals and vocals from others (+Noise Gate)
|
jsunster/vit-base-patch16-224-in21k-finetuned-lora-food101
|
jsunster
| 2023-08-06T11:27:26Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:59:24Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Beatokaine/LunarLander-Second-Test-Kaine
|
Beatokaine
| 2023-08-06T11:14:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T11:14:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.62 +/- 20.94
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
...
```
|
jlodge83/ppo-Huggy
|
jlodge83
| 2023-08-06T10:59:14Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T10:59:02Z |
---
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: jlodge83/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ldhldh/polyglot-ko-1.3b_lora_big_tern_30kstep
|
ldhldh
| 2023-08-06T10:55:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:42:35Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Ardra05/my-pet-dog
|
Ardra05
| 2023-08-06T10:50:15Z | 16 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T10:46:38Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by Ardra05 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE101
Sample pictures of this concept:
|
Amalya/fat
|
Amalya
| 2023-08-06T10:44:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-06T10:44:35Z |
the fat sister from the Disney-style fairy tale for children has lost weight and transformed
|
DejaVuChan/reze
|
DejaVuChan
| 2023-08-06T10:39:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-06T10:38:18Z |
---
license: creativeml-openrail-m
---
|
Lukee4/biomedlm-2020_2labels
|
Lukee4
| 2023-08-06T10:37:00Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:36:58Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
tiggerhelloworld/q-FrozenLake-v1-4x4-noSlippery
|
tiggerhelloworld
| 2023-08-06T10:33:36Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T10:33:33Z |
---
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="tiggerhelloworld/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"])
```
|
Lukee4/biomedlm-2019_3labels
|
Lukee4
| 2023-08-06T10:31:21Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:31:19Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
aphi/poca-SoccerTwos_v2
|
aphi
| 2023-08-06T10:28:16Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-08-06T10:26:44Z |
---
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: aphi/poca-SoccerTwos_v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
shubhxms/dqn-SpaceInvadersNoFrameskip-v4
|
shubhxms
| 2023-08-06T10:27:02Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T10:26:24Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 619.50 +/- 116.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shubhxms -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shubhxms -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shubhxms
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Geotrend/bert-base-en-es-pt-cased
|
Geotrend
| 2023-08-06T10:24:38Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# bert-base-en-es-pt-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-es-pt-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-en-es-pt-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
s3nh/WizardLM-1.0-Uncensored-Llama2-13b-GGML
|
s3nh
| 2023-08-06T10:24:03Z | 0 | 4 |
transformers
|
[
"transformers",
"text-generation",
"en",
"zh",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-06T09:50:39Z |
---
license: openrail
language:
- en
- zh
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/ehartford/WizardLM-1.0-Uncensored-Llama2-13b)
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
This is a retraining of https://huggingface.co/WizardLM/WizardLM-13B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.
Note that LLaMA itself has inherent ethical beliefs, so there's no such thing as a "truly uncensored" model. But this model will be more compliant than WizardLM/WizardLM-13B-V1.0.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
Like WizardLM/WizardLM-13B-V1.0, this model is trained with Vicuna-1.1 style prompts.
```
You are a helpful AI assistant.
USER: <prompt>
ASSISTANT:
```
|
Lukee4/biomedlm-2019_2labels
|
Lukee4
| 2023-08-06T10:18:24Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:18:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Lukee4/biogpt-2019_2labels
|
Lukee4
| 2023-08-06T10:14:04Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T09:43:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
DejaVuChan/kizuki
|
DejaVuChan
| 2023-08-06T10:10:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-03T13:35:27Z |
---
license: creativeml-openrail-m
---
|
NiscR/ppo-SnowballTarget
|
NiscR
| 2023-08-06T10:05:07Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-06T10:05:04Z |
---
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: NiscR/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/quency_nikke
|
CyberHarem
| 2023-08-06T10:03:57Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/quency_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T09:58:39Z |
---
license: mit
datasets:
- CyberHarem/quency_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of quency_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/quency_nikke.pt` as the embedding and `1500/quency_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `quency_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/quency_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/quency_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/quency_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/quency_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/quency_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/quency_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/quency_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/quency_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/quency_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/quency_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/quency_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/quency_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/quency_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/quency_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/quency_nikke.zip) |
|
TheBloke/Llama-2-70B-Chat-fp16
|
TheBloke
| 2023-08-06T10:02:48Z | 28 | 48 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-2",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-19T02:21:26Z |
---
inference: false
language:
- en
license: other
model_type: llama
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
<!-- 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 -->
# Meta's Llama 2 70B Chat fp16
These files are fp16 pytorch model files for [Meta's Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf).
They were produced by downloading the PTH files from Meta, and then converting to HF format using the latest Transformers 4.32.0.dev0, from Git, with the Llama 2 PR included: https://github.com/huggingface/transformers/pull/24891.
Command to convert was:
```
python3 /workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir /workspace/git/llama/download --model_size 70B --output_dir /workspace/process/llama-2-70b-chat/source --safe_serialization true
```
The files were saved in Safetensors format.
I am uploading this repo because I initially tried to create GPTQs using the [Meta Llama 2 70B Chat HF repo](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf), but got strange errors that suggested the weights were not correct. But converting from the PTH files using the latest `convert_llama_weights_to_hf.py` script worked fine.
Many thanks to William Beauchamp from [Chai](https://chai-research.com/) for providing the hardware for these quantisations!
