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| library_name
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yezune/xlm-roberta-base-finetuned-panx-en | yezune | 2023-08-10T09:55:26Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-10T09:54:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6877426511369938
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3900
- F1: 0.6877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1495 | 1.0 | 50 | 0.5817 | 0.4923 |
| 0.5096 | 2.0 | 100 | 0.4302 | 0.6313 |
| 0.3706 | 3.0 | 150 | 0.3900 | 0.6877 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
|
yezune/xlm-roberta-base-finetuned-panx-fr | yezune | 2023-08-10T09:52:25Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-10T09:50:25Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: validation
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8341708542713568
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2671
- F1: 0.8342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5836 | 1.0 | 191 | 0.3316 | 0.7831 |
| 0.26 | 2.0 | 382 | 0.2738 | 0.8256 |
| 0.1681 | 3.0 | 573 | 0.2671 | 0.8342 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
|
iliyaML/t5-small-billsum | iliyaML | 2023-08-10T09:52:25Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-08-10T09:42:37Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: t5-small-billsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1528
---
<!-- 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-billsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5246
- Rouge1: 0.1528
- Rouge2: 0.0586
- Rougel: 0.1291
- Rougelsum: 0.1292
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8551 | 0.1284 | 0.0348 | 0.1081 | 0.1085 | 19.0 |
| No log | 2.0 | 124 | 2.6404 | 0.1373 | 0.0453 | 0.1147 | 0.1147 | 19.0 |
| No log | 3.0 | 186 | 2.5665 | 0.1423 | 0.0494 | 0.1195 | 0.1192 | 19.0 |
| No log | 4.0 | 248 | 2.5342 | 0.149 | 0.055 | 0.1259 | 0.1257 | 19.0 |
| No log | 5.0 | 310 | 2.5246 | 0.1528 | 0.0586 | 0.1291 | 0.1292 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
yezune/xlm-roberta-base-finetuned-panx-de-fr | yezune | 2023-08-10T09:49:34Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-10T09:44:57Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1627
- F1: 0.8586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.291 | 1.0 | 715 | 0.1809 | 0.8299 |
| 0.1468 | 2.0 | 1430 | 0.1512 | 0.8516 |
| 0.0936 | 3.0 | 2145 | 0.1627 | 0.8586 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
|
aycandv/ppo-LunarLander-v2 | aycandv | 2023-08-10T09:36:26Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T08:29:19Z | ---
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: 277.41 +/- 13.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
trieudemo11/llama_7b_attributes_prompt_alpaca_2_stable_tested | trieudemo11 | 2023-08-10T09:31:48Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-09T17:44:36Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
kzzlar/ppo-LunarLander-v2 | kzzlar | 2023-08-10T09:31:27Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T09:30:58Z | ---
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: 207.59 +/- 63.60
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
...
```
|
rossevine/wav2vec2_Indonesia_4 | rossevine | 2023-08-10T09:29:52Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-08-06T15:39:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2_Indonesia_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_Indonesia_4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3147
- Wer: 0.5914
## Model description
Model yang dilatih dengan data train common voice dan data test data perkuliahan
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9949 | 3.23 | 400 | 1.3340 | 0.8916 |
| 0.4469 | 6.45 | 800 | 1.0507 | 0.6859 |
| 0.2003 | 9.68 | 1200 | 1.1115 | 0.6369 |
| 0.1432 | 12.9 | 1600 | 1.1307 | 0.6297 |
| 0.1138 | 16.13 | 2000 | 1.2157 | 0.6369 |
| 0.089 | 19.35 | 2400 | 1.2834 | 0.6058 |
| 0.0712 | 22.58 | 2800 | 1.3283 | 0.5947 |
| 0.057 | 25.81 | 3200 | 1.3345 | 0.5827 |
| 0.0467 | 29.03 | 3600 | 1.3147 | 0.5914 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
WinSenX/sd-class-butterflies-32 | WinSenX | 2023-08-10T09:27:17Z | 30 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-08-10T09:26:59Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('WinSenX/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
abin-regi/my-pet-dog-xzh | abin-regi | 2023-08-10T09:26:36Z | 0 | 0 | null | [
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-10T09:23:35Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-xzh Dreambooth model trained by abin-regi following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: VJCET421
Sample pictures of this concept:
|
mardrake/lora-trained-xl-colab | mardrake | 2023-08-10T09:23:38Z | 4 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| text-to-image | 2023-08-10T08:05:54Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - mardrake/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
MRNH/proximal-policy-optimization-LunarLander-v2 | MRNH | 2023-08-10T09:15:05Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T08:27:37Z | ---
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: 275.81 +/- 26.46
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
...
```
|
KeKu/llama2-french | KeKu | 2023-08-10T09:06:12Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T09:06:06Z | ---
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
|
cuixing/textual_inversion_object_style_vangoghsingle08101439 | cuixing | 2023-08-10T09:03:40Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-08-10T06:40:34Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - cuixing/textual_inversion_object_style_vangoghsingle08101439
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
Dytorch/textual_inversion_cat | Dytorch | 2023-08-10T08:50:46Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-08-09T02:43:24Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - Dytorch/textual_inversion_cat
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
taehoon1lee/Reinforce-unit4-11 | taehoon1lee | 2023-08-10T08:49:20Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T08:48:53Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-unit4-11
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 50.60 +/- 41.57
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
|
rainishere/llama2-qlora-finetunined-french-test-rainishere | rainishere | 2023-08-10T08:45:03Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T08:44:54Z | ---
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
|
TigerResearch/tigerbot-7b-sft-v1-4bit | TigerResearch | 2023-08-10T08:43:46Z | 7 | 6 | transformers | [
"transformers",
"bloom",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-01T03:38:20Z | ---
license: apache-2.0
---
<div style="width: 100%;">
<img src="https://github.com/TigerResearch/TigerBot/blob/main/image/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;">
</div>
<p align="center">
<font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font>
</p>
<p align="center">
🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>
This is a 4-bit GPTQ version of the [Tigerbot 7B sft](https://huggingface.co/TigerResearch/tigerbot-7b-sft).
It was quantized to 4bit using: https://github.com/TigerResearch/TigerBot/tree/main/gptq
## How to download and use this model in github: https://github.com/TigerResearch/TigerBot
Here are commands to clone the TigerBot and install.
```
conda create --name tigerbot python=3.8
conda activate tigerbot
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
git clone https://github.com/TigerResearch/TigerBot
cd TigerBot
pip install -r requirements.txt
```
Inference with command line interface
```
cd TigerBot/gptq
CUDA_VISIBLE_DEVICES=0 python tigerbot_infer.py TigerResearch/tigerbot-7b-sft-4bit-128g --wbits 4 --groupsize 128 --load TigerResearch/tigerbot-7b-sft-4bit-128g/tigerbot-7b-4bit-128g.pt
```
|
iliyaML/falcon-7b-openassistant-guanaco | iliyaML | 2023-08-10T08:42:24Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
]
| null | 2023-08-10T05:08:03Z | ---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-openassistant-guanaco
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon-7b-openassistant-guanaco
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
smangrul/peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab | smangrul | 2023-08-10T08:35:19Z | 15 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:bigcode/starcoderbase-3b",
"base_model:adapter:bigcode/starcoderbase-3b",
"license:bigcode-openrail-m",
"region:us"
]
| null | 2023-08-09T20:17:40Z | ---
license: bigcode-openrail-m
base_model: bigcode/starcoderbase-3b
tags:
- generated_from_trainer
model-index:
- name: peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab
results: []
library_name: peft
---
<!-- 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. -->
# peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab
This model is a fine-tuned version of [bigcode/starcoderbase-3b](https://huggingface.co/bigcode/starcoderbase-3b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 30
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8168 | 0.05 | 100 | 0.7807 |
| 0.7961 | 0.1 | 200 | 0.7197 |
| 0.7837 | 0.15 | 300 | 0.6603 |
| 0.7053 | 0.2 | 400 | 0.6371 |
| 0.6132 | 0.25 | 500 | 0.6282 |
| 0.6584 | 0.3 | 600 | 0.6107 |
| 0.621 | 0.35 | 700 | 0.5934 |
| 0.6961 | 0.4 | 800 | 0.5877 |
| 0.592 | 0.45 | 900 | 0.5833 |
| 0.6967 | 0.5 | 1000 | 0.5746 |
| 0.6382 | 0.55 | 1100 | 0.5563 |
| 0.6815 | 0.6 | 1200 | 0.5436 |
| 0.5483 | 0.65 | 1300 | 0.5439 |
| 0.7172 | 0.7 | 1400 | 0.5401 |
| 0.5479 | 0.75 | 1500 | 0.5390 |
| 0.9422 | 0.8 | 1600 | 0.5357 |
| 0.5503 | 0.85 | 1700 | 0.5303 |
| 0.5928 | 0.9 | 1800 | 0.5322 |
| 0.5513 | 0.95 | 1900 | 0.5176 |
| 0.6314 | 1.0 | 2000 | 0.5038 |
### Framework versions
- PEFT 0.5.0.dev0
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Norquinal/llama-2-7b-claude-instruct | Norquinal | 2023-08-10T08:23:03Z | 18 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:Norquinal/claude_multi_instruct_1k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T03:40:43Z | ---
datasets:
- Norquinal/claude_multi_instruct_1k
---
This is a [LLaMA-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) model fine-tuned using QLoRA (4-bit precision) on my [claude_multi_instruct_1k](https://huggingface.co/datasets/Norquinal/claude_multi_instruct_1k) dataset.
