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owanr/SBIC-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T01:54:14Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T03:09:20Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2809
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0855 | 1.0 | 12516 | 1.2298 |
| 0.9046 | 2.0 | 25032 | 1.2809 |
| 0.9135 | 3.0 | 37548 | 1.2809 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Dal3l/Ass1
|
Dal3l
| 2023-12-15T03:12:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T03:12:22Z |
Entry not found
|
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owanr/SChem5Labels-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T02:15:48Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T03:12:37Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
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. -->
# SChem5Labels-roberta-base-inter-shuffle-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7950
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0176 | 1.0 | 3164 | 3.9282 |
| 3.636 | 2.0 | 6328 | 3.7950 |
| 3.5245 | 3.0 | 9492 | 3.7950 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
GaboChoropan/Weegee
|
GaboChoropan
| 2023-12-17T04:12:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T03:16:56Z |
Entry not found
|
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] |
crumb/e4-k2-d1024-hm2-1gt
|
crumb
| 2023-12-15T03:40:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T03:19:39Z |
Entry not found
|
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maxwell-pi/wav2vec2-large-xls-r-300m-turkish-colab
|
maxwell-pi
| 2023-12-15T03:22:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T03:22:32Z |
Entry not found
|
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DanGoldBr/TESTE
|
DanGoldBr
| 2023-12-15T03:29:44Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T03:25:01Z |
---
license: openrail
---
|
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chongzhou/EdgeSAM
|
chongzhou
| 2023-12-15T03:33:25Z | 0 | 1 | null |
[
"onnx",
"region:us"
] | null | 2023-12-15T03:30:01Z |
Entry not found
|
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] |
huggingface/yrsq-dnj7-jghjk3-0
|
huggingface
| 2023-12-15T03:35:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain",
"dataset:huggingface/autotrain-data-yrsq-dnj7-jghjk3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T03:30:01Z |
Invalid username or password.
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_24
|
ADISH007
| 2023-12-15T03:31:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T03:30:48Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_chatGPT_Seed105
|
behzadnet
| 2023-12-15T03:31:20Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T03:31:13Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.7.0.dev0
## 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 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_chatGPT_Seed105
|
behzadnet
| 2023-12-15T03:31:29Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T03:31:27Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.7.0.dev0
|
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] |
sefcar/modelsefcar
|
sefcar
| 2023-12-15T20:05:54Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T03:32:02Z |
---
license: mit
---
|
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] |
arnavm234/fine_tuned_1
|
arnavm234
| 2023-12-15T03:48:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T03:48:41Z |
Entry not found
|
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] |
Xrunner/pokemon-lora
|
Xrunner
| 2023-12-15T03:51:52Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T03:51:52Z |
---
license: apache-2.0
---
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep07
|
stvhuang
| 2023-12-15T03:54:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T03:52:23Z |
Entry not found
|
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owanr/SBIC-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T02:20:14Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T03:53:24Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-inter-sorted-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5815
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5004 | 1.0 | 12516 | 2.4221 |
| 2.534 | 2.0 | 25032 | 2.5815 |
| 2.7586 | 3.0 | 37548 | 2.5815 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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raja22/ft_sd
|
raja22
| 2023-12-15T04:04:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:04:59Z |
Entry not found
|
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Maxinstellar/gpt-week-summarization_256_256_16_0
|
Maxinstellar
| 2023-12-15T12:10:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:05:42Z |
Entry not found
|
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] |
Nopphakorn/tf_Seq2Bin
|
Nopphakorn
| 2023-12-15T04:16:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T04:06:52Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Nopphakorn/tf_Seq2Bin
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. -->
# Nopphakorn/tf_Seq2Bin
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3882
- Validation Loss: 0.3856
- Train Accuracy: 0.8706
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 15835, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3882 | 0.3856 | 0.8706 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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AndresBriC/q-FrozenLake-v1-4x4-noSlippery
|
AndresBriC
| 2023-12-15T04:11:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T04:11:30Z |
---
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="AndresBriC/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"])
```
|
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] |
maxwell-pi/wav2vec2-large-xls-r-300m-basque-colab
|
maxwell-pi
| 2023-12-15T20:44:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T04:11:44Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-basque-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-basque-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Wanfq/distillchat_1.7.4
|
Wanfq
| 2023-12-15T05:01:53Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"distillchat",
"openchat",
"Distill-SFT",
"en",
"dataset:Wanfq/distillchat_v1_mixture",
"base_model:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T04:16:09Z |
---
license: apache-2.0
model-index:
- name: distillchat_v1.7
results: []
tags:
- distillchat
- openchat
- mistral
- Distill-SFT
datasets:
- Wanfq/distillchat_v1_mixture
language:
- en
base_model: openchat/openchat_3.5
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for DistillChat V1.7
DistillChat V1.7 is a fine-tuned version of Mistral trained on a mixture of publicly available, synthetic, and human datasets.
The difference from DistillChat V1.0 is we follow openchat-3.5 to use the "Code" rather than "GPT-4 Correct" condition for code-related datasets.
The V1.7.4 version is the "merge-single-teacher-distill" version. We use Nous-Capybara-34B and Phind-CodeLlama-34B-v2 as the teacher LLMs to conduct knowledge distillation.
## Model description
- **Model type:** Distill-SFT
- **Language(s) (NLP):** Primarily English
- **Finetuned from model:** [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
### Model Sources
- **Repository:** https://github.com/fanqiwan/distillchat
- **Model Family:** All the models and the dataset are found in the [DistillChat collection](https://huggingface.co/collections/Wanfq/distillchat-6562c1fe4e74b2075a0e617e).