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-70B-chat-GPTQ)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
* [My fp16 conversion of the unquantised PTH model files](https://huggingface.co/TheBloke/Llama-2-70B-chat-fp16)
## Prompt template: Llama-2-Chat
```
System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
User: {prompt}
Assistant:
```
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Meta's Llama 2 70B Chat
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
s3nh/moss-base-7b-GGML
|
s3nh
| 2023-08-06T10:01:36Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-06T09:41:28Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/fnlp/moss-base-7b)
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
Moss-base-7b是一个70亿参数量的预训练语言模型，可以作为基座模型用来进行SFT训练等。
### Import from Transformers
To load the Moss 7B model using Transformers, use the following code:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("fnlp/moss-base-7b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("fnlp/moss-base-7b", trust_remote_code=True).cuda()
>>> model = model.eval()
>>> inputs = tokenizer(["流浪地球的导演是"], return_tensors="pt")
>>> for k,v in inputs.items():
inputs[k] = v.cuda()
>>> outputs = model.generate(**inputs, do_sample=True, temperature=0.8, top_p=0.8, repetition_penalty=1.1, max_new_tokens=256)
>>> response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
>>> print(response)
郭帆
主演分别是吴京和屈楚萧 还有李光洁刘德华等等
这电影可以说是目前国内科幻片的天花板了
票房也是突破50亿大关啦
小编真的非常期待这部电影呀
所以呢今天就给大家整理了关于影片中的很多细节图哦~
不知道大家有没有注意到呢
```
|
Lukee4/biogpt-2020_3labels
|
Lukee4
| 2023-08-06T10:01:15Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T10:01:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
AronGeorge10/my-pet-cat
|
AronGeorge10
| 2023-08-06T09:34:28Z | 21 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T09:30:33Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat Dreambooth model trained by AronGeorge10 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE269
Sample pictures of this concept:
|
TheRains/cv9-special-batch12-tiny
|
TheRains
| 2023-08-06T09:31:23Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T08:18:56Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 32.100299056820795
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5086
- Wer: 32.1003
## 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: 12
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5121 | 1.45 | 1000 | 0.5645 | 36.2595 |
| 0.3401 | 2.9 | 2000 | 0.5052 | 32.9377 |
| 0.1855 | 4.35 | 3000 | 0.5086 | 32.1003 |
| 0.1638 | 5.81 | 4000 | 0.5088 | 32.1831 |
| 0.1047 | 7.26 | 5000 | 0.5143 | 32.1601 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
George-Ogden/gptr2-nano-with-momentum-without-weight-decay
|
George-Ogden
| 2023-08-06T09:28:17Z | 31 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"research",
"en",
"dataset:wikipedia",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-08-05T13:59:29Z |
---
license: mit
datasets:
- wikipedia
language:
- en
tags:
- research
---
This model is significantly undertrained and designed for research purposes only.
For use in transformers:
```python
from transformers import AutoTokenizer, GPT2Model
import torch.nn as nn
import torch
class RMSLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-8, affine=True):
super(RMSLayerNorm, self).__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.affine = affine
if self.affine:
self.weight = nn.Parameter(torch.ones(()))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
def forward(self, x):
rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
x_normalized = x / rms
if self.affine:
x_normalized = x_normalized * self.weight
return x_normalized
def replace(model):
for name, child in model.named_children():
if isinstance(child, nn.modules.normalization.LayerNorm):
setattr(model, name, RMSLayerNorm(child.normalized_shape, eps=child.eps, affine=True))
else:
replace(child)
return model
class GPTR2Model(GPT2Model):
def __init__(self, config):
super().__init__(config)
replace(self)
model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-with-momentum-without-weight-decay")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
```
For more details and example usage, see https://github.com/George-Ogden/residual-streams
|
George-Ogden/gptr2-nano-with-momentum-with-weight-decay
|
George-Ogden
| 2023-08-06T09:27:54Z | 40 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"research",
"en",
"dataset:wikipedia",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-08-01T12:26:52Z |
---
license: mit
datasets:
- wikipedia
language:
- en
tags:
- research
---
This model is significantly undertrained and designed for research purposes only.
For use in transformers:
```python
from transformers import AutoTokenizer, GPT2Model
import torch.nn as nn
import torch
class RMSLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-8, affine=True):
super(RMSLayerNorm, self).__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.affine = affine
if self.affine:
self.weight = nn.Parameter(torch.ones(()))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
def forward(self, x):
rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
x_normalized = x / rms
if self.affine:
x_normalized = x_normalized * self.weight
return x_normalized
def replace(model):
for name, child in model.named_children():
if isinstance(child, nn.modules.normalization.LayerNorm):
setattr(model, name, RMSLayerNorm(child.normalized_shape, eps=child.eps, affine=True))
else:
replace(child)
return model
class GPTR2Model(GPT2Model):
def __init__(self, config):
super().__init__(config)
replace(self)
model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-with-momentum-with-weight-decay")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
```
For more details and example usage, see https://github.com/George-Ogden/residual-streams
|
George-Ogden/gptr2-nano-without-momentum-with-weight-decay
|
George-Ogden
| 2023-08-06T09:27:11Z | 35 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"research",
"en",
"dataset:wikipedia",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-08-01T12:23:13Z |
---
license: mit
datasets:
- wikipedia
language:
- en
tags:
- research
---
This model is significantly undertrained and designed for research purposes only.
For use in transformers:
```python
from transformers import AutoTokenizer, GPT2Model
import torch.nn as nn
import torch
class RMSLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-8, affine=True):
super(RMSLayerNorm, self).__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.affine = affine
if self.affine:
self.weight = nn.Parameter(torch.ones(()))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
def forward(self, x):
rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
x_normalized = x / rms
if self.affine:
x_normalized = x_normalized * self.weight
return x_normalized
def replace(model):
for name, child in model.named_children():
if isinstance(child, nn.modules.normalization.LayerNorm):
setattr(model, name, RMSLayerNorm(child.normalized_shape, eps=child.eps, affine=True))
else:
replace(child)
return model
class GPTR2Model(GPT2Model):
def __init__(self, config):
super().__init__(config)
replace(self)
model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-without-momentum-with-weight-decay")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
```
For more details and example usage, see https://github.com/George-Ogden/residual-streams
|
George-Ogden/gptr2-nano-without-momentum-without-weight-decay
|
George-Ogden
| 2023-08-06T09:26:49Z | 32 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"research",
"en",
"dataset:wikipedia",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-08-05T14:07:14Z |
---
license: mit
datasets:
- wikipedia
language:
- en
tags:
- research
---
This model is significantly undertrained and designed for research purposes only.