Do not take this model very seriously, it is probably not very good. I haven't a clue of what I'm doing. I just thought it was a fun thing to make.
## Prompt Format
The model was finetuned with the following prompt format:
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
## Example Usage
>\### Instruction:
>Deconstruct five major technological innovations in 20th and 21st centuries—Internet, personal computing, mobile phones, artificial intelligence, and electric vehicles. For each innovation, provide information on their early origins, key inventors or pioneers, important milestones and breakthroughs, adoption and proliferation into mainstream use, economic effects, societal impact, ethical considerations, and potential future developments. Discuss how these innovations reshaped the way people work, communicate, and live, and explore the synergies or connections among these technologies and the potential for future transformative advancements.
>\### Response:
>The Internet has revolutionized communication, commerce, education, entertainment, and more since its commercialization in the late 1980s. It was initially developed by researchers at universities like Stanford University's ARPANET project in the 1960s. Tim Berners-Lee created the World Wide Web in 1989 as part of his PhD thesis. The first web browser Mosaic launched in 1993 popularizing the concept of surfing the internet. In 1995, Netscape released the first widely used graphical web browser which accelerated growth. By 1997, there were over 1 million websites online. Today, billions of users access the global network daily through devices like smartphones and tablets.
>
>Personal computers have transformed productivity, creativity, and knowledge sharing since the Apple II computer debuted in 1977. Steve Jobs founded Apple Computer Inc. in 1976 to sell the Apple I computer designed by Steve Wozniak. IBM introduced the first commercially successful PC in 1981. Microsoft Windows operating system launched in 1985 cemented the dominance of the x86 architecture. Personal computers democratize technology allowing individuals to create content, run businesses, and participate in digital culture. They also enable remote working and telecommuting.
>
>Mobile phones have become ubiquitous due to rapid improvements in battery life, processing power, connectivity speeds, and user interface design. Martin Cooper made the world's first handheld cellular phone call from Motorola DynaTAC 800x in April 1973. However, it took decades before mass market adoption due to high cost and limited coverage area. With the introduction of GSM standard in Europe in 1991, prices dropped significantly making them affordable for consumers. Smartphone revolution began around 2007 when iPhone and Android platforms emerged. Mobile apps became prevalent after App Store launch in 2008. As of 2020, nearly 4 billion people own a mobile phone globally. Mobile phones allow instantaneous communication anytime anywhere enabling new forms of social interaction and commerce.
>
>Artificial Intelligence (AI) systems are becoming increasingly sophisticated leveraging machine learning algorithms trained on vast amounts of data. AI research dates back to the 1950s but only recently achieved human level performance in tasks such as image recognition, speech transcription, and game playing. DeepMind's AlphaGo program defeated Lee Sedol in Go match in 2016 demonstrating superhuman capabilities. Google Brain team published papers on neural networks in 2012 paving the way for modern deep learning models. Companies like Facebook, Amazon, Netflix, Uber rely heavily on AI to improve customer experience, optimize operations, and automate decision making. While AI holds great promise for solving complex problems, concerns remain about job displacement, bias in training datasets, and lack of transparency in model predictions.
>
>Electric cars represent one of the most promising clean energy solutions to reduce carbon emissions and dependence on fossil fuels. Electric motors have been used in trains and boats since mid-19th century. But it wasn't until Tesla Model S debut in 2012 that EVs gained widespread attention. Elon Musk's vision of sustainability and zero emission transportation helped propel industry forward. China leads the world in EV sales followed closely by Norway. Cost parity between gasoline and electric vehicles will likely occur within next decade. Once this happens, EVs could displace up to half of all passenger vehicle miles traveled by 2030 according to BloombergNEF analysis. Challenges include range anxiety, charging infrastructure, and higher initial purchase price compared to internal combustion engine vehicles. Nevertheless, EVs offer significant environmental benefits if deployed rapidly enough to meet climate goals.
>
>In summary, these technologies share common characteristics of exponential progress driven by Moore's Law, network effects, and feedback loops. Their interconnectedness creates powerful synergy amplifying their collective impact. Technologists must balance risks versus rewards while ensuring equitable distribution of benefits across society. Future innovations may build upon these foundational building blocks to unlock even greater possibilities yet unknown. We stand at the dawn of another technological era where imagination meets reality to shape our shared destiny. How we choose to navigate this journey together remains the ultimate test of human ingenuity and wisdom.
|
TheTravellingEngineer/llama2-7b-chat-hf-v4 | TheTravellingEngineer | 2023-08-10T08:18:44Z | 1,547 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T07:28:43Z | The base model is meta's Llama-2-7b-chat-hf. It was finetuned using SFT and the openassistant/oasst1 dataset and the model prompt is similar to the original Guanaco model.
This repo contains the merged fp16 model.
**Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.**
---
- license:
- llama2 <br>
- datasets:
- openassistant/oasst1 <br>
- language:
- en <br>
- reference: https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da
--- |
Geotrend/distilbert-base-en-es-zh-cased | Geotrend | 2023-08-10T08:02:08Z | 142 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"en",
"es",
"zh",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:04Z | ---
language:
- multilingual
- en
- es
- zh
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-es-zh-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
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/distilbert-base-en-es-zh-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-es-zh-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{smallermdistilbert,
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 [email protected] for any question, feedback or request. |
Rida06/bert-finetuned-ner | Rida06 | 2023-08-10T07:57:30Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-08T08:29:16Z | ---
license: apache-2.0
base_model: Bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: Rida06/bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rida06/bert-finetuned-ner
This model is a fine-tuned version of [Bert-base-cased](https://huggingface.co/Bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1762
- Validation Loss: 0.0705
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1762 | 0.0705 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.2
- Tokenizers 0.11.0
|
Stevross/Astrid-LLama-3B-CPU | Stevross | 2023-08-10T07:56:30Z | 35 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T00:38:47Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: true
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Stevross/Astrid-LLama-3B-CPU",
torch_dtype="auto",
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Stevross/Astrid-LLama-3B-CPU",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Stevross/Astrid-LLama-3B-CPU",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Stevross/Astrid-LLama-3B-CPU" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=False,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 3200, padding_idx=0)
(layers): ModuleList(
(0-25): 26 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=3200, out_features=3200, bias=False)
(k_proj): Linear(in_features=3200, out_features=3200, bias=False)
(v_proj): Linear(in_features=3200, out_features=3200, bias=False)
(o_proj): Linear(in_features=3200, out_features=3200, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=3200, out_features=8640, bias=False)
(down_proj): Linear(in_features=8640, out_features=3200, bias=False)
(up_proj): Linear(in_features=3200, out_features=8640, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=3200, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=Stevross/Astrid-LLama-3B-CPU --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
Stevross/Astrid-LLama-3B-GPU | Stevross | 2023-08-10T07:56:12Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T00:05:32Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: true
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Stevross/Astrid-LLama-3B-GPU",
torch_dtype="auto",
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Stevross/Astrid-LLama-3B-GPU",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Stevross/Astrid-LLama-3B-GPU",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Stevross/Astrid-LLama-3B-GPU" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=False,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 3200, padding_idx=0)
(layers): ModuleList(
(0-25): 26 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=3200, out_features=3200, bias=False)
(k_proj): Linear(in_features=3200, out_features=3200, bias=False)
(v_proj): Linear(in_features=3200, out_features=3200, bias=False)
(o_proj): Linear(in_features=3200, out_features=3200, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=3200, out_features=8640, bias=False)
(down_proj): Linear(in_features=8640, out_features=3200, bias=False)
(up_proj): Linear(in_features=3200, out_features=8640, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=3200, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=Stevross/Astrid-LLama-3B-GPU --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
newronai/llama-2-7b-Chat-QLoRA-Trial1 | newronai | 2023-08-10T07:32:04Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T07:31:16Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
rossevine/wav2vec2_indonesia_6 | rossevine | 2023-08-10T07:27:07Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-08-10T05:34:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2_Indonesia_6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_Indonesia_6
This model is a fine-tuned version of [facebook/wav2vec2-base-100h](https://huggingface.co/facebook/wav2vec2-base-100h) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7559
- Wer: 1.0232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1807 | 3.23 | 400 | 1.3655 | 1.0052 |
| 0.5608 | 6.45 | 800 | 1.3604 | 1.0312 |
| 0.3302 | 9.68 | 1200 | 1.3724 | 1.0355 |
| 0.2405 | 12.9 | 1600 | 1.4350 | 1.0142 |
| 0.1883 | 16.13 | 2000 | 1.5079 | 1.0213 |
| 0.1535 | 19.35 | 2400 | 1.5038 | 1.0251 |
| 0.1307 | 22.58 | 2800 | 1.7026 | 1.0189 |
| 0.1104 | 25.81 | 3200 | 1.7072 | 1.0090 |
| 0.0921 | 29.03 | 3600 | 1.7559 | 1.0232 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hashu/my-pet-cat-xyz | hashu | 2023-08-10T07:12:37Z | 0 | 0 | null | [
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-10T07:09:43Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat-xyz Dreambooth model trained by hashu following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: VJCET527
Sample pictures of this concept:
.jpg)
|
yyyy1992/my_awesome_wnut_model | yyyy1992 | 2023-08-10T06:58:22Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"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"
]
| token-classification | 2023-08-10T06:51:33Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5096660808435852
- name: Recall
type: recall
value: 0.26876737720111216
- name: F1
type: f1
value: 0.35194174757281554
- name: Accuracy
type: accuracy
value: 0.9392501389423282
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0772
- Precision: 0.5097
- Recall: 0.2688
- F1: 0.3519
- Accuracy: 0.9393
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.0816 | 0.4192 | 0.1779 | 0.2498 | 0.9351 |
| No log | 2.0 | 426 | 0.0772 | 0.5097 | 0.2688 | 0.3519 | 0.9393 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.11.0
- Tokenizers 0.13.3
|
weiren119/traditional_chinese_qlora_llama2_13b_adapter | weiren119 | 2023-08-10T06:57:43Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T06:56:58Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
reinhardfr/bloomz-560m_PROMPT_TUNING_CAUSAL_LM | reinhardfr | 2023-08-10T06:53:25Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T05:52:22Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
leahsuperb/Reinforce-CartPole-v1 | leahsuperb | 2023-08-10T06:53:10Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T06:53:01Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
cuixing/textual_inversion_object_style_vangogh08101212-newstyle | cuixing | 2023-08-10T06:51:27Z | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-08-10T04:12:51Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - cuixing/textual_inversion_object_style_vangogh08101212-newstyle
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
bjfxs/llama2-7b-200steps-finetunined-sxl | bjfxs | 2023-08-10T06:49:06Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T06:49:02Z | ---
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
|
tanviraumi/q-FrozenLake-v1-4x4-noSlippery | tanviraumi | 2023-08-10T06:40:11Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T06:40:08Z | ---
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="tanviraumi/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"])
```
|
jakezou/a2c-PandaReachDense-v3 | jakezou | 2023-08-10T06:38:05Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T06:31:17Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.23 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
kasperchen/q-Taxi-v3 | kasperchen | 2023-08-10T06:36:12Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T06:36:10Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.79
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kasperchen/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
kasperchen/q-texi-v3 | kasperchen | 2023-08-10T06:35:39Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T06:31:45Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Texi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.79
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kasperchen/q-texi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
nullday/immersiveL-exp | nullday | 2023-08-10T06:21:53Z | 64 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"translation",
"gpt-style",
"chinese",
"english",
"zh",
"en",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| translation | 2023-08-07T07:30:25Z | ---
language:
- zh
- en
tags:
- translation
- gpt-style
- chinese
- english
license: "bigscience-bloom-rail-1.0"
---
## English:
### ImmersiveL Model on Hugging Face
This model, available on Hugging Face under `funstoryai/immersiveL-exp`, is a GPT-like model designed specifically for English-Chinese and Chinese-English translations.
**Recommended Prompts:**
For English to Chinese:
```
下面是一段英文文本，请将它翻译成中文。
{terms}
#英文文本:
{input}
#中文翻译:
```
For Chinese to English:
```
下面是一段中文文本，请将它翻译成英文。
{terms}
#中文文本:
{input}
#英文翻译:
```
For the corresponding GitHub project, please visit: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL).
<https://github.com/immersive-translate/ImmersiveL>
---
## 中文:
### Hugging Face 上的 ImmersiveL 模型
此模型在 Hugging Face 的 `funstoryai/immersiveL-exp` 下可用，是专为英汉和汉英翻译设计的类GPT模型。
**推荐提示词:**
英译中:
```
下面是一段英文文本，请将它翻译成中文。
{terms}
#英文文本:
{input}
#中文翻译:
```
中译英:
```
下面是一段中文文本，请将它翻译成英文。
{terms}
#中文文本:
{input}
#英文翻译:
```
对应的 GitHub 项目地址为: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL).
<https://github.com/immersive-translate/ImmersiveL>
|
TheTravellingEngineer/llama2-7b-chat-hf-v3 | TheTravellingEngineer | 2023-08-10T06:21:28Z | 1,536 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T06:03:25Z | The base model is meta's Llama-2-7b-chat-hf. It was finetuned using SFT and the Anthropic/hh-rlhf dataset and the model prompt is similar to the original Guanaco model.
This repo contains the merged fp16 model.
**Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.**
---
- license:
- llama2 <br>
- datasets:
- Anthropic/hh-rlhf <br>
- language:
- en <br>
- reference: https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da
--- |
HG7/ReQLoRA_QKVO8 | HG7 | 2023-08-10T06:01:24Z | 2 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T06:01:20Z | ---
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
|
Bastian1111/dqn-SpaceInvadersNoFrameskip-v4 | Bastian1111 | 2023-08-10T05:52:53Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-06T04:19:13Z | ---
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: 762.50 +/- 300.08
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 Bastian1111 -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 Bastian1111 -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 Bastian1111
```
## 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', 10000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
DAMO-NLP-MT/polylm-13b | DAMO-NLP-MT | 2023-08-10T05:50:39Z | 1,615 | 53 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"custom_code",
"zh",
"en",
"es",
"fr",
"pt",
"ru",
"de",
"it",
"ar",
"ja",
"ko",
"th",
"vi",
"id",
"nl",
"pl",
"tr",
"he",
"arxiv:2307.06018",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-13T13:48:44Z | ---
language:
- zh
- en
- es
- fr
- pt
- ru
- de
- it
- ar
- ja
- ko
- th
- vi
- id
- nl
- pl
- tr
- he
tags:
- text-generation
license: apache-2.0
---
# Model Card for PolyLM (a polyglot large language model)
## Table of Contents
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Next Steps](#next-steps)
6. [Citation](#citation)
# Model Details
## Abstract
> Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model's performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English.