## Performance
TBD
## Input Format
The model is trained to use the following format:
```
GPT4 Correct User: Your message here!<|end_of_turn|>GPT4 Correct Assistant:
```
For best results, format all inputs in this manner.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [DistillChat V1 Mixture](https://huggingface.co/datasets/Wanfq/distillchat_v1_mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
Here's how you can run the model using the 🤗 Transformers:
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("Wanfq/distillchat_v1.7")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
The GPT4 template is also available as the integrated `tokenizer.chat_template`,
which can be used instead of manually specifying the template:
```python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The DistillChat models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Mistral models, however, it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during Distill-SFT training:
- learning_rate: 5e-06
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
- model_max_length: 2048
- do_distill: True
- distill_with_ref_model: True
- distill_with_aligned_model_0: False
- distill_with_aligned_model_1: True
- distill_optimize_type: ranking
- distill_weight_type: soft_metric
- distill_loss_type: ce
- distill_teacher_temperature: 1.0
- lm_loss_weight: 0.9
- distill_greater_as_gt: True
- distill_greater_as_gt_type: hard
## Citation
TBD
## Acknowledgements
TBD
*Model card adapted from [Tulu 2](https://huggingface.co/allenai/tulu-2-dpo-70b/blob/main/README.md)*
|
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] |
rawrbeanz/Scott
|
rawrbeanz
| 2023-12-15T07:33:18Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-15T04:16:28Z |
---
license: other
license_name: idk
license_link: LICENSE
---
|
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Wanfq/distillchat_1.7.5
|
Wanfq
| 2023-12-15T05:04:38Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"distillchat",
"openchat",
"Distill-SFT",
"en",
"dataset:Wanfq/distillchat_v1_mixture",
"base_model:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T04:17:13Z |
---
license: apache-2.0
model-index:
- name: distillchat_v1.7
results: []
tags:
- distillchat
- openchat
- mistral
- Distill-SFT
datasets:
- Wanfq/distillchat_v1_mixture
language:
- en
base_model: openchat/openchat_3.5
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for DistillChat V1.7
DistillChat V1.7 is a fine-tuned version of Mistral trained on a mixture of publicly available, synthetic, and human datasets.
The difference from DistillChat V1.0 is we follow openchat-3.5 to use the "Code" rather than "GPT-4 Correct" condition for code-related datasets.
The V1.7.5 version is the "merge-single-teacher-distill" version. We use Nous-Capybara-34B and Phind-CodeLlama-34B-v2 as the teacher LLMs to conduct knowledge distillation.
## Model description
- **Model type:** Distill-SFT
- **Language(s) (NLP):** Primarily English
- **Finetuned from model:** [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
### Model Sources
- **Repository:** https://github.com/fanqiwan/distillchat
- **Model Family:** All the models and the dataset are found in the [DistillChat collection](https://huggingface.co/collections/Wanfq/distillchat-6562c1fe4e74b2075a0e617e).
## Performance
TBD
## Input Format
The model is trained to use the following format:
```
GPT4 Correct User: Your message here!<|end_of_turn|>GPT4 Correct Assistant:
```
For best results, format all inputs in this manner.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [DistillChat V1 Mixture](https://huggingface.co/datasets/Wanfq/distillchat_v1_mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
Here's how you can run the model using the 🤗 Transformers:
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("Wanfq/distillchat_v1.7")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
The GPT4 template is also available as the integrated `tokenizer.chat_template`,
which can be used instead of manually specifying the template:
```python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The DistillChat models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Mistral models, however, it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during Distill-SFT training:
- learning_rate: 5e-06
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
- model_max_length: 2048
- do_distill: True
- distill_with_ref_model: True
- distill_with_aligned_model_0: True
- distill_with_aligned_model_1: False
- distill_optimize_type: ranking
- distill_weight_type: soft_metric
- distill_loss_type: ce
- distill_teacher_temperature: 1.0
- lm_loss_weight: 0.9
- distill_greater_as_gt: True
- distill_greater_as_gt_type: hard
## Citation
TBD
## Acknowledgements
TBD
*Model card adapted from [Tulu 2](https://huggingface.co/allenai/tulu-2-dpo-70b/blob/main/README.md)*
|
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hieuAI/my_awesome_qa_model
|
hieuAI
| 2023-12-15T04:23:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:23:18Z |
Entry not found
|
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AndresBriC/Taxi-v3_DeepRLCourse
|
AndresBriC
| 2023-12-15T04:26:19Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T04:26:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_DeepRLCourse
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AndresBriC/Taxi-v3_DeepRLCourse", 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"])
```
|
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mayanklad/faq-canada-immigration-tokenizer
|
mayanklad
| 2023-12-15T04:26:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:26:31Z |
Entry not found
|
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enniA/resultado_1
|
enniA
| 2023-12-15T04:31:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:31:57Z |
Entry not found
|
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preeth46/gym
|
preeth46
| 2023-12-15T04:48:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:36:37Z |
Entry not found
|
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] |
CheWei/distilbert-base-uncased-lora-text-classification
|
CheWei
| 2023-12-15T04:41:33Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T04:41:30Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-lora-text-classification
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-lora-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8835
- Accuracy: {'accuracy': 0.889}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.4674 | {'accuracy': 0.849} |
| 0.452 | 2.0 | 500 | 0.5597 | {'accuracy': 0.871} |
| 0.452 | 3.0 | 750 | 0.5479 | {'accuracy': 0.876} |
| 0.2332 | 4.0 | 1000 | 0.6206 | {'accuracy': 0.885} |
| 0.2332 | 5.0 | 1250 | 0.6563 | {'accuracy': 0.879} |
| 0.102 | 6.0 | 1500 | 0.8237 | {'accuracy': 0.881} |
| 0.102 | 7.0 | 1750 | 0.8258 | {'accuracy': 0.883} |
| 0.0467 | 8.0 | 2000 | 0.8632 | {'accuracy': 0.884} |
| 0.0467 | 9.0 | 2250 | 0.8802 | {'accuracy': 0.886} |
| 0.013 | 10.0 | 2500 | 0.8835 | {'accuracy': 0.889} |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
zabir735/clip-by-zabir_bert
|
zabir735
| 2023-12-15T05:33:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"base_model:openai/clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2023-12-15T04:42:00Z |
---
base_model: openai/clip-vit-base-patch16
tags:
- generated_from_trainer
model-index:
- name: clip-by-zabir_bert
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-by-zabir_bert
This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1560
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Bhandari007/distil-whisper-large-v3-ne-slr-without-psl
|
Bhandari007
| 2023-12-15T04:42:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:42:17Z |
Entry not found
|
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] |
dubanulises/Duban
|
dubanulises
| 2023-12-15T13:47:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T04:53:18Z |
Entry not found
|
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] |
reboot168/llama-2-7b-fine-tuned
|
reboot168
| 2023-12-15T05:12:12Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T04:56:06Z |
Entry not found
|