For use in transformers:
```python
from transformers import AutoTokenizer, GPT2Model
import torch.nn as nn
import torch
class RMSLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-8, affine=True):
super(RMSLayerNorm, self).__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.affine = affine
if self.affine:
self.weight = nn.Parameter(torch.ones(()))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
def forward(self, x):
rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
x_normalized = x / rms
if self.affine:
x_normalized = x_normalized * self.weight
return x_normalized
def replace(model):
for name, child in model.named_children():
if isinstance(child, nn.modules.normalization.LayerNorm):
setattr(model, name, RMSLayerNorm(child.normalized_shape, eps=child.eps, affine=True))
else:
replace(child)
return model
class GPTR2Model(GPT2Model):
def __init__(self, config):
super().__init__(config)
replace(self)
model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-without-momentum-without-weight-decay")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
```
For more details and example usage, see https://github.com/George-Ogden/residual-streams
|
Mathew2001/my-pet-dog-xzg
|
Mathew2001
| 2023-08-06T09:23:55Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T09:18:54Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-xzg Dreambooth model trained by Mathew2001 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE257
Sample pictures of this concept:
|
Tverous/sft-trl-claim-128
|
Tverous
| 2023-08-06T09:23:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"dataset:anli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-05T08:47:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- anli
model-index:
- name: sft-trl-claim-128
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. -->
# sft-trl-claim-128
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the anli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3201
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4364 | 0.08 | 1000 | 1.8760 |
| 0.9805 | 0.16 | 2000 | 1.2595 |
| 0.6629 | 0.24 | 3000 | 1.2970 |
| 0.4647 | 0.32 | 4000 | 0.9789 |
| 0.4579 | 0.4 | 5000 | 0.8591 |
| 0.383 | 0.48 | 6000 | 0.8866 |
| 0.4915 | 0.56 | 7000 | 0.4281 |
| 0.4139 | 0.64 | 8000 | 0.3946 |
| 0.2563 | 0.72 | 9000 | 0.3653 |
| 0.3179 | 0.8 | 10000 | 0.3528 |
| 0.4199 | 0.88 | 11000 | 0.3602 |
| 0.3877 | 0.96 | 12000 | 0.3457 |
| 0.2332 | 1.04 | 13000 | 0.3882 |
| 0.3817 | 1.11 | 14000 | 0.3604 |
| 0.2734 | 1.19 | 15000 | 0.3613 |
| 0.213 | 1.27 | 16000 | 0.3722 |
| 0.3154 | 1.35 | 17000 | 0.3378 |
| 0.2258 | 1.43 | 18000 | 0.3117 |
| 0.3198 | 1.51 | 19000 | 0.3213 |
| 0.2959 | 1.59 | 20000 | 0.3050 |
| 0.2588 | 1.67 | 21000 | 0.3190 |
| 0.2279 | 1.75 | 22000 | 0.3065 |
| 0.2988 | 1.83 | 23000 | 0.3077 |
| 0.3701 | 1.91 | 24000 | 0.3092 |
| 0.281 | 1.99 | 25000 | 0.3038 |
| 0.1743 | 2.07 | 26000 | 0.3542 |
| 0.1374 | 2.15 | 27000 | 0.3550 |
| 0.1282 | 2.23 | 28000 | 0.3386 |
| 0.1757 | 2.31 | 29000 | 0.3489 |
| 0.1371 | 2.39 | 30000 | 0.3316 |
| 0.1689 | 2.47 | 31000 | 0.3291 |
| 0.1882 | 2.55 | 32000 | 0.3292 |
| 0.1685 | 2.63 | 33000 | 0.3196 |
| 0.1775 | 2.71 | 34000 | 0.3320 |
| 0.1963 | 2.79 | 35000 | 0.3278 |
| 0.1733 | 2.87 | 36000 | 0.3221 |
| 0.1503 | 2.95 | 37000 | 0.3201 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
HaceHazretleri/ppo-Huggy_default
|
HaceHazretleri
| 2023-08-06T09:22:51Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-06T09:22:38Z |
---
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: HaceHazretleri/ppo-Huggy_default
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
alphin2002/my-bag
|
alphin2002
| 2023-08-06T08:58:41Z | 11 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T08:54:56Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Bag Dreambooth model trained by alphin2002 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE125
Sample pictures of this concept:
|
guyhadad01/dqn-SpaceInvadersNoFrameskip-v4
|
guyhadad01
| 2023-08-06T08:55:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T08:54:50Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 714.00 +/- 271.01
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga guyhadad01 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga guyhadad01 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga guyhadad01
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
CyberHarem/maiden_nikke
|
CyberHarem
| 2023-08-06T08:49:13Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/maiden_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T08:41:20Z |
---
license: mit
datasets:
- CyberHarem/maiden_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of maiden_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/maiden_nikke.pt` as the embedding and `1500/maiden_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `maiden_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/maiden_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/maiden_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/maiden_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/maiden_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/maiden_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/maiden_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/maiden_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/maiden_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/maiden_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/maiden_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/maiden_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/maiden_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/maiden_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/maiden_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/maiden_nikke.zip) |
|
Yossshi/ppo-LunarLander-v2
|
Yossshi
| 2023-08-06T08:29:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T08:29:30Z |
---
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: 243.31 +/- 20.70
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
...
```
|
CyberHarem/guillotine_nikke
|
CyberHarem
| 2023-08-06T08:22:43Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/guillotine_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T08:17:11Z |
---
license: mit
datasets:
- CyberHarem/guillotine_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of guillotine_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/guillotine_nikke.pt` as the embedding and `1500/guillotine_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `guillotine_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:--------------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/guillotine_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/guillotine_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/guillotine_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/guillotine_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/guillotine_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/guillotine_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/guillotine_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/guillotine_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/guillotine_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/guillotine_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/guillotine_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/guillotine_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/guillotine_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/guillotine_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/guillotine_nikke.zip) |
|
yaohuacn/ppo-LunarLander-v2
|
yaohuacn
| 2023-08-06T08:09:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T08:04:56Z |
---
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: 231.49 +/- 50.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
vimal52/Flant5_base_finetune_QLoRa
|
vimal52
| 2023-08-06T07:58:57Z | 4 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-08-06T03:39:44Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
CyberHarem/sin_nikke
|
CyberHarem
| 2023-08-06T07:58:24Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/sin_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T07:52:09Z |
---
license: mit
datasets:
- CyberHarem/sin_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of sin_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/sin_nikke.pt` as the embedding and `1500/sin_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `sin_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/sin_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/sin_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/sin_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/sin_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/sin_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/sin_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/sin_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/sin_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/sin_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/sin_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/sin_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/sin_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/sin_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/sin_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/sin_nikke.zip) |
|
RedRayz/MyVAE
|
RedRayz
| 2023-08-06T07:48:11Z | 0 | 3 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-06T07:37:28Z |
---
license: creativeml-openrail-m
---
|
supriya1429/genaiproj
|
supriya1429
| 2023-08-06T07:46:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-05T14:16:05Z |
---
title: Gen AI Project NxtWave
emoji: 🏆
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 3.39.0
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
fromhell01/Reinforce-PixelCopter-1
|
fromhell01
| 2023-08-06T07:44:36Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T07:44:33Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.80 +/- 38.96
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
|
zeusfsx/instruction-detection
|
zeusfsx
| 2023-08-06T07:35:11Z | 117 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-15T18:15:52Z |
---
license: mit
language:
- uk
metrics:
- f1
pipeline_tag: token-classification
widget:
- text: Як пукнути та не вкакатися - розказує лікар вищого ступення
example_title: Приклад 1
- text: Що таке дихання маткою? - розказує лікар гінеколог
example_title: Приклад 2
---
# Instruction Detection Model
Welcome to the repository for the instruction detection model! This model, hosted on the [Hugging Face Model Hub](https://huggingface.co/models), is designed specifically for detecting instructions in newspaper titles. **Ukranian Language Only**. Utilizing token classification, it scans through the given input - a newspaper title, and labels tokens that appear to signify an instruction.