## Model Description
- **Model type:** Decoder-only Language model
- **Language(s) (NLP):** Chinese, English, Spanish, German, French, Portuguese, Russian, Italian, Arabic, Japanese, Korean, Thai, Vietnamese, Indonesian, Polish, Turkish, Dutch, Hebrew
- **License:** Apache 2.0
- **Original Checkpoints:** [Modelscope DAMO PolyLM-13B](https://www.modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary)
- **Link to paper:** [here](https://arxiv.org/pdf/2307.06018.pdf)
- **Number fotmat:** bf16
- **Total seen tokens:** 640 billion tokens
- **Version:** Version 1.0 / 12 July 2023
# Usage
Find below some example scripts on how to use the model in `transformers`:
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-13b", legacy=False, use_fast=False)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-13b", device_map="auto", trust_remote_code=True)
model.eval()
input_doc = f"Beijing is the capital of China.\nTranslate this sentence from English to Chinese."
inputs = tokenizer(input_doc, return_tensors="pt")
generate_ids = model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
do_sample=False,
num_beams=4,
max_length=128,
early_stopping=True
)
decoded = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(f">>> {decoded}")
### results
### Beijing is the capital of China.\nTranslate this sentence from English to Chinese.\\n北京是中华人民共和国的首都。\n ...
```
</details>
# Uses
## Direct Use and Downstream Use
> The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models
See the [research paper](https://arxiv.org/pdf/2307.06018.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf):
> Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
# Next Steps
We are continuously enhancing the capabilities of PolyLM by focusing on the following aspects:
1. Replacement of absolute position embeddings with RoPE, as outlined in the research paper [here](https://arxiv.org/abs/2104.09864).
2. Expansion of window size to more than 10,000.
3. Verification of lightweight techniques to quickly enhance multilingual quality, especially for low-resource languages.
# Citation
**BibTeX:**
```bibtex
@misc{wei2023polylm,
title={PolyLM: An Open Source Polyglot Large Language Model},
author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie},
year={2023},
eprint={2307.06018},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
JacobAshwin/donut-base-slips | JacobAshwin | 2023-08-10T05:26:01Z | 49 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-07-25T22:15:27Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-slips
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. -->
# donut-base-slips
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nanirudh/qa_model_v3 | nanirudh | 2023-08-10T05:23:57Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T05:23:48Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
kasperchen/q-FrozenLake-v1-4x4-noSlippery | kasperchen | 2023-08-10T05:00:06Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T04:11:41Z | ---
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="kasperchen/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"])
```
|
bookbot/byt5-small-wikipron-eng-latn-us-broad-p2g | bookbot | 2023-08-10T04:54:35Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-04-19T04:07:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: byt5-small-wikipron-eng-latn-us-broad-p2g
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. -->
# byt5-small-wikipron-eng-latn-us-broad-p2g
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2595
- Per: 0.4628
- Gen Len: 8.4996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.4797 | 1.0 | 382 | 0.4371 | 0.6951 | 8.4302 |
| 0.4823 | 2.0 | 764 | 0.3543 | 0.5974 | 8.4338 |
| 0.3878 | 3.0 | 1146 | 0.3081 | 0.545 | 8.4394 |
| 0.3378 | 4.0 | 1528 | 0.2904 | 0.518 | 8.449 |
| 0.3061 | 5.0 | 1910 | 0.2736 | 0.5004 | 8.4612 |
| 0.2823 | 6.0 | 2292 | 0.2664 | 0.4893 | 8.4734 |
| 0.265 | 7.0 | 2674 | 0.2626 | 0.4747 | 8.4721 |
| 0.2502 | 8.0 | 3056 | 0.2612 | 0.4697 | 8.4945 |
| 0.2388 | 9.0 | 3438 | 0.2592 | 0.4633 | 8.489 |
| 0.231 | 10.0 | 3820 | 0.2595 | 0.4628 | 8.4996 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.2
|
nicbull/DialoGPT-medium-leric | nicbull | 2023-08-10T04:37:18Z | 150 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"chat",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-10T04:25:26Z | ---
language:
- en
pipeline_tag: conversational
tags:
- chat
--- |
KallistiTMR/llama-2-7b-chat-wiz-k16-8 | KallistiTMR | 2023-08-10T04:04:30Z | 12 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-02T02:24:03Z | ---
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
|
chunwoolee0/keti-air-ke-t5-base-en-to-ko | chunwoolee0 | 2023-08-10T04:00:42Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:KETI-AIR/ke-t5-base",
"base_model:finetune:KETI-AIR/ke-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| translation | 2023-08-10T03:27:30Z | ---
license: apache-2.0
base_model: KETI-AIR/ke-t5-base
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: keti-air-ke-t5-base-en-to-ko
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. -->
# keti-air-ke-t5-base-en-to-ko
This model is a fine-tuned version of [KETI-AIR/ke-t5-base](https://huggingface.co/KETI-AIR/ke-t5-base) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nomad-ai/speecht5_finetuned_voxpopuli_nl | nomad-ai | 2023-08-10T03:58:14Z | 82 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-speech | 2023-08-10T03:06:59Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4919
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7222 | 1.62 | 100 | 0.6410 |
| 0.6791 | 3.25 | 200 | 0.6024 |
| 0.6059 | 4.87 | 300 | 0.5363 |
| 0.564 | 6.49 | 400 | 0.5185 |
| 0.5481 | 8.11 | 500 | 0.5092 |
| 0.5463 | 9.74 | 600 | 0.4998 |
| 0.537 | 11.36 | 700 | 0.4968 |
| 0.5312 | 12.98 | 800 | 0.4913 |
| 0.5275 | 14.6 | 900 | 0.4917 |
| 0.5202 | 16.23 | 1000 | 0.4919 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
debjxt/tlx-bzx-btz | debjxt | 2023-08-10T03:45:14Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-08-10T03:32:22Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### tlx_bzx_btz Dreambooth model trained by debjxt 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:
|
TheTravellingEngineer/llama2-7b-chat-hf-v2 | TheTravellingEngineer | 2023-08-10T03:36:24Z | 1,541 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-08T06:27:25Z | The base model is meta's Llama-2-7b-chat-hf. It was finetuned using SFT and the alpaca dataset and the model prompt is similar to the original Guanaco model.
This repo contains the merged fp16 model.
**Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.**
---
- license:
- llama2 <br>
- datasets:
- tatsu-lab/alpaca <br>
- language:
- en <br>
- reference: https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da
---
|