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-0.028933173045516014,
-0.05796251446008682,
0.31015992164611816,
-1.0262157917022705
] |
banhabang/full_reg_model_4000
|
banhabang
| 2023-12-15T04:58:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T04:56:10Z |
Entry not found
|
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] |
AdriMSH/resultado_2
|
AdriMSH
| 2023-12-16T02:41:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-15T05:00:54Z |
---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: resultado_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resultado_2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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rilkery/corgy_dog_LoRA
|
rilkery
| 2023-12-15T05:02:25Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-15T05:02:25Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - rilkery/corgy_dog_LoRA
<Gallery />
## Model description
These are rilkery/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](rilkery/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
|
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Wanfq/distillchat_1.7.6
|
Wanfq
| 2023-12-15T06:03:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"distillchat",
"openchat",
"Distill-SFT",
"en",
"dataset:Wanfq/distillchat_v1_mixture",
"base_model:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T05:02:29Z |
---
license: apache-2.0
model-index:
- name: distillchat_v1.7
results: []
tags:
- distillchat
- openchat
- mistral
- Distill-SFT
datasets:
- Wanfq/distillchat_v1_mixture
language:
- en
base_model: openchat/openchat_3.5
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for DistillChat V1.7
DistillChat V1.7 is a fine-tuned version of Mistral trained on a mixture of publicly available, synthetic, and human datasets.
The difference from DistillChat V1.0 is we follow openchat-3.5 to use the "Code" rather than "GPT-4 Correct" condition for code-related datasets.
The V1.7.6 version is the "multi-teacher-distill" version. We use Nous-Capybara-34B and Phind-CodeLlama-34B-v2 as the teacher LLMs to conduct knowledge distillation.
## Model description
- **Model type:** Distill-SFT
- **Language(s) (NLP):** Primarily English
- **Finetuned from model:** [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
### Model Sources
- **Repository:** https://github.com/fanqiwan/distillchat
- **Model Family:** All the models and the dataset are found in the [DistillChat collection](https://huggingface.co/collections/Wanfq/distillchat-6562c1fe4e74b2075a0e617e).
## Performance
TBD
## Input Format
The model is trained to use the following format:
```
GPT4 Correct User: Your message here!<|end_of_turn|>GPT4 Correct Assistant:
```
For best results, format all inputs in this manner.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [DistillChat V1 Mixture](https://huggingface.co/datasets/Wanfq/distillchat_v1_mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
Here's how you can run the model using the 🤗 Transformers:
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("Wanfq/distillchat_v1.7")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
The GPT4 template is also available as the integrated `tokenizer.chat_template`,
which can be used instead of manually specifying the template:
```python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The DistillChat models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Mistral models, however, it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during Distill-SFT training:
- learning_rate: 5e-06
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
- model_max_length: 2048
- do_distill: True
- distill_with_ref_model: True
- distill_with_aligned_model_0: True
- distill_with_aligned_model_1: True
- distill_optimize_type: ranking
- distill_weight_type: soft_metric
- distill_loss_type: ce
- distill_teacher_temperature: 1.0
- lm_loss_weight: 0.9
- distill_greater_as_gt: True
- distill_greater_as_gt_type: hard
## Citation
TBD
## Acknowledgements
TBD
*Model card adapted from [Tulu 2](https://huggingface.co/allenai/tulu-2-dpo-70b/blob/main/README.md)*