Model Card: [zeusfsx/instruction-detection](https://huggingface.co/zeusfsx/instruction-detection)
## Table of Contents
- [Introduction](#introduction)
- [Usage](#usage)
- [Training](#training)
- [Evaluation](#evaluation)
- [License](#license)
- [Citation](#citation)
- [Contact Information](#contact-information)
## Introduction
In the age of information, newspaper titles are often crafted to attract attention and occasionally incorporate direct or indirect instructions. This model can help analyze these titles, detect such instructions, and tag them accordingly.
It employs token classification task, a common technique in Natural Language Processing (NLP), to detect and label instructions in the text.
## Usage
Here's how to use this model:
### In Python
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("zeusfsx/instruction-detection")
model = AutoModelForTokenClassification.from_pretrained("zeusfsx/instruction-detection")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Your example newspaper title here"
output = nlp(example)
print(output)
```
This will return a list of recognized tokens marked with label 'INSTRUCTION'.
## Training
It's based on the transformer architecture and specifically uses the [xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model from `ukr-models`, fine-tuned for the token classification task. The training data was carefully chosen to include a balanced distribution of titles containing instructions and those not containing instructions.
The dataset contains newspaper titles (~6k titles), with tokens representing instructions manually labeled.
## Evaluation
Model performance was evaluated using a held-out test set, again consisting of manually labeled newspaper titles. F1 - 0.9601, ACCURACY - 0.9968 for the 'INSTRUCTION' label
## License
This project is licensed under the terms of the MIT license.
## Citation
If you use our model or this repository in your research, please cite it as follows:
```
@misc{instruction-detection,
author = {Oleksandr Korovii},
title = {Instruction Detection Model},
year = {2023},
publisher = {HuggingFace Model Hub},
url = {https://huggingface.co/zeusfsx/instruction-detection},
note = {Accessed: 2023-06-20}
}
```
## Contact Information
For any questions or suggestions, feel free to open an issue in this repository.
Contributions to improve this model or the associated documentation are welcome!
|
CyberHarem/frima_nikke
|
CyberHarem
| 2023-08-06T07:31:34Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/frima_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T07:27:26Z |
---
license: mit
datasets:
- CyberHarem/frima_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of frima_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/frima_nikke.pt` as the embedding and `1500/frima_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `frima_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:---------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/frima_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/frima_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/frima_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/frima_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/frima_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/frima_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/frima_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/frima_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/frima_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/frima_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/frima_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/frima_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/frima_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/frima_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/frima_nikke.zip) |
|
principle/lukatuning1
|
principle
| 2023-08-06T07:02:14Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T07:02:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
chandrasutrisnotjhong/a2c-PandaReachDense-v2
|
chandrasutrisnotjhong
| 2023-08-06T06:49:45Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T11:59:26Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.92 +/- 0.43
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Naruke/LunarRadar-PPO
|
Naruke
| 2023-08-06T06:46:41Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T06:11:18Z |
---
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: -75.02 +/- 111.93
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': 100000
'learning_rate': 0.00025
'num_envs': 8
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 12
'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': 'Naruke/LunarRadar-PPO'
'batch_size': 1024
'minibatch_size': 256}
```
|
ofirmac/ofir
|
ofirmac
| 2023-08-06T06:25:33Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikisql",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-05T21:19:16Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- wikisql
model-index:
- name: ofir
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. -->
# ofir
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2732
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4116 | 1.0 | 4049 | 0.3393 |
| 0.353 | 2.0 | 8098 | 0.3000 |
| 0.3333 | 3.0 | 12147 | 0.2846 |
| 0.3113 | 4.0 | 16196 | 0.2758 |
| 0.302 | 5.0 | 20245 | 0.2732 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
CyberHarem/sakura_nikke
|
CyberHarem
| 2023-08-06T06:22:24Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/sakura_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T06:18:39Z |
---
license: mit
datasets:
- CyberHarem/sakura_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of sakura_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/sakura_nikke.pt` as the embedding and `1500/sakura_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `sakura_nikke`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/sakura_nikke.zip) |
| 1400 |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/sakura_nikke.zip) |
| 1300 |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/sakura_nikke.zip) |
| 1200 |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/sakura_nikke.zip) |
| 1100 |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/sakura_nikke.zip) |
| 1000 |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/sakura_nikke.zip) |
| 900 |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/sakura_nikke.zip) |
| 800 |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/sakura_nikke.zip) |
| 700 |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/sakura_nikke.zip) |
| 600 |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/sakura_nikke.zip) |
| 500 |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/sakura_nikke.zip) |
| 400 |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/sakura_nikke.zip) |
| 300 |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/sakura_nikke.zip) |
| 200 |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/sakura_nikke.zip) |
| 100 |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/sakura_nikke.zip) |
|
TheRains/cv9-special-batch12-base
|
TheRains
| 2023-08-06T06:20:26Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T04:59:17Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 23.77271681619508
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4079
- Wer: 23.7727
## 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: 12
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3536 | 1.45 | 1000 | 0.4083 | 26.1882 |
| 0.2171 | 2.9 | 2000 | 0.3794 | 24.4813 |
| 0.0604 | 4.35 | 3000 | 0.3954 | 24.5595 |
| 0.0531 | 5.81 | 4000 | 0.4079 | 23.7727 |
| 0.0245 | 7.26 | 5000 | 0.4240 | 23.9291 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sohailsiddiqui/marian-finetuned-kde4-en-to-fr
|
sohailsiddiqui
| 2023-08-06T06:05:45Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-05T21:00:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: sohailsiddiq99/marian-finetuned-kde4-en-to-fr
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. -->
# sohailsiddiq99/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0584
- Validation Loss: 0.8824