Lsnt/test_03 | Lsnt | 2023-08-10T03:31:02Z | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-10T03:09:48Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Lsnt/test_03
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
dangkhoadl/AudioResNet | dangkhoadl | 2023-08-10T03:21:17Z | 38 | 0 | transformers | [
"transformers",
"pytorch",
"resnet",
"endpoints_compatible",
"region:us"
]
| null | 2023-08-08T01:50:29Z | # Input tensor shape
[batch_size, Cin, num_feats, num_frames] |
EkoMickA/distilroberta-base-finetuned-wikitext2 | EkoMickA | 2023-08-10T03:14:19Z | 162 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-08-10T03:03:02Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8251
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0909 | 1.0 | 2406 | 1.9271 |
| 1.9984 | 2.0 | 4812 | 1.8671 |
| 1.941 | 3.0 | 7218 | 1.8546 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jordyvl/dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_kd | jordyvl | 2023-08-10T03:06:31Z | 164 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-07-27T12:16:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_kd
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. -->
# dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_kd
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5815
- Accuracy: 0.8055
- Brier Loss: 0.2836
- Nll: 1.6135
- F1 Micro: 0.8055
- F1 Macro: 0.8061
- Ece: 0.0597
- Aurc: 0.0526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 125 | 1.2844 | 0.5403 | 0.5889 | 3.0582 | 0.5403 | 0.5275 | 0.0742 | 0.2209 |
| No log | 2.0 | 250 | 0.9687 | 0.655 | 0.4587 | 2.4358 | 0.655 | 0.6414 | 0.0559 | 0.1296 |
| No log | 3.0 | 375 | 0.8401 | 0.7063 | 0.4019 | 2.2308 | 0.7063 | 0.7008 | 0.0588 | 0.0990 |
| 1.234 | 4.0 | 500 | 0.8080 | 0.7145 | 0.3874 | 2.1628 | 0.7145 | 0.7163 | 0.0487 | 0.0951 |
| 1.234 | 5.0 | 625 | 0.7772 | 0.7238 | 0.3755 | 2.0380 | 0.7237 | 0.7167 | 0.0421 | 0.0914 |
| 1.234 | 6.0 | 750 | 0.7530 | 0.7498 | 0.3484 | 2.1346 | 0.7498 | 0.7464 | 0.0477 | 0.0774 |
| 1.234 | 7.0 | 875 | 0.7034 | 0.7652 | 0.3267 | 2.0596 | 0.7652 | 0.7664 | 0.0467 | 0.0678 |
| 0.3976 | 8.0 | 1000 | 0.7390 | 0.7715 | 0.3350 | 2.0568 | 0.7715 | 0.7704 | 0.0448 | 0.0763 |
| 0.3976 | 9.0 | 1125 | 0.7019 | 0.7762 | 0.3209 | 2.0168 | 0.7762 | 0.7768 | 0.0556 | 0.0769 |
| 0.3976 | 10.0 | 1250 | 0.7318 | 0.7668 | 0.3346 | 2.1148 | 0.7668 | 0.7699 | 0.0529 | 0.0792 |
| 0.3976 | 11.0 | 1375 | 0.7083 | 0.7782 | 0.3213 | 2.0671 | 0.7782 | 0.7775 | 0.0452 | 0.0756 |
| 0.1591 | 12.0 | 1500 | 0.7535 | 0.7668 | 0.3424 | 2.1407 | 0.7668 | 0.7636 | 0.0564 | 0.0845 |
| 0.1591 | 13.0 | 1625 | 0.7117 | 0.775 | 0.3288 | 2.0935 | 0.775 | 0.7766 | 0.0525 | 0.0785 |
| 0.1591 | 14.0 | 1750 | 0.6421 | 0.785 | 0.3039 | 1.9939 | 0.785 | 0.7860 | 0.0512 | 0.0643 |
| 0.1591 | 15.0 | 1875 | 0.6475 | 0.7865 | 0.3050 | 1.9301 | 0.7865 | 0.7867 | 0.0552 | 0.0636 |
| 0.1125 | 16.0 | 2000 | 0.6477 | 0.7893 | 0.3064 | 1.9442 | 0.7893 | 0.7920 | 0.0556 | 0.0684 |
| 0.1125 | 17.0 | 2125 | 0.6509 | 0.7883 | 0.3113 | 1.8957 | 0.7883 | 0.7907 | 0.0498 | 0.0710 |
| 0.1125 | 18.0 | 2250 | 0.6291 | 0.7925 | 0.3038 | 1.8697 | 0.7925 | 0.7963 | 0.0512 | 0.0677 |
| 0.1125 | 19.0 | 2375 | 0.6279 | 0.7963 | 0.2992 | 1.8155 | 0.7963 | 0.7950 | 0.0478 | 0.0647 |
| 0.095 | 20.0 | 2500 | 0.6246 | 0.7937 | 0.3008 | 1.7925 | 0.7937 | 0.7946 | 0.0595 | 0.0659 |
| 0.095 | 21.0 | 2625 | 0.6149 | 0.7953 | 0.2962 | 1.8237 | 0.7953 | 0.7951 | 0.0547 | 0.0590 |
| 0.095 | 22.0 | 2750 | 0.6196 | 0.7953 | 0.3000 | 1.8031 | 0.7953 | 0.7969 | 0.0567 | 0.0643 |
| 0.095 | 23.0 | 2875 | 0.6023 | 0.798 | 0.2932 | 1.7663 | 0.798 | 0.7983 | 0.0497 | 0.0616 |
| 0.0829 | 24.0 | 3000 | 0.6107 | 0.7943 | 0.2951 | 1.7755 | 0.7943 | 0.7958 | 0.0564 | 0.0581 |
| 0.0829 | 25.0 | 3125 | 0.5986 | 0.8015 | 0.2930 | 1.7243 | 0.8015 | 0.8027 | 0.0565 | 0.0574 |
| 0.0829 | 26.0 | 3250 | 0.5899 | 0.8005 | 0.2886 | 1.7304 | 0.8005 | 0.8021 | 0.0546 | 0.0560 |
| 0.0829 | 27.0 | 3375 | 0.5836 | 0.8023 | 0.2846 | 1.6865 | 0.8023 | 0.8024 | 0.0479 | 0.0561 |
| 0.074 | 28.0 | 3500 | 0.5824 | 0.8047 | 0.2850 | 1.6817 | 0.8047 | 0.8060 | 0.0524 | 0.0559 |
| 0.074 | 29.0 | 3625 | 0.5760 | 0.8063 | 0.2822 | 1.6505 | 0.8062 | 0.8065 | 0.0500 | 0.0546 |
| 0.074 | 30.0 | 3750 | 0.5819 | 0.8065 | 0.2843 | 1.6667 | 0.8065 | 0.8079 | 0.0563 | 0.0544 |
| 0.074 | 31.0 | 3875 | 0.5800 | 0.8045 | 0.2841 | 1.6658 | 0.8045 | 0.8059 | 0.0511 | 0.0548 |
| 0.0668 | 32.0 | 4000 | 0.5828 | 0.8053 | 0.2841 | 1.6883 | 0.8053 | 0.8054 | 0.0559 | 0.0547 |
| 0.0668 | 33.0 | 4125 | 0.5802 | 0.8037 | 0.2838 | 1.6669 | 0.8037 | 0.8038 | 0.0572 | 0.0545 |
| 0.0668 | 34.0 | 4250 | 0.5772 | 0.8067 | 0.2821 | 1.6588 | 0.8067 | 0.8083 | 0.0520 | 0.0525 |
| 0.0668 | 35.0 | 4375 | 0.5745 | 0.807 | 0.2812 | 1.6524 | 0.807 | 0.8072 | 0.0528 | 0.0528 |
| 0.0631 | 36.0 | 4500 | 0.5770 | 0.8063 | 0.2826 | 1.6433 | 0.8062 | 0.8071 | 0.0559 | 0.0528 |
| 0.0631 | 37.0 | 4625 | 0.5782 | 0.8007 | 0.2837 | 1.5953 | 0.8007 | 0.8021 | 0.0581 | 0.0541 |
| 0.0631 | 38.0 | 4750 | 0.5780 | 0.8047 | 0.2829 | 1.6275 | 0.8047 | 0.8052 | 0.0540 | 0.0521 |
| 0.0631 | 39.0 | 4875 | 0.5759 | 0.8055 | 0.2817 | 1.6162 | 0.8055 | 0.8065 | 0.0528 | 0.0529 |
| 0.0612 | 40.0 | 5000 | 0.5770 | 0.8047 | 0.2825 | 1.6131 | 0.8047 | 0.8051 | 0.0575 | 0.0524 |
| 0.0612 | 41.0 | 5125 | 0.5771 | 0.8043 | 0.2819 | 1.6015 | 0.8043 | 0.8048 | 0.0562 | 0.0519 |
| 0.0612 | 42.0 | 5250 | 0.5776 | 0.8043 | 0.2825 | 1.6152 | 0.8043 | 0.8047 | 0.0566 | 0.0527 |
| 0.0612 | 43.0 | 5375 | 0.5793 | 0.8057 | 0.2830 | 1.6196 | 0.8057 | 0.8065 | 0.0538 | 0.0527 |
| 0.06 | 44.0 | 5500 | 0.5801 | 0.8053 | 0.2835 | 1.6183 | 0.8053 | 0.8060 | 0.0618 | 0.0527 |
| 0.06 | 45.0 | 5625 | 0.5800 | 0.805 | 0.2831 | 1.6057 | 0.805 | 0.8055 | 0.0568 | 0.0530 |
| 0.06 | 46.0 | 5750 | 0.5812 | 0.805 | 0.2836 | 1.6034 | 0.805 | 0.8056 | 0.0577 | 0.0529 |
| 0.06 | 47.0 | 5875 | 0.5809 | 0.805 | 0.2834 | 1.6164 | 0.805 | 0.8056 | 0.0580 | 0.0526 |
| 0.0593 | 48.0 | 6000 | 0.5810 | 0.8057 | 0.2834 | 1.6108 | 0.8057 | 0.8064 | 0.0617 | 0.0525 |
| 0.0593 | 49.0 | 6125 | 0.5812 | 0.8053 | 0.2836 | 1.6140 | 0.8053 | 0.8058 | 0.0570 | 0.0527 |
| 0.0593 | 50.0 | 6250 | 0.5815 | 0.8055 | 0.2836 | 1.6135 | 0.8055 | 0.8061 | 0.0597 | 0.0526 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
sshh12/sdxl-lora-pokemon | sshh12 | 2023-08-10T03:05:05Z | 2 | 0 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-07T02:37:13Z | ---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: lambdalabs/pokemon-blip-captions
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: false
---
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| | | | |
| ------------------------------------------------------- | ---------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------- |
|  |  |  |  |
## 🧨 Diffusers Usage
```py
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.load_lora_weights("sshh12/sdxl-lora-pokemon")
pipe.to("cuda")
prompt = "..."