|
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enniA-0/resultado_1
|
enniA-0
| 2023-12-15T05:05:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:05:57Z |
Entry not found
|
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Ananya1997/food_classifier
|
Ananya1997
| 2023-12-15T05:06:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:06:40Z |
Entry not found
|
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kchekuri/kchekuri_qa_model
|
kchekuri
| 2023-12-15T05:06:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:06:43Z |
Entry not found
|
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giangduong/multinews-length2048
|
giangduong
| 2023-12-15T05:09:35Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-15T05:08:49Z |
Entry not found
|
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gracejwu/ppo-CartPole-v1
|
gracejwu
| 2023-12-15T05:18:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:18:11Z |
Entry not found
|
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-0.028933173045516014,
-0.05796251446008682,
0.31015992164611816,
-1.0262157917022705
] |
kwwww/bert-base-uncased_4_1039_token_headwise
|
kwwww
| 2023-12-15T09:29:02Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"dataset:bionlp2004",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T05:19:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bionlp2004
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased_4_1039_token_headwise
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_4_1039_token_headwise
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bionlp2004 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7036
- Precision: 0.7424
- Recall: 0.7713
- F1: 0.7566
- Accuracy: 0.9330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 65 | 0.3621 | 0.3676 | 0.4841 | 0.4178 | 0.8704 |
| No log | 2.0 | 130 | 0.2679 | 0.5317 | 0.6341 | 0.5784 | 0.9048 |
| No log | 3.0 | 195 | 0.2544 | 0.5950 | 0.6512 | 0.6218 | 0.9134 |
| No log | 4.0 | 260 | 0.2279 | 0.6275 | 0.7048 | 0.6640 | 0.9256 |
| No log | 5.0 | 325 | 0.2279 | 0.6544 | 0.7308 | 0.6905 | 0.9263 |
| No log | 6.0 | 390 | 0.2459 | 0.6537 | 0.7497 | 0.6984 | 0.9253 |
| No log | 7.0 | 455 | 0.2392 | 0.6777 | 0.7423 | 0.7086 | 0.9290 |
| 0.2297 | 8.0 | 520 | 0.2729 | 0.6762 | 0.6695 | 0.6729 | 0.9232 |
| 0.2297 | 9.0 | 585 | 0.2805 | 0.6490 | 0.7522 | 0.6968 | 0.9245 |
| 0.2297 | 10.0 | 650 | 0.3040 | 0.6514 | 0.7182 | 0.6832 | 0.9239 |
| 0.2297 | 11.0 | 715 | 0.2952 | 0.6745 | 0.7146 | 0.6939 | 0.9263 |
| 0.2297 | 12.0 | 780 | 0.2960 | 0.6708 | 0.7164 | 0.6929 | 0.9265 |
| 0.2297 | 13.0 | 845 | 0.3286 | 0.6830 | 0.7270 | 0.7043 | 0.9260 |
| 0.2297 | 14.0 | 910 | 0.3219 | 0.6595 | 0.7331 | 0.6944 | 0.9253 |
| 0.2297 | 15.0 | 975 | 0.3222 | 0.6858 | 0.7286 | 0.7065 | 0.9272 |
| 0.0631 | 16.0 | 1040 | 0.3338 | 0.6950 | 0.7500 | 0.7215 | 0.9295 |
| 0.0631 | 17.0 | 1105 | 0.3447 | 0.6643 | 0.7364 | 0.6985 | 0.9268 |
| 0.0631 | 18.0 | 1170 | 0.3583 | 0.6823 | 0.7279 | 0.7044 | 0.9264 |
| 0.0631 | 19.0 | 1235 | 0.3492 | 0.6939 | 0.7295 | 0.7113 | 0.9278 |
| 0.0631 | 20.0 | 1300 | 0.3729 | 0.6621 | 0.7684 | 0.7113 | 0.9259 |
| 0.0631 | 21.0 | 1365 | 0.3892 | 0.6548 | 0.6944 | 0.6740 | 0.9221 |
| 0.0631 | 22.0 | 1430 | 0.3885 | 0.6744 | 0.7086 | 0.6911 | 0.9241 |
| 0.0631 | 23.0 | 1495 | 0.3976 | 0.6511 | 0.7554 | 0.6994 | 0.9231 |
| 0.0367 | 24.0 | 1560 | 0.3550 | 0.6495 | 0.7491 | 0.6958 | 0.9262 |
| 0.0367 | 25.0 | 1625 | 0.3628 | 0.6689 | 0.7391 | 0.7023 | 0.9282 |
| 0.0367 | 26.0 | 1690 | 0.4010 | 0.6649 | 0.7526 | 0.7060 | 0.9260 |
| 0.0367 | 27.0 | 1755 | 0.4152 | 0.6683 | 0.7502 | 0.7069 | 0.9267 |
| 0.0367 | 28.0 | 1820 | 0.4081 | 0.6820 | 0.7322 | 0.7062 | 0.9267 |
| 0.0367 | 29.0 | 1885 | 0.4058 | 0.6834 | 0.7385 | 0.7099 | 0.9281 |
| 0.0367 | 30.0 | 1950 | 0.4027 | 0.6688 | 0.7194 | 0.6932 | 0.9253 |
| 0.0279 | 31.0 | 2015 | 0.3923 | 0.6847 | 0.7518 | 0.7167 | 0.9294 |
| 0.0279 | 32.0 | 2080 | 0.4188 | 0.6800 | 0.7542 | 0.7152 | 0.9285 |
| 0.0279 | 33.0 | 2145 | 0.4250 | 0.6815 | 0.7412 | 0.7101 | 0.9288 |