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0584 | 0.8824 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.11.0
|
asmitha26/falcon-medical
|
asmitha26
| 2023-08-06T05:59:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T05:24:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
chandrasutrisnotjhong/a2c-AntBulletEnv-v0
|
chandrasutrisnotjhong
| 2023-08-06T05:54:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T11:13:45Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1565.71 +/- 269.62
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bibin7/my-sneaker-xzg
|
bibin7
| 2023-08-06T05:49:37Z | 5 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T05:45:45Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Sneaker-xzg Dreambooth model trained by bibin7 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE331
Sample pictures of this concept:
|
srikanthsri/SRIKANTH-Falcon-finetune
|
srikanthsri
| 2023-08-06T05:18:46Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T04:59:09Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
umarzein/roberta-base-squad2-twitter-sent-ext-lora-balanced-continued
|
umarzein
| 2023-08-06T05:09:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T05:09:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
CyberHarem/chokai_azurlane
|
CyberHarem
| 2023-08-06T05:09:03Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/chokai_azurlane",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T05:05:42Z |
---
license: mit
datasets:
- CyberHarem/chokai_azurlane
pipeline_tag: text-to-image
tags:
- art
---
# Lora of chokai_azurlane
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/chokai_azurlane.pt` as the embedding and `1500/chokai_azurlane.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `chokai_azurlane`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-------------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/chokai_azurlane.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/chokai_azurlane.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/chokai_azurlane.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/chokai_azurlane.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/chokai_azurlane.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/chokai_azurlane.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/chokai_azurlane.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/chokai_azurlane.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/chokai_azurlane.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/chokai_azurlane.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/chokai_azurlane.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/chokai_azurlane.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/chokai_azurlane.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/chokai_azurlane.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/chokai_azurlane.zip) |
|
umarzein/roberta-base-squad2-twitter-sent-ext-lora-balanced
|
umarzein
| 2023-08-06T04:57:11Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-06T04:57:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
fromhell01/Reinforce-CartPolev1
|
fromhell01
| 2023-08-06T04:40:31Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-06T04:40:22Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPolev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 285.20 +/- 135.46
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
|
CyberHarem/maxwell_nikke
|
CyberHarem
| 2023-08-06T04:29:08Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/maxwell_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T04:23:45Z |
---
license: mit
datasets:
- CyberHarem/maxwell_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of maxwell_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/maxwell_nikke.pt` as the embedding and `1500/maxwell_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `maxwell_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-----------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/maxwell_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/maxwell_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/maxwell_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/maxwell_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/maxwell_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/maxwell_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/maxwell_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/maxwell_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/maxwell_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/maxwell_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/maxwell_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/maxwell_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/maxwell_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/maxwell_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/maxwell_nikke.zip) |
|
AmelieSchreiber/esm2_t6_8M_UR50D_lora_rna_binding_sites
|
AmelieSchreiber
| 2023-08-06T04:16:01Z | 6 | 0 |
peft
|
[
"peft",
"pytorch",
"esm",
"region:us"
] | null | 2023-08-03T04:01:04Z |
---
library_name: peft
---
# ESM-2 Fine-Tuned with PEFT LoRA
This is an attempt to use Parameter Efficient Fine Tuning ([PEFT](https://huggingface.co/docs/peft/conceptual_guides/lora)) with
Low Rank Adaptation ([LoRA](https://huggingface.co/docs/peft/task_guides/token-classification-lora))
for the ESM-2 model [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D).
The Github can be found [here](https://github.com/Amelie-Schreiber/esm2_loras/tree/main). The model is intended to serve as an
RNA binding site predictor for protein sequences. However, there seems to be something
going wrong either during training, or at inference, as the model seems to only predict non-binding sites.
The problem is likely in the training setup. Any feedback, comments, or discussion would be greatly appreciated.
## Training procedure
### Framework versions
- PEFT 0.4.0
|
526christian/526Mix-AnimeMac
|
526christian
| 2023-08-06T04:15:01Z | 0 | 3 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-06T06:45:49Z |
---
license: creativeml-openrail-m
---
<!-- Provide a quick summary of what the model is/does. -->
Image examples on CivitAI: https://civitai.com/posts/127435
An alternate upload of 526Mix-AnimeMac, a Stable Diffusion 1.5 model based on [526Mix](https://civitai.com/models/15022?modelVersionId=29212) and [Macaron Mix](https://civitai.com/models/11069/macaron-mix) by Nerfgun3. The model tries to transfer the whimiscal and artistic personality of 526Mix into an anime form.
<!-- Provide a longer summary of what this model is. -->
## USAGE TIPS
[bad-artist](https://huggingface.co/nick-x-hacker/bad-artist) (the base version, not the anime version) negative embedding recommended. To lean output more towards an anime style rather than semi-realistic, type anime in your prompt. I don't use any other negative embeddings and try to limit their use as much as possible, as they reduce model expressiveness. Since anime does ground the wacky things you can make a bit, said wacky things in your prompts may need extra weighting to come out in flying colors.
### Background
The goal of this model was to bring 526Mix's whimsical and artistic personality into anime (even though 526Mix can do a pretty cool anime style, it can be a bit finicky and likes giving people red/orange eyes all the time).
I'm not big on anime myself, so this was more of an experiment, but this model seems to be just as capable as I'd want it to be if I were an avid anime enjoyer. If you liked 526Mix's variety and personality (and cozy interior design), and also like anime, you'll probably have fun with this.
This model is a straightforward mix of 526Mix-V1.3.5, and Nerfgun3's Macaron Mix, the latter having had the noise offset added at 0.70 multiplier. This is done with a weighted sum at a 40:60 ratio. I always like Nerfgun3's art and embeddings, so I felt I could trust that model to be fairly in line with my own creative desires and expectations.
As always, I suggest going to the source models for the full experience, and Nerfgun3's Macaron Mix and Newartmodel aren't exceptions here.