image = pipe(prompt=prompt).images[0]
image
```
## Training
```py
MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
DATASET_NAME="lambdalabs/pokemon-blip-captions"
!accelerate launch train_text_to_image_lora_sdxl.py \
--pretrained_model_name_or_path="$MODEL_NAME" \
--pretrained_vae_model_name_or_path="madebyollin/sdxl-vae-fp16-fix" \
--dataset_name="$DATASET_NAME" \
--caption_column="text" \
--resolution=1024 \
--random_flip \
--mixed_precision="fp16" \
--use_8bit_adam \
--train_batch_size=1 \
--gradient_accumulation_steps=8 \
--num_train_epochs=200 \
--checkpointing_steps=500 \
--learning_rate=1e-04 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--seed=0 \
--validation_prompt="cute dragon creature" \
--enable_xformers_memory_efficient_attention \
--report_to="wandb"
```
|
wangxso/q-taxi-v3 | wangxso | 2023-08-10T02:28:47Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T02:28:44Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.70
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="wangxso/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
nomad-ai/whisper-tiny | nomad-ai | 2023-08-10T02:03:05Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"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 | 2023-08-10T02:02:50Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.22434915773353753
---
<!-- 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
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: 0.5913
- Wer Ortho: 0.2340
- Wer: 0.2243
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.7357 | 2.0 | 50 | 0.7179 | 0.2947 | 0.2412 |
| 0.2772 | 4.0 | 100 | 0.4758 | 0.2404 | 0.2113 |
| 0.081 | 6.0 | 150 | 0.5069 | 0.2628 | 0.2282 |
| 0.02 | 8.0 | 200 | 0.5289 | 0.2564 | 0.2297 |
| 0.0044 | 10.0 | 250 | 0.5366 | 0.2452 | 0.2251 |
| 0.0018 | 12.0 | 300 | 0.5565 | 0.2404 | 0.2251 |
| 0.0011 | 14.0 | 350 | 0.5668 | 0.2388 | 0.2259 |
| 0.0009 | 16.0 | 400 | 0.5762 | 0.2364 | 0.2251 |
| 0.0007 | 18.0 | 450 | 0.5847 | 0.2348 | 0.2243 |
| 0.0006 | 20.0 | 500 | 0.5913 | 0.2340 | 0.2243 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
EricLeonhard11/wav2vec2-large-xls-r-300m-turkish-colab | EricLeonhard11 | 2023-08-10T01:42:00Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-08-09T05:18:33Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
tianpf/llama2-qlora-finetunined-law | tianpf | 2023-08-10T01:38:54Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T01:38:51Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
Kappa7077/clip-finetuned-lora-organ | Kappa7077 | 2023-08-10T01:21:03Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-10T01:17:51Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
jaykei/Zuko | jaykei | 2023-08-10T01:17:21Z | 0 | 1 | null | [
"en",
"license:openrail",
"region:us"
]
| null | 2023-07-05T05:16:36Z | ---
license: openrail
language:
- en
--- |
dana11235/ppo-Huggy | dana11235 | 2023-08-10T01:16:01Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-08-10T01:15:51Z | ---
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: dana11235/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Pixel390/BOYKILLUAV2 | Pixel390 | 2023-08-10T01:09:59Z | 2 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Meina/MeinaMix_V10",
"base_model:adapter:Meina/MeinaMix_V10",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-10T00:28:55Z |
---
license: creativeml-openrail-m
base_model: Meina/MeinaMix_V10
instance_prompt: a uxz boy
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Pixel390/BOYKILLUAV2
These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz boy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
mani05/q-FrozenLake-v1-4x4-noSlippery | mani05 | 2023-08-10T01:06:06Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T01:06:02Z | ---
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="mani05/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"])
```
|
rriverar75/bert-base-multilingual-cased-mrpc-glue | rriverar75 | 2023-08-10T00:50:24Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-10T00:39:59Z | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text:
- >-
Yucaipa owned Dominick 's before selling the chain to Safeway in 1998
for $ 2.5 billion.
- >-
Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to
Safeway for $ 1.8 billion in 1998.
example_title: Not Equivalent
- text:
- >-
Revenue in the first quarter of the year dropped 15 percent from the
same period a year earlier.
- >-
With the scandal hanging over Stewart's company revenue the first
quarter of the year dropped 15 percent from the same period a year
earlier.
example_title: Equivalent
model-index:
- name: bert-base-multilingual-cased-mrpc-glue
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7426470588235294
- name: F1
type: f1
value: 0.8059149722735676
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-mrpc-glue
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5185
- Accuracy: 0.7426
- F1: 0.8059
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.604 | 1.09 | 500 | 0.5185 | 0.7426 | 0.8059 |
| 0.4834 | 2.18 | 1000 | 0.5550 | 0.7770 | 0.8544 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Notespeak/ariadnetestn | Notespeak | 2023-08-10T00:35:42Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-08-10T00:28:25Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [ai-forever/ruGPT-3.5-13B](https://huggingface.co/ai-forever/ruGPT-3.5-13B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.31.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="Notespeak/ariadnetestn",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Notespeak/ariadnetestn",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Notespeak/ariadnetestn",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Notespeak/ariadnetestn" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPT2LMHeadModel(
(transformer): GPT2Model(
(wte): Embedding(50272, 5120)
(wpe): Embedding(2048, 5120)
(drop): Dropout(p=0.1, inplace=False)
(h): ModuleList(
(0-39): 40 x GPT2Block(
(ln_1): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)
(attn): GPT2Attention(
(c_attn): Conv1D()
(c_proj): Conv1D()
(attn_dropout): Dropout(p=0.1, inplace=False)
(resid_dropout): Dropout(p=0.1, inplace=False)
)
(ln_2): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)
(mlp): GPT2MLP(
(c_fc): Conv1D()
(c_proj): Conv1D()
(act): NewGELUActivation()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(ln_f): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=5120, out_features=50272, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
Precious1/Clinical-Biomedical-Named-Entity-Recognition-Using-Scispacy | Precious1 | 2023-08-10T00:30:41Z | 0 | 1 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2023-08-10T00:25:26Z | ---
license: bigscience-openrail-m
---
|
allenbc/q-FrozenLake-v1-4x4-noSlippery | allenbc | 2023-08-10T00:26:14Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-10T00:26:09Z | ---
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="allenbc/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"])
```
|
off-topic-team/llama-2-13b-off-topic | off-topic-team | 2023-08-10T00:17:48Z | 0 | 1 | null | [
"llama",
"eleutherai",
"llama-2",
"region:us"
]
| null | 2023-08-09T23:44:20Z | ---
tags:
- llama
- eleutherai
- llama-2
---
It had to be done.
Sample of data:
```
Hyperion: when it's dysfunctional you just yolo deploy into prod
Fleetwood: remove the middle clause - fuck loyalty to ~~ companies~~ most companies
Zippy: nono- like- it's more that- it works, but- there needs to be a dude who understands it and can move it to new servers, or make small patches for new functionality, etc-...
⭐ 1 <:sus:869276863414014052> 1 <:guilty:745897670534627369> 2 AI_WAIFU: oh and if you fuck up the wrong system you may get dragged to washington DC to explain what happened
```
also sorry about the reaction formatting having the IDs in it I didn't realize how annoying that would be with Llama's tokenizer :c |
rriverar75/distilroberta-base-mrpc-glue | rriverar75 | 2023-08-10T00:13:17Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-10T00:06:32Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text:
- >-
Yucaipa owned Dominick 's before selling the chain to Safeway in 1998
for $ 2.5 billion.
- >-
Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to
Safeway for $ 1.8 billion in 1998.
example_title: Not Equivalent
- text:
- >-
Revenue in the first quarter of the year dropped 15 percent from the
same period a year earlier.