| 0.0279 | 34.0 | 2210 | 0.4161 | 0.6864 | 0.7628 | 0.7226 | 0.9299 |
| 0.0279 | 35.0 | 2275 | 0.4158 | 0.7025 | 0.7590 | 0.7297 | 0.9298 |
| 0.0279 | 36.0 | 2340 | 0.4208 | 0.6852 | 0.7648 | 0.7228 | 0.9285 |
| 0.0279 | 37.0 | 2405 | 0.4105 | 0.6930 | 0.7311 | 0.7115 | 0.9283 |
| 0.0279 | 38.0 | 2470 | 0.4363 | 0.6726 | 0.7794 | 0.7221 | 0.9287 |
| 0.019 | 39.0 | 2535 | 0.4223 | 0.6823 | 0.7482 | 0.7137 | 0.9269 |
| 0.019 | 40.0 | 2600 | 0.4363 | 0.6913 | 0.7227 | 0.7066 | 0.9267 |
| 0.019 | 41.0 | 2665 | 0.4231 | 0.6982 | 0.7610 | 0.7282 | 0.9302 |
| 0.019 | 42.0 | 2730 | 0.4709 | 0.6851 | 0.7193 | 0.7017 | 0.9250 |
| 0.019 | 43.0 | 2795 | 0.4535 | 0.7179 | 0.7272 | 0.7225 | 0.9293 |
| 0.019 | 44.0 | 2860 | 0.4276 | 0.6997 | 0.7601 | 0.7286 | 0.9299 |
| 0.019 | 45.0 | 2925 | 0.4856 | 0.6858 | 0.6840 | 0.6849 | 0.9242 |
| 0.019 | 46.0 | 2990 | 0.4392 | 0.6899 | 0.7319 | 0.7102 | 0.9256 |
| 0.0189 | 47.0 | 3055 | 0.4423 | 0.6921 | 0.7623 | 0.7255 | 0.9298 |
| 0.0189 | 48.0 | 3120 | 0.4592 | 0.6925 | 0.7560 | 0.7229 | 0.9292 |
| 0.0189 | 49.0 | 3185 | 0.4285 | 0.6818 | 0.7578 | 0.7178 | 0.9290 |
| 0.0189 | 50.0 | 3250 | 0.4413 | 0.6775 | 0.7407 | 0.7077 | 0.9264 |
| 0.0189 | 51.0 | 3315 | 0.4474 | 0.6901 | 0.7553 | 0.7212 | 0.9292 |
| 0.0189 | 52.0 | 3380 | 0.4558 | 0.6790 | 0.7608 | 0.7176 | 0.9278 |
| 0.0189 | 53.0 | 3445 | 0.4695 | 0.6894 | 0.7446 | 0.7160 | 0.9281 |
| 0.0157 | 54.0 | 3510 | 0.4737 | 0.6748 | 0.7333 | 0.7029 | 0.9266 |
| 0.0157 | 55.0 | 3575 | 0.5053 | 0.6838 | 0.7529 | 0.7167 | 0.9266 |
| 0.0157 | 56.0 | 3640 | 0.4746 | 0.6745 | 0.7315 | 0.7019 | 0.9254 |
| 0.0157 | 57.0 | 3705 | 0.4753 | 0.7202 | 0.7277 | 0.7239 | 0.9288 |
| 0.0157 | 58.0 | 3770 | 0.4465 | 0.6942 | 0.7558 | 0.7237 | 0.9296 |
| 0.0157 | 59.0 | 3835 | 0.4873 | 0.6992 | 0.7634 | 0.7299 | 0.9294 |
| 0.0157 | 60.0 | 3900 | 0.4738 | 0.6784 | 0.7457 | 0.7105 | 0.9267 |
| 0.0157 | 61.0 | 3965 | 0.5076 | 0.6827 | 0.7760 | 0.7263 | 0.9270 |
| 0.0153 | 62.0 | 4030 | 0.4785 | 0.6977 | 0.7524 | 0.7240 | 0.9302 |
| 0.0153 | 63.0 | 4095 | 0.5587 | 0.7030 | 0.6841 | 0.6934 | 0.9219 |
| 0.0153 | 64.0 | 4160 | 0.4740 | 0.6759 | 0.7392 | 0.7062 | 0.9266 |
| 0.0153 | 65.0 | 4225 | 0.4434 | 0.6807 | 0.7473 | 0.7124 | 0.9276 |
| 0.0153 | 66.0 | 4290 | 0.4587 | 0.6972 | 0.7455 | 0.7206 | 0.9291 |
| 0.0153 | 67.0 | 4355 | 0.4701 | 0.6967 | 0.7497 | 0.7222 | 0.9283 |
| 0.0153 | 68.0 | 4420 | 0.4957 | 0.6823 | 0.7414 | 0.7106 | 0.9267 |
| 0.0153 | 69.0 | 4485 | 0.4917 | 0.6851 | 0.7547 | 0.7183 | 0.9285 |
| 0.0147 | 70.0 | 4550 | 0.5401 | 0.6675 | 0.7430 | 0.7033 | 0.9249 |
| 0.0147 | 71.0 | 4615 | 0.4943 | 0.6700 | 0.7407 | 0.7036 | 0.9278 |
| 0.0147 | 72.0 | 4680 | 0.4488 | 0.6931 | 0.7457 | 0.7184 | 0.9284 |
| 0.0147 | 73.0 | 4745 | 0.4615 | 0.6986 | 0.7448 | 0.7210 | 0.9296 |
| 0.0147 | 74.0 | 4810 | 0.4820 | 0.7035 | 0.7416 | 0.7220 | 0.9294 |
| 0.0147 | 75.0 | 4875 | 0.5227 | 0.7040 | 0.7274 | 0.7155 | 0.9261 |
| 0.0147 | 76.0 | 4940 | 0.4992 | 0.6838 | 0.7288 | 0.7056 | 0.9261 |
| 0.0148 | 77.0 | 5005 | 0.5103 | 0.6476 | 0.7639 | 0.7010 | 0.9246 |
| 0.0148 | 78.0 | 5070 | 0.5046 | 0.6940 | 0.7266 | 0.7099 | 0.9258 |
| 0.0148 | 79.0 | 5135 | 0.4947 | 0.7072 | 0.7382 | 0.7224 | 0.9294 |
| 0.0148 | 80.0 | 5200 | 0.5373 | 0.7020 | 0.6881 | 0.6950 | 0.9211 |
| 0.0148 | 81.0 | 5265 | 0.5046 | 0.7016 | 0.7342 | 0.7175 | 0.9276 |
| 0.0148 | 82.0 | 5330 | 0.5293 | 0.6869 | 0.7679 | 0.7251 | 0.9285 |
| 0.0148 | 83.0 | 5395 | 0.4888 | 0.6945 | 0.7583 | 0.7250 | 0.9297 |
| 0.0148 | 84.0 | 5460 | 0.5260 | 0.7000 | 0.7590 | 0.7284 | 0.9289 |
| 0.0121 | 85.0 | 5525 | 0.4930 | 0.6701 | 0.7128 | 0.6908 | 0.9231 |
| 0.0121 | 86.0 | 5590 | 0.4973 | 0.7025 | 0.7137 | 0.7081 | 0.9268 |
| 0.0121 | 87.0 | 5655 | 0.4871 | 0.6855 | 0.7364 | 0.7100 | 0.9262 |
| 0.0121 | 88.0 | 5720 | 0.4794 | 0.7020 | 0.7468 | 0.7237 | 0.9296 |
| 0.0121 | 89.0 | 5785 | 0.5453 | 0.6603 | 0.7374 | 0.6967 | 0.9215 |
| 0.0121 | 90.0 | 5850 | 0.4842 | 0.6952 | 0.7641 | 0.7280 | 0.9300 |
| 0.0121 | 91.0 | 5915 | 0.4918 | 0.7056 | 0.7311 | 0.7181 | 0.9290 |