This model has a baked Waifu Diffusion 1.4 VAE, same as 526Mix. No-bake version to follow.
|
Za88yes/Ris
|
Za88yes
| 2023-08-06T04:14:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-05T19:46:10Z |
---
license: creativeml-openrail-m
---
|
mrizalf7/t5-small-indosum-2
|
mrizalf7
| 2023-08-06T04:00:49Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-02T15:51:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-indosum-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. -->
# t5-small-indosum-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3772
- Rouge1: 15.3275
- Rouge2: 11.9241
- Rougel: 15.0748
- Rougelsum: 15.1979
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.4206 | 1.0 | 2230 | 0.3852 | 15.3515 | 11.9406 | 15.1018 | 15.2234 | 19.0 |
| 0.4058 | 2.0 | 4460 | 0.3783 | 15.3331 | 11.9321 | 15.0835 | 15.2069 | 19.0 |
| 0.3977 | 3.0 | 6690 | 0.3772 | 15.3275 | 11.9241 | 15.0748 | 15.1979 | 19.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
TheRains/cv9-special-batch8-base
|
TheRains
| 2023-08-06T03:55:46Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T02:44:22Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 23.19300667126754
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3793
- Wer: 23.1930
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4648 | 0.97 | 1000 | 0.4258 | 27.6236 |
| 0.2992 | 1.94 | 2000 | 0.3831 | 24.4444 |
| 0.1597 | 2.9 | 3000 | 0.3778 | 23.6163 |
| 0.1137 | 3.87 | 4000 | 0.3793 | 23.1930 |
| 0.0632 | 4.84 | 5000 | 0.3893 | 23.5013 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
TheRains/cv9-special-batch4-base
|
TheRains
| 2023-08-06T03:50:23Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-06T02:30:42Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 23.40004600874166
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3697
- Wer: 23.4000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5013 | 0.48 | 1000 | 0.4523 | 28.5990 |
| 0.4145 | 0.97 | 2000 | 0.4067 | 25.8109 |
| 0.2437 | 1.45 | 3000 | 0.3821 | 24.3800 |
| 0.2566 | 1.94 | 4000 | 0.3695 | 23.9798 |
| 0.1161 | 2.42 | 5000 | 0.3697 | 23.4000 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dnagpt/human_gpt2-v1
|
dnagpt
| 2023-08-06T03:47:10Z | 160 | 9 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"biology",
"en",
"dataset:dnagpt/human_genome_GCF_009914755.1",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-08-05T09:41:39Z |
---
license: apache-2.0
datasets:
- dnagpt/human_genome_GCF_009914755.1
language:
- en
metrics:
- perplexity
library_name: transformers
tags:
- biology
---
dna language model trained using gpt2. using human genome data.
Key features of our dangpt models:
1. BPE tokenization instead of k-mers (DNABERT, DNABERT2 also use BPE)
2. GPT model, but not bert(DNABERT, GENA_LM)
3. pre-training on the latest T2T human genome assembly
4. details:https://github.com/maris205/dnagpt. includes train/bpe code
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('dnagpt/human_gpt2-v1')
tokenizer.tokenize("GAGCACATTCGCCTGCGTGCGCACTCACACACACGTTCAAAAAGAGTCCATTCGATTCTGGCAGTAG")
#result: [G','AGCAC','ATTCGCC',....]
model = AutoModel.from_pretrained('dnagpt/human_gpt2-v1')
import torch
dna = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC"
inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"]
hidden_states = model(inputs)[0] # [1, sequence_length, 768]
# embedding with mean pooling
embedding_mean = torch.mean(hidden_states[0], dim=0)
print(embedding_mean.shape) # expect to be 768
# embedding with max pooling
embedding_max = torch.max(hidden_states[0], dim=0)[0]
print(embedding_max.shape) # expect to be 768
|
govindkrishnan123/my-pet-dog
|
govindkrishnan123
| 2023-08-06T03:24:13Z | 13 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T03:17:30Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by govindkrishnan123 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE97
Sample pictures of this concept:
|
Tien203/fine-tune-llama
|
Tien203
| 2023-08-06T03:21:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T10:16:41Z |
---
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 0.5.0.dev0
|
yashgoenka/gorilla-llama-2-7B-QLoRA
|
yashgoenka
| 2023-08-06T03:20:39Z | 15 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"llama-2-7b",
"gorilla",
"qlora",
"api",
"dataset:yashgoenka/gorilla-16k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T22:49:08Z |
---
license: apache-2.0
datasets:
- yashgoenka/gorilla-16k
pipeline_tag: text-generation
tags:
- llama
- llama-2
- llama-2-7b
- gorilla
- qlora
- api
library_name: transformers
---
|
layyyy/sd
|
layyyy
| 2023-08-06T03:19:59Z | 0 | 0 | null |
[
"onnx",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-06T14:45:03Z |
---
license: creativeml-openrail-m
---
|
lentejasconacheto/resi
|
lentejasconacheto
| 2023-08-06T03:16:10Z | 0 | 0 | null |
[
"es",
"en",
"license:openrail",
"region:us"
] | null | 2023-08-06T03:13:23Z |
---
license: openrail
language:
- es
- en
---
|
mrizalf7/t5-small-indosum-1
|
mrizalf7
| 2023-08-06T03:11:53Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-02T15:51:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-indosum-1
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. -->
# t5-small-indosum-1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4057
- Rouge1: 15.2719
- Rouge2: 11.8877
- Rougel: 15.0297
- Rougelsum: 15.1504
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.482 | 1.0 | 4460 | 0.4370 | 15.1965 | 11.8078 | 14.9509 | 15.0704 | 19.0 |
| 0.4464 | 2.0 | 8920 | 0.4110 | 15.2567 | 11.8679 | 15.0112 | 15.132 | 19.0 |
| 0.4421 | 3.0 | 13380 | 0.4057 | 15.2719 | 11.8877 | 15.0297 | 15.1504 | 19.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
TheRains/cv9-special-batch8-small
|
TheRains
| 2023-08-06T03:07:36Z | 81 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_9_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-05T10:37:49Z |
---
language:
- id
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: Whisper Small Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_9_0 id
type: mozilla-foundation/common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 12.472969864274212
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Indonesian
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_9_0 id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2873
- Wer: 12.4730
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3041 | 0.97 | 1000 | 0.2612 | 14.7090 |
| 0.1437 | 1.94 | 2000 | 0.2485 | 14.0419 |
| 0.0555 | 2.9 | 3000 | 0.2530 | 12.8778 |
| 0.0173 | 3.87 | 4000 | 0.2704 | 12.5880 |
| 0.0067 | 4.84 | 5000 | 0.2873 | 12.4730 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
c72599/whisper-tiny-minds14
|
c72599
| 2023-08-06T03:01:09Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-05T18:00:28Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-minds14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.2893081761006289
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-minds14
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7197
- Wer Ortho: 0.3028
- Wer: 0.2893
## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0008 | 17.86 | 500 | 0.7197 | 0.3028 | 0.2893 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.14.1
- Tokenizers 0.13.3
|
CyberHarem/yulha_nikke
|
CyberHarem
| 2023-08-06T02:37:54Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/yulha_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T02:32:32Z |
---
license: mit
datasets:
- CyberHarem/yulha_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of yulha_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/yulha_nikke.pt` as the embedding and `1500/yulha_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `yulha_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:---------------------------------|
| 1500 |  | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/yulha_nikke.zip) |
| 1400 |  | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/yulha_nikke.zip) |
| 1300 |  | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/yulha_nikke.zip) |
| 1200 |  | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/yulha_nikke.zip) |
| 1100 |  | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/yulha_nikke.zip) |
| 1000 |  | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/yulha_nikke.zip) |
| 900 |  | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/yulha_nikke.zip) |
| 800 |  | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/yulha_nikke.zip) |
| 700 |  | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/yulha_nikke.zip) |
| 600 |  | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/yulha_nikke.zip) |
| 500 |  | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/yulha_nikke.zip) |
| 400 |  | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/yulha_nikke.zip) |
| 300 |  | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/yulha_nikke.zip) |
| 200 |  | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/yulha_nikke.zip) |
| 100 |  | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/yulha_nikke.zip) |
|
StarFox7/Llama-2-ko-7B-ggml
|
StarFox7
| 2023-08-06T02:32:51Z | 0 | 4 | null |
[
"ko",
"arxiv:2307.09288",
"license:llama2",
"region:us"
] | null | 2023-08-04T07:09:51Z |
---
license: llama2
language:
- ko
---
# Llama-2-ko-7b-ggml
<img src=https://huggingface.co/StarFox7/Llama-2-ko-7B-ggml/resolve/main/cute.png style="max-width: 200px; width: 100%" />
Llama-2-ko-7b-ggml 은 [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) 의 **GGML** 포맷 모델입니다.