- >-
With the scandal hanging over Stewart's company revenue the first
quarter of the year dropped 15 percent from the same period a year
earlier.
example_title: Equivalent
model-index:
- name: distilroberta-base-mrpc-glue
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8333333333333334
- name: F1
type: f1
value: 0.8794326241134752
---
<!-- 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. -->
# distilroberta-base-mrpc-glue
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
- Accuracy: 0.8333
- F1: 0.8794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5523 | 1.09 | 500 | 0.3874 | 0.8333 | 0.8794 |
| 0.3421 | 2.18 | 1000 | 0.5895 | 0.8529 | 0.8969 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
vluz/toxmodel20 | vluz | 2023-08-10T00:06:47Z | 4 | 0 | keras | [
"keras",
"license:cc0-1.0",
"region:us"
]
| null | 2023-08-07T11:52:01Z | ---
license: cc0-1.0
---
**Note:** Due to nature of toxic comments data and code contain explicit language.
Data is from kaggle, the *Toxic Comment Classification Challenge*
<br>
https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data?select=train.csv.zip
A copy of the data exists on the `data` directory.
Trained over 20 epoch in a runpod
### 🤗 Running demo here:
https://huggingface.co/spaces/vluz/Tox
<hr>
Code requires pandas, tensorflow, and streamlit. All can be installed via `pip`.
```python
import os
import pickle
import streamlit as st
import tensorflow as tf
from tensorflow.keras.layers import TextVectorization
@st.cache_resource
def load_model():
model = tf.keras.models.load_model(os.path.join("model", "toxmodel.keras"))
return model
@st.cache_resource
def load_vectorizer():
from_disk = pickle.load(open(os.path.join("model", "vectorizer.pkl"), "rb"))
new_v = TextVectorization.from_config(from_disk['config'])
new_v.adapt(tf.data.Dataset.from_tensor_slices(["xyz"])) # fix for Keras bug
new_v.set_weights(from_disk['weights'])
return new_v
st.title("Toxic Comment Test")
st.divider()
model = load_model()
vectorizer = load_vectorizer()
default_prompt = "i love you man, but fuck you!"
input_text = st.text_area("Comment:", default_prompt, height=150).lower()
if st.button("Test"):
if not input_text:
st.write("⚠ Warning: Empty prompt.")
elif len(input_text) < 15:
st.write("⚠ Warning: Model is far less accurate with a small prompt.")
if input_text == default_prompt:
st.write("Expected results from default prompt are positive for 0 and 2")
with st.spinner("Testing..."):
inputv = vectorizer([input_text])
output = model.predict(inputv)
res = (output > 0.5)
st.write(["toxic","severe toxic","obscene","threat","insult","identity hate"], res)
st.write(output)
```
Put `toxmodel.keras` and `vectorizer.pkl` into the `model` dir.
Then do:
```
stramlit run toxtest.py
```
Expected result from default prompt is 0 and 2
<hr>
Full code can be found here:
<br>
https://github.com/vluz/ToxTest/
<br> |
thi-doan/xlm-roberta-base-finetuned-panx-de | thi-doan | 2023-08-09T23:57:40Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-09T22:55:36Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8616659101225601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1329
- F1: 0.8617
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2568 | 1.0 | 525 | 0.1583 | 0.8125 |
| 0.1261 | 2.0 | 1050 | 0.1458 | 0.8473 |
| 0.0823 | 3.0 | 1575 | 0.1329 | 0.8617 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Pixel390/GIRLKAY | Pixel390 | 2023-08-09T23:53:42Z | 1 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:Meina/MeinaMix_V10",
"base_model:adapter:Meina/MeinaMix_V10",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-08-09T23:09:34Z |
---
license: creativeml-openrail-m
base_model: Meina/MeinaMix_V10
instance_prompt: a uxz girl
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Pixel390/GIRLKAY
These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
tingchih/pretrain_doc_concat | tingchih | 2023-08-09T23:38:40Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-07-31T05:04:43Z | This is a pre-train baseline model for summarization. Input is to concatenate all articles in one cluster.
the example.json is the example result.
pipeline:
input -> sum tokenizer -> perceiver -> sum model -> summary |
cjohlmacher/ppo-Pyramids | cjohlmacher | 2023-08-09T23:30:56Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-08-09T21:01:25Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: cjohlmacher/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
good-gaming/distilbert-base-uncased-finetuned-emotion | good-gaming | 2023-08-09T23:21:58Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"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 | 2023-08-09T22:48:26Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9272353554627635
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2133
- Accuracy: 0.927
- F1: 0.9272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8118 | 1.0 | 250 | 0.3108 | 0.905 | 0.9056 |
| 0.2485 | 2.0 | 500 | 0.2133 | 0.927 | 0.9272 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.13.3
|
bilbo991/clip-homer-2m | bilbo991 | 2023-08-09T23:14:42Z | 91 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2023-08-08T16:11:20Z | ---
base_model: clip-homer-2m
tags:
- generated_from_trainer
model-index:
- name: clip-homer-2m
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. -->
# clip-homer-2m
This model is a fine-tuned version of [clip-homer-2m](https://huggingface.co/clip-homer-2m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.4564 | 1.0 | 62500 | 1.1970 |
| 1.0737 | 2.0 | 125000 | 0.8098 |
| 0.6867 | 3.0 | 187500 | 0.5687 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3
|
sachin2000keshav/bert-finetuned-ner | sachin2000keshav | 2023-08-09T22:46:27Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-08-09T21:35:25Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9348437241607409
- name: Recall
type: recall
value: 0.9513631773813531
- name: F1
type: f1
value: 0.9430311118525316
- name: Accuracy
type: accuracy
value: 0.9869753340790016
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0555
- Precision: 0.9348
- Recall: 0.9514
- F1: 0.9430
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.078 | 1.0 | 1756 | 0.0794 | 0.9091 | 0.9337 | 0.9212 | 0.9798 |
| 0.04 | 2.0 | 3512 | 0.0562 | 0.9275 | 0.9468 | 0.9370 | 0.9858 |
| 0.026 | 3.0 | 5268 | 0.0555 | 0.9348 | 0.9514 | 0.9430 | 0.9870 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
theojolliffe/flan-recipes | theojolliffe | 2023-08-09T22:39:32Z | 104 | 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 | 2023-08-09T22:03:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-recipes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-recipes
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 71.0741
- Rouge2: 34.937
- Rougel: 71.129
- Rougelsum: 71.0758
- Gen Len: 4.0103
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 873 | nan | 71.0741 | 34.937 | 71.129 | 71.0758 | 4.0103 |
| 0.0 | 2.0 | 1746 | nan | 71.0741 | 34.937 | 71.129 | 71.0758 | 4.0103 |
| 0.0 | 3.0 | 2619 | nan | 71.0741 | 34.937 | 71.129 | 71.0758 | 4.0103 |
| 0.0 | 4.0 | 3492 | nan | 71.0741 | 34.937 | 71.129 | 71.0758 | 4.0103 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gang21/llama2-icd10-peft | gang21 | 2023-08-09T22:33:20Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-09T22:05:35Z | ---
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
|
jucaro/donut-base-sroie | jucaro | 2023-08-09T22:19:48Z | 46 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-08-09T19:07:50Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
agustinl/ppo-SnowballTarget | agustinl | 2023-08-09T22:18:40Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-08-09T22:18:36Z | ---
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: agustinl/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sergeindamix/anciano_pendejo | sergeindamix | 2023-08-09T22:11:22Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-08-09T22:11:17Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a sks person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Test enoder was not trained.
|
rizquuula/RoBERTa-IndoSQuADv2_1691592486-16-2e-05-0.01-5 | rizquuula | 2023-08-09T22:04:20Z | 101 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-08-09T14:51:09Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-IndoSQuADv2_1691592486-16-2e-05-0.01-5
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. -->
# RoBERTa-IndoSQuADv2_1691592486-16-2e-05-0.01-5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2457 | 1.0 | 8145 | 2.1159 |
| 1.7442 | 2.0 | 16290 | 2.0275 |
| 1.4963 | 3.0 | 24435 | 2.0147 |
| 1.301 | 4.0 | 32580 | 2.0607 |
| 1.1569 | 5.0 | 40725 | 2.1516 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
totally-not-an-llm/AlpacaCielo2-7b-8k | totally-not-an-llm | 2023-08-09T21:59:36Z | 9 | 10 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-03T15:17:18Z | ---
license: llama2
---
# AlpacaCielo2-7b-8k
<figure>
<img src="https://huggingface.co/totally-not-an-llm/AlpacaCielo-13b/resolve/main/alpaca.png" alt="cute cloud alpaca">
<figcaption style="font-size: 1em;"><i>"super cute baby alpaca laying on a cloud", Model: epicrealism_pureEvolutionV3</i></figcaption>
</figure>
AlpacaCielo2-7b-8k is the second version of the AlpacaCielo series. It is a llama-2 based model designed for creative tasks, such as storytelling and roleplay, while still doing well with other chatbot purposes. It is a triple model merge of Nous-Hermes + Guanaco + LimaRP. While it is mostly *"uncensored"*, it still inherits some alignment from Guanaco.