| 0.0121 | 92.0 | 5980 | 0.5144 | 0.6811 | 0.7405 | 0.7096 | 0.9258 |
| 0.0123 | 93.0 | 6045 | 0.5427 | 0.6905 | 0.7284 | 0.7090 | 0.9251 |
| 0.0123 | 94.0 | 6110 | 0.5272 | 0.7066 | 0.7529 | 0.7290 | 0.9287 |
| 0.0123 | 95.0 | 6175 | 0.5289 | 0.7087 | 0.7439 | 0.7259 | 0.9299 |
| 0.0123 | 96.0 | 6240 | 0.4713 | 0.7008 | 0.7454 | 0.7224 | 0.9288 |
| 0.0123 | 97.0 | 6305 | 0.4996 | 0.7040 | 0.7499 | 0.7262 | 0.9292 |
| 0.0123 | 98.0 | 6370 | 0.4996 | 0.7015 | 0.7428 | 0.7216 | 0.9298 |
| 0.0123 | 99.0 | 6435 | 0.5215 | 0.6938 | 0.7527 | 0.7221 | 0.9280 |
| 0.0091 | 100.0 | 6500 | 0.5168 | 0.7095 | 0.7392 | 0.7240 | 0.9299 |
| 0.0091 | 101.0 | 6565 | 0.4943 | 0.7013 | 0.7493 | 0.7245 | 0.9283 |
| 0.0091 | 102.0 | 6630 | 0.5093 | 0.7107 | 0.7459 | 0.7279 | 0.9290 |
| 0.0091 | 103.0 | 6695 | 0.5147 | 0.6897 | 0.7075 | 0.6985 | 0.9252 |
| 0.0091 | 104.0 | 6760 | 0.4826 | 0.7209 | 0.7025 | 0.7116 | 0.9273 |
| 0.0091 | 105.0 | 6825 | 0.5267 | 0.7136 | 0.7295 | 0.7215 | 0.9292 |
| 0.0091 | 106.0 | 6890 | 0.5081 | 0.6907 | 0.7547 | 0.7213 | 0.9284 |
| 0.0091 | 107.0 | 6955 | 0.4663 | 0.6927 | 0.7502 | 0.7203 | 0.9285 |
| 0.0112 | 108.0 | 7020 | 0.5241 | 0.7091 | 0.7198 | 0.7144 | 0.9272 |
| 0.0112 | 109.0 | 7085 | 0.4689 | 0.6904 | 0.7751 | 0.7303 | 0.9290 |
| 0.0112 | 110.0 | 7150 | 0.5325 | 0.7081 | 0.7670 | 0.7363 | 0.9291 |
| 0.0112 | 111.0 | 7215 | 0.5381 | 0.7037 | 0.7158 | 0.7097 | 0.9246 |
| 0.0112 | 112.0 | 7280 | 0.5423 | 0.7160 | 0.7184 | 0.7172 | 0.9280 |
| 0.0112 | 113.0 | 7345 | 0.5480 | 0.6945 | 0.7628 | 0.7271 | 0.9281 |
| 0.0112 | 114.0 | 7410 | 0.5324 | 0.7074 | 0.7340 | 0.7205 | 0.9270 |
| 0.0112 | 115.0 | 7475 | 0.5023 | 0.6825 | 0.7310 | 0.7059 | 0.9248 |
| 0.0105 | 116.0 | 7540 | 0.4980 | 0.7065 | 0.7681 | 0.7360 | 0.9311 |
| 0.0105 | 117.0 | 7605 | 0.5431 | 0.6901 | 0.7455 | 0.7168 | 0.9260 |
| 0.0105 | 118.0 | 7670 | 0.5262 | 0.6651 | 0.7630 | 0.7107 | 0.9233 |
| 0.0105 | 119.0 | 7735 | 0.4880 | 0.7021 | 0.7463 | 0.7235 | 0.9304 |
| 0.0105 | 120.0 | 7800 | 0.5234 | 0.7154 | 0.7108 | 0.7131 | 0.9259 |
| 0.0105 | 121.0 | 7865 | 0.5343 | 0.6967 | 0.7133 | 0.7049 | 0.9234 |
| 0.0105 | 122.0 | 7930 | 0.4882 | 0.6893 | 0.7560 | 0.7211 | 0.9275 |
| 0.0105 | 123.0 | 7995 | 0.5187 | 0.7107 | 0.7416 | 0.7258 | 0.9289 |
| 0.011 | 124.0 | 8060 | 0.5276 | 0.6932 | 0.7531 | 0.7219 | 0.9275 |
| 0.011 | 125.0 | 8125 | 0.5346 | 0.7019 | 0.7394 | 0.7202 | 0.9270 |
| 0.011 | 126.0 | 8190 | 0.5380 | 0.7099 | 0.7347 | 0.7221 | 0.9284 |
| 0.011 | 127.0 | 8255 | 0.4963 | 0.7102 | 0.7538 | 0.7314 | 0.9302 |
| 0.011 | 128.0 | 8320 | 0.5169 | 0.7036 | 0.7182 | 0.7108 | 0.9260 |
| 0.011 | 129.0 | 8385 | 0.5335 | 0.7208 | 0.7389 | 0.7297 | 0.9304 |
| 0.011 | 130.0 | 8450 | 0.5204 | 0.6989 | 0.7527 | 0.7248 | 0.9285 |
| 0.0088 | 131.0 | 8515 | 0.5203 | 0.6956 | 0.7385 | 0.7164 | 0.9268 |
| 0.0088 | 132.0 | 8580 | 0.5236 | 0.6822 | 0.7450 | 0.7122 | 0.9254 |
| 0.0088 | 133.0 | 8645 | 0.4771 | 0.6908 | 0.7738 | 0.7300 | 0.9304 |
| 0.0088 | 134.0 | 8710 | 0.5215 | 0.7137 | 0.7383 | 0.7258 | 0.9287 |
| 0.0088 | 135.0 | 8775 | 0.5171 | 0.7109 | 0.7441 | 0.7271 | 0.9298 |
| 0.0088 | 136.0 | 8840 | 0.5616 | 0.7222 | 0.7252 | 0.7237 | 0.9286 |
| 0.0088 | 137.0 | 8905 | 0.5248 | 0.7030 | 0.7601 | 0.7305 | 0.9283 |
| 0.0088 | 138.0 | 8970 | 0.5134 | 0.7080 | 0.7695 | 0.7375 | 0.9301 |
| 0.0083 | 139.0 | 9035 | 0.5323 | 0.7096 | 0.7346 | 0.7219 | 0.9280 |
| 0.0083 | 140.0 | 9100 | 0.5319 | 0.6881 | 0.7601 | 0.7223 | 0.9266 |
| 0.0083 | 141.0 | 9165 | 0.5579 | 0.7081 | 0.7434 | 0.7253 | 0.9273 |
| 0.0083 | 142.0 | 9230 | 0.5526 | 0.6931 | 0.7628 | 0.7263 | 0.9289 |
| 0.0083 | 143.0 | 9295 | 0.5019 | 0.7056 | 0.7650 | 0.7341 | 0.9302 |
| 0.0083 | 144.0 | 9360 | 0.5722 | 0.6983 | 0.7394 | 0.7183 | 0.9263 |
| 0.0083 | 145.0 | 9425 | 0.5329 | 0.6989 | 0.7625 | 0.7293 | 0.9285 |
| 0.0083 | 146.0 | 9490 | 0.5908 | 0.6953 | 0.7526 | 0.7228 | 0.9271 |
| 0.0072 | 147.0 | 9555 | 0.5361 | 0.7113 | 0.7488 | 0.7295 | 0.9269 |
| 0.0072 | 148.0 | 9620 | 0.5338 | 0.7161 | 0.7362 | 0.7260 | 0.9292 |
| 0.0072 | 149.0 | 9685 | 0.5415 | 0.6955 | 0.7380 | 0.7161 | 0.9278 |
| 0.0072 | 150.0 | 9750 | 0.6163 | 0.7042 | 0.6822 | 0.6930 | 0.9208 |
| 0.0072 | 151.0 | 9815 | 0.5642 | 0.7000 | 0.7560 | 0.7269 | 0.9285 |
| 0.0072 | 152.0 | 9880 | 0.5096 | 0.6997 | 0.7547 | 0.7262 | 0.9270 |
| 0.0072 | 153.0 | 9945 | 0.5272 | 0.7078 | 0.7504 | 0.7285 | 0.9287 |
| 0.0083 | 154.0 | 10010 | 0.5149 | 0.7013 | 0.7708 | 0.7344 | 0.9277 |
| 0.0083 | 155.0 | 10075 | 0.5412 | 0.7104 | 0.7661 | 0.7372 | 0.9286 |
| 0.0083 | 156.0 | 10140 | 0.5437 | 0.6905 | 0.7758 | 0.7307 | 0.9268 |
| 0.0083 | 157.0 | 10205 | 0.5201 | 0.7018 | 0.7785 | 0.7382 | 0.9293 |
| 0.0083 | 158.0 | 10270 | 0.5342 | 0.7146 | 0.7486 | 0.7312 | 0.9298 |
| 0.0083 | 159.0 | 10335 | 0.5250 | 0.6920 | 0.7059 | 0.6989 | 0.9230 |
| 0.0083 | 160.0 | 10400 | 0.5368 | 0.7079 | 0.7587 | 0.7324 | 0.9304 |
| 0.0083 | 161.0 | 10465 | 0.5146 | 0.6989 | 0.7567 | 0.7267 | 0.9294 |
| 0.008 | 162.0 | 10530 | 0.5450 | 0.7107 | 0.7486 | 0.7292 | 0.9294 |
| 0.008 | 163.0 | 10595 | 0.5746 | 0.6972 | 0.7306 | 0.7135 | 0.9238 |
| 0.008 | 164.0 | 10660 | 0.5169 | 0.7152 | 0.7349 | 0.7249 | 0.9283 |
| 0.008 | 165.0 | 10725 | 0.5512 | 0.7138 | 0.7299 | 0.7218 | 0.9266 |
| 0.008 | 166.0 | 10790 | 0.5472 | 0.7180 | 0.7029 | 0.7103 | 0.9244 |
| 0.008 | 167.0 | 10855 | 0.5727 | 0.6987 | 0.7567 | 0.7265 | 0.9266 |
| 0.008 | 168.0 | 10920 | 0.5660 | 0.6860 | 0.7610 | 0.7216 | 0.9256 |
| 0.008 | 169.0 | 10985 | 0.4885 | 0.7207 | 0.7587 | 0.7392 | 0.9304 |
| 0.0077 | 170.0 | 11050 | 0.5768 | 0.6752 | 0.7461 | 0.7089 | 0.9228 |
| 0.0077 | 171.0 | 11115 | 0.5170 | 0.7143 | 0.7313 | 0.7227 | 0.9279 |
| 0.0077 | 172.0 | 11180 | 0.5368 | 0.7016 | 0.7551 | 0.7274 | 0.9290 |
| 0.0077 | 173.0 | 11245 | 0.5343 | 0.7190 | 0.7652 | 0.7413 | 0.9317 |
| 0.0077 | 174.0 | 11310 | 0.5472 | 0.7231 | 0.7234 | 0.7233 | 0.9275 |
| 0.0077 | 175.0 | 11375 | 0.5275 | 0.7222 | 0.7601 | 0.7407 | 0.9307 |
| 0.0077 | 176.0 | 11440 | 0.5628 | 0.7044 | 0.7488 | 0.7259 | 0.9291 |
| 0.0064 | 177.0 | 11505 | 0.5391 | 0.7118 | 0.7122 | 0.7120 | 0.9256 |
| 0.0064 | 178.0 | 11570 | 0.5637 | 0.7121 | 0.7522 | 0.7316 | 0.9302 |
| 0.0064 | 179.0 | 11635 | 0.5401 | 0.7134 | 0.7513 | 0.7319 | 0.9280 |
| 0.0064 | 180.0 | 11700 | 0.6035 | 0.6963 | 0.7825 | 0.7369 | 0.9275 |
| 0.0064 | 181.0 | 11765 | 0.5969 | 0.6907 | 0.7592 | 0.7233 | 0.9276 |
| 0.0064 | 182.0 | 11830 | 0.5661 | 0.7196 | 0.7427 | 0.7309 | 0.9290 |
| 0.0064 | 183.0 | 11895 | 0.5654 | 0.7163 | 0.7401 | 0.7280 | 0.9291 |
| 0.0064 | 184.0 | 11960 | 0.5865 | 0.6989 | 0.7578 | 0.7271 | 0.9292 |
| 0.0047 | 185.0 | 12025 | 0.5389 | 0.6961 | 0.7473 | 0.7208 | 0.9271 |
| 0.0047 | 186.0 | 12090 | 0.5643 | 0.7093 | 0.7535 | 0.7307 | 0.9284 |
| 0.0047 | 187.0 | 12155 | 0.5704 | 0.7024 | 0.7531 | 0.7269 | 0.9282 |
| 0.0047 | 188.0 | 12220 | 0.5567 | 0.7035 | 0.7302 | 0.7166 | 0.9263 |
| 0.0047 | 189.0 | 12285 | 0.6077 | 0.7021 | 0.7311 | 0.7163 | 0.9241 |
| 0.0047 | 190.0 | 12350 | 0.5530 | 0.7082 | 0.7551 | 0.7309 | 0.9303 |
| 0.0047 | 191.0 | 12415 | 0.5351 | 0.7114 | 0.7630 | 0.7363 | 0.9296 |
| 0.0047 | 192.0 | 12480 | 0.5314 | 0.7247 | 0.7376 | 0.7311 | 0.9297 |
| 0.0065 | 193.0 | 12545 | 0.5895 | 0.7111 | 0.7409 | 0.7257 | 0.9263 |
| 0.0065 | 194.0 | 12610 | 0.5750 | 0.7112 | 0.7560 | 0.7329 | 0.9280 |
| 0.0065 | 195.0 | 12675 | 0.5497 | 0.7103 | 0.7569 | 0.7329 | 0.9295 |
| 0.0065 | 196.0 | 12740 | 0.5754 | 0.7124 | 0.7446 | 0.7282 | 0.9285 |
| 0.0065 | 197.0 | 12805 | 0.5606 | 0.7148 | 0.7634 | 0.7383 | 0.9302 |
| 0.0065 | 198.0 | 12870 | 0.5837 | 0.7187 | 0.7376 | 0.7280 | 0.9275 |
| 0.0065 | 199.0 | 12935 | 0.5944 | 0.7186 | 0.7371 | 0.7277 | 0.9277 |
| 0.0045 | 200.0 | 13000 | 0.5789 | 0.7166 | 0.7477 | 0.7318 | 0.9296 |
| 0.0045 | 201.0 | 13065 | 0.5776 | 0.6905 | 0.7373 | 0.7131 | 0.9240 |
| 0.0045 | 202.0 | 13130 | 0.6194 | 0.6920 | 0.7434 | 0.7168 | 0.9255 |
| 0.0045 | 203.0 | 13195 | 0.5758 | 0.7099 | 0.7365 | 0.7230 | 0.9280 |
| 0.0045 | 204.0 | 13260 | 0.5436 | 0.7151 | 0.7434 | 0.7289 | 0.9293 |
| 0.0045 | 205.0 | 13325 | 0.5624 | 0.7250 | 0.7481 | 0.7363 | 0.9300 |
| 0.0045 | 206.0 | 13390 | 0.5567 | 0.7097 | 0.7446 | 0.7268 | 0