- Llama2 tokenizer 에 [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) 에서 사용된 한국어 Additaional Token 을 반영하여 생성했습니다.
- **GGML** 포맷 모델은 [llama.cpp](https://github.com/ggerganov/llama.cpp) 를 사용하여 C/C++ 기반으로 Inference 합니다.
- **GGML** 포맷 모델은 비교적 낮은 사양의 컴퓨팅 자원에서도 Inference 가능합니다. ( 예: 4비트 양자화 모델 (q4) 은 CPU,7-8GB RAM 환경에서 Inference 가능 )
- [llama.cpp](https://github.com/ggerganov/llama.cpp) 의 Python Binding 패키지인 [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) 을 사용하면 python 환경에서도 Inference 가능합니다.
참고로, [Llama-2-ko-7b-chat-ggml](https://huggingface.co/StarFox7/Llama-2-ko-7B-chat-ggml) 에서 [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) 에 [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2) 을 추가 학습한 [kfkas/Llama-2-ko-7b-chat](https://huggingface.co/kfkas/Llama-2-ko-7b-Chat) 의 **GGML** 포맷 모델을 찾을 수 있습니다.
---
# 양자화 (Quantization)
이 Repository 에는 [llama.cpp](https://github.com/ggerganov/llama.cpp) 에서 제공하는 quantization method 를 적용한 f16, q4_0, q4_1, q5_0, q5_1, q8_0 모델을 포함합니다. 각 모델의 File Size 는 다음과 같습니다.
| Model| Measure |q4_0|q4_1|q5_0|q5_1|q8_0|f16|f32|
|------|---------|------|------|------|-----|----------|------|-------------|
| 7B |file size|3.9G | 4.3G | 4.7G | 5.2G | 7.2G |13.5G|27.4G|
---
# Inference Code Example (Python)
다음은 Inference 를 위한 간단한 Example Code 입니다. [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) 그리고 이 Repository 의 Llama-2-ko-7b-ggml-q4_0.bin 가 필요합니다.
```python
# !pip install llama-cpp-python # llama-cpp-python 이 설치되어 있지 않다면 주석을 해제하여 설치합니다.
from llama_cpp import Llama
llm = Llama(model_path = 'Llama-2-ko-7b-ggml-q4_0.bin',
n_ctx=1024,
# n_gpu_layers=1 #gpu 가속을 원하는 경우 주석을 해제하고 Metal(Apple M1) 은 1, Cuda(Nvidia) 는 Video RAM Size 를 고려하여 적정한 수치를 입력합니다.)
)
output = llm("Q: 인생에 대해서 설명하시오. A: ", max_tokens=1024, stop=["Q:", "\n"], echo=True)
print( output['choices'][0]['text'].replace('▁',' ') )
#출력 결과
'''
Q: 인생에 대해서 설명하시오.
A: 20대에는 모든 것을 할 수 있는 시기로, 자신이 하고 싶은 일과 하고 싶은 공부를 선택해 공부할 수 있고 자신이 이루고 싶은 것들을 성취하고 꿈꿀 수 있는 시기라고 생각했습니다.
이러한 이유로 20대의 저를 설명하라고 한다면 '꿈이 많은 젊은이'가 가장 어울릴 듯합니다. 어렸을 때는 마냥 어른이 되고 싶었고, 중학생 때에는 빨리 고등학교에 올라가고 싶었습니다.
고등학교에 올라가서도 저는 대학에 진학하고 싶은 마음에 공부를 열심히 하였습니다. 대학에 입학한 후에도 저의 공부는 계속되었습니다. 하지만 2학년 정도 되었을 때,
'내가 하고 있는 것이 정말 내가 하고 싶은 일일까?' 라는 생각이 들기 시작했습니다. 이런 고민 끝에 저는 방황을 하였고 결국 제가 정말 원하는 일이 무엇인지 몰랐기 때문에 학교를 그만두기로 결심하게 되었습니다.
저는 인생의 목표는 행복이라고 생각했고, 행복한 삶을 살기 위해 모든 것을 할 수 있다는 20대에 제가 하고 싶었던 일을 선택해 제 자신을 발전시키고 성취해가며 꿈을 이루려고 하였습니다.
그래서 저는 '내가 하고 싶은 일과 내가 잘 할 수 있는 일'을 찾기 위해 여러 곳을 둘러보고 경험하였습니다. 여러 곳을 둘러보다 보니 저는 자신이 하고 싶은 일을 찾아내고, 잘 할 수 있다는 자신감을 가지게 되었습니다.
그 후 저는 제가 좋아하는 것을 찾고 성취해가며 꿈을 이루기 위해 노력하고 있습니다.