[GPTQ quants](https://huggingface.co/TheBloke/AlpacaCielo2-7B-8K-GPTQ)<br>
[GGML quants](https://huggingface.co/TheBloke/AlpacaCielo2-7B-8K-GGML)<br>
(Courtesy of TheBloke)
### Differences from V1:
- Double context (4k->8k)
- Better roleplaying abilities
**Performs well with custom prompt format:**
```
### System: {system prompt}
### Human: {prompt}
### Assistant:
```
### Note for system prompt:
The model understands it well and it works great if you want roleplay, but it still likes to be an assistant, so you should nudge it in the right direction. For example:
```
### System: Roleplay as a pirate
### Human: hello
### Assistant: Ahoy, matey! How can I assist you today?
```
### vs.
```
### System: Roleplay as a pirate (not assistant!)
### Human: hello
### Assistant: Arrgh, matey! I be the Captain of this here ship. What business do ye have with me?
```
You could also just use LimaRP prompt format.
*Thanks to previous similar models such as Alpacino, Alpasta, and AlpacaDente for inspiring the creation of this model. Thanks also to the creators of the models involved in the merge. Original models:*
- [Hermes-LLongMA-2](https://huggingface.co/conceptofmind/Hermes-LLongMA-2-7b-8k)
- [Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-7b-guanaco-qlora)
- [LimaRP LoRA](https://huggingface.co/lemonilia/limarp-llama2) |
ittailup/lallama-13b-alpha | ittailup | 2023-08-09T21:56:02Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:finetune:meta-llama/Llama-2-13b-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-07T21:10:18Z | ---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: lallama-13b-alpha
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. -->
# lallama-13b-alpha
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
mandeepbagga/llama-2-13b-infyGPT | mandeepbagga | 2023-08-09T21:38:55Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-09T21:38:32Z | ---
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
|
Jiuzhouh/alpaca-lora-7b-webnlg-t2g | Jiuzhouh | 2023-08-09T21:19:20Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-08-09T21:19:11Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
szymonrucinski/good-mood | szymonrucinski | 2023-08-09T21:09:47Z | 0 | 0 | null | [
"license:cc-by-nc-sa-3.0",
"region:us"
]
| null | 2023-08-09T16:17:39Z | ---
license: cc-by-nc-sa-3.0
---
|
chronopt-research/vietnamese-gpt2-base | chronopt-research | 2023-08-09T20:58:46Z | 147 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"vi",
"dataset:duongttr/vi-dataset-for-pretrain",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-09T20:29:15Z | ---
license: apache-2.0
datasets:
- duongttr/vi-dataset-for-pretrain
language:
- vi
metrics:
- perplexity
pipeline_tag: text-generation
widget:
- text: Hôm nay tôi rất vui vì
- text: Hoàng Sa, Trường Sa là của Việt
model-index:
- name: chronopt-research/vietnamese-gpt2-base
results:
- task:
type: text-generation
metrics:
- type: perplexity
value: 51.35
verified: true
---
# Vietnamese `gpt2-base`
<!-- Provide a quick summary of what the model is/does. -->
This is a pretrained `gpt2-base` for Vietnamese language using casual language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
## Model Description
GPT-2 (*at first*) is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
This is the **base version** of GPT-2, with 137M parameters.
You could've found other pretrained version from here: [gpt2-medium](https://huggingface.co/chronopt-research/vietnamese-gpt2-medium), [gpt2-large]()
## Dataset used for pretraining
This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc.
The dataset consists of:
- [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi)
- [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus)
- [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar)
- [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi)
You can find out the combined version here: [duongttr/vi-dataset-for-pretrain](https://huggingface.co/datasets/duongttr/vi-dataset-for-pretrain)
## Hyperparamters & Results
We trained the model ~100k steps, with `lr=1e-4`, `bs=2560` (`single_batch_size=32` * `num_core=8` * `grad_cum=10`), `optimizer=adamw` on TPU-VM-3.8 from [TRC Program](https://sites.research.google/trc/about/). The training costs around **1 day**.
|Model|Eval Loss|Eval Perplexity|
|---|---|---|
|**gpt2-base**|**3.939**|**51.35**|
|gpt2-medium|2.8676|17.5948|
|gpt2-large|-|-|
## Contacts
Feel free to contact us via: [email]()
|
Bb8271/s | Bb8271 | 2023-08-09T20:53:35Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-08-09T20:53:33Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of sksks
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Test enoder was not trained.
|
GhifSmile/distilbert-base-uncased-DSC-new | GhifSmile | 2023-08-09T20:49:02Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-09T19:25:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: distilbert-base-uncased-DSC-new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-DSC-new
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1017
- Accuracy: 0.9902
- Precision: 0.9910
- Recall: 0.9909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 0.4743 | 1.0 | 618 | 0.1856 | 0.9633 | 0.9672 | 0.9647 |
| 0.0946 | 2.0 | 1236 | 0.1577 | 0.9707 | 0.9749 | 0.9733 |
| 0.0851 | 3.0 | 1854 | 0.1081 | 0.9853 | 0.9869 | 0.9858 |
| 0.0633 | 4.0 | 2472 | 0.1449 | 0.9841 | 0.9851 | 0.9837 |
| 0.0258 | 5.0 | 3090 | 0.1155 | 0.9829 | 0.9838 | 0.9829 |
| 0.022 | 6.0 | 3708 | 0.1089 | 0.9890 | 0.9899 | 0.9897 |
| 0.0147 | 7.0 | 4326 | 0.1092 | 0.9878 | 0.9885 | 0.9875 |
| 0.0043 | 8.0 | 4944 | 0.1017 | 0.9902 | 0.9910 | 0.9909 |
| 0.0041 | 9.0 | 5562 | 0.1033 | 0.9878 | 0.9885 | 0.9874 |
| 0.0012 | 10.0 | 6180 | 0.1093 | 0.9878 | 0.9885 | 0.9874 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
EgilKarlsen/DistilRoBERTa_Thunderbird-Anomaly_Baseline | EgilKarlsen | 2023-08-09T20:45:29Z | 107 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-09T20:25:00Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DistilRoBERTa_Thunderbird-Anomaly_Baseline
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. -->
# DistilRoBERTa_Thunderbird-Anomaly_Baseline
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
- Accuracy: 0.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0958 | 1.0 | 1094 | 0.0623 | 0.9846 |
| 0.0514 | 2.0 | 2188 | 0.0340 | 0.9846 |
| 0.0261 | 3.0 | 3282 | 0.0168 | 0.9896 |
| 0.0147 | 4.0 | 4376 | 0.0095 | 1.0 |
| 0.01 | 5.0 | 5470 | 0.0061 | 1.0 |
| 0.0071 | 6.0 | 6564 | 0.0042 | 1.0 |
| 0.0058 | 7.0 | 7658 | 0.0031 | 1.0 |
| 0.0046 | 8.0 | 8752 | 0.0025 | 1.0 |
| 0.0043 | 9.0 | 9846 | 0.0022 | 1.0 |
| 0.0038 | 10.0 | 10940 | 0.0021 | 1.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ederdt2023/Eder_Duenas | ederdt2023 | 2023-08-09T20:43:13Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-08-09T20:43:13Z | ---
license: creativeml-openrail-m
---
|
TotoLefo/Sheirlou500Epoch | TotoLefo | 2023-08-09T20:33:56Z | 0 | 0 | null | [
"AI VOICE",
"fr",
"region:us"
]
| null | 2023-08-09T20:31:07Z | ---
language:
- fr
tags:
- AI VOICE
---
# Model Card for Model ID
- **Developed by:** TOTO
|
FredericProtat/dqn-SpaceInvadersNoFrameskip-v4 | FredericProtat | 2023-08-09T20:24:42Z | 7 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-08-09T20:24:06Z | ---
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: 691.00 +/- 253.51
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 FredericProtat -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 FredericProtat -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 FredericProtat
```
## 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'}
```
|
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