'''
```
---
> Below is the original model card of the Llama-2 model.
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
CyberHarem/pepper_nikke
|
CyberHarem
| 2023-08-06T02:14:37Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/pepper_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T02:11:25Z |
---
license: mit
datasets:
- CyberHarem/pepper_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of pepper_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/pepper_nikke.pt` as the embedding and `1500/pepper_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `pepper_nikke`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/pepper_nikke.zip) |
| 1400 |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/pepper_nikke.zip) |
| 1300 |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/pepper_nikke.zip) |
| 1200 |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/pepper_nikke.zip) |
| 1100 |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/pepper_nikke.zip) |
| 1000 |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/pepper_nikke.zip) |
| 900 |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/pepper_nikke.zip) |
| 800 |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/pepper_nikke.zip) |
| 700 |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/pepper_nikke.zip) |
| 600 |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/pepper_nikke.zip) |
| 500 |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/pepper_nikke.zip) |
| 400 |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/pepper_nikke.zip) |
| 300 |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/pepper_nikke.zip) |
| 200 |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/pepper_nikke.zip) |
| 100 |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/pepper_nikke.zip) |
|
hugmeonce/finetuning-sentiment-model-3000-samples
|
hugmeonce
| 2023-08-06T01:38:24Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-06T01:31:51Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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: 0.87
- name: F1
type: f1
value: 0.8737864077669903
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.3363
- Accuracy: 0.87
- F1: 0.8738
## 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Angry-Wizard/map-training
|
Angry-Wizard
| 2023-08-06T01:21:52Z | 40 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-06T01:18:35Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Map_Training Dreambooth model trained by Angry-Wizard with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
CyberHarem/jackal_nikke
|
CyberHarem
| 2023-08-06T01:11:06Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/jackal_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T01:06:58Z |
---
license: mit
datasets:
- CyberHarem/jackal_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of jackal_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/jackal_nikke.pt` as the embedding and `1500/jackal_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `jackal_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/jackal_nikke.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/jackal_nikke.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/jackal_nikke.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/jackal_nikke.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/jackal_nikke.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/jackal_nikke.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/jackal_nikke.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/jackal_nikke.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/jackal_nikke.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/jackal_nikke.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/jackal_nikke.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/jackal_nikke.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/jackal_nikke.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/jackal_nikke.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/jackal_nikke.zip) |
|
jrzhang/CSKG_Roberta_large
|
jrzhang
| 2023-08-06T00:32:26Z | 0 | 1 | null |
[
"en",
"license:openrail++",
"region:us"
] | null | 2023-08-06T00:31:35Z |
---
license: openrail++
language:
- en
---
|
CyberHarem/liter_nikke
|
CyberHarem
| 2023-08-06T00:28:52Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/liter_nikke",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-06T00:25:10Z |
---
license: mit
datasets:
- CyberHarem/liter_nikke
pipeline_tag: text-to-image
tags:
- art
---
# Lora of liter_nikke
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/liter_nikke.pt` as the embedding and `1500/liter_nikke.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `liter_nikke`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:---------------------------------|
| 1500 |  | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/liter_nikke.zip) |
| 1400 |  | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/liter_nikke.zip) |
| 1300 |  | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/liter_nikke.zip) |
| 1200 |  | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/liter_nikke.zip) |
| 1100 |  | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/liter_nikke.zip) |
| 1000 |  | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/liter_nikke.zip) |
| 900 |  | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/liter_nikke.zip) |
| 800 |  | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/liter_nikke.zip) |
| 700 |  | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/liter_nikke.zip) |
| 600 |  | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/liter_nikke.zip) |
| 500 |  | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/liter_nikke.zip) |
| 400 |  | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/liter_nikke.zip) |
| 300 |  | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/liter_nikke.zip) |
| 200 |  | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/liter_nikke.zip) |
| 100 |  | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/liter_nikke.zip) |
|
AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-localization
|
AmelieSchreiber
| 2023-08-06T00:28:13Z | 219 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"esm",
"text-classification",
"esm2",
"protein language model",
"biology",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T23:26:03Z |
---
license: mit
language:
- en
library_name: transformers
tags:
- esm
- esm2
- protein language model
- biology
---
# ESM-2 (`esm2_t6_8M_UR50D`)
This is a fine-tuned version of [ESM-2](https://huggingface.co/facebook/esm2_t6_8M_UR50D) for sequence classification
that categorizes protein sequences into two classes, either "cystolic" or "membrane".
## Training and Accuracy
The model is trained using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb)
and achieved an eval accuracy of 94.83163664839468 %.
## Using the Model
To use try running:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Initialize the tokenizer and model
model_path_directory = "AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-localization"
tokenizer = AutoTokenizer.from_pretrained(model_path_directory)
model = AutoModelForSequenceClassification.from_pretrained(model_path_directory)
# Define a function to predict the category of a protein sequence
def predict_category(sequence):
# Tokenize the sequence and convert it to tensor format
inputs = tokenizer(sequence, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
# Make prediction
with torch.no_grad():
logits = model(**inputs).logits
# Determine the category with the highest score
predicted_class = torch.argmax(logits, dim=1).item()
# Return the category: 0 for cytosolic, 1 for membrane
return "cytosolic" if predicted_class == 0 else "membrane"
# Example sequence
new_protein_sequence = "MTQRAGAAMLPSALLLLCVPGCLTVSGPSTVMGAVGESLSVQCRYEEKYKTFNKYWCRQPCLPIWHEMVETGGSEGVVRSDQVIITDHPGDLTFTVTLENLTADDAGKYRCGIATILQEDGLSGFLPDPFFQVQVLVSSASSTENSVKTPASPTRPSQCQGSLPSSTCFLLLPLLKVPLLLSILGAILWVNRPWRTPWTES"
# Predict the category
category = predict_category(new_protein_sequence)
print(f"The predicted category for the sequence is: {category}")
```
|
RichBro/squad_gpt2
|
RichBro
| 2023-08-05T23:46:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T04:24:54Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
timjwhite/whisper-tiny-dv
|
timjwhite
| 2023-08-05T23:43:44Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-05T11:31:30Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-dv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[-19%:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3484562066792691
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-dv
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7263
- Wer Ortho: 0.3483
- Wer: 0.3485
## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0008 | 17.24 | 500 | 0.6662 | 0.3483 | 0.3491 |
| 0.0002 | 34.48 | 1000 | 0.7263 | 0.3483 | 0.3485 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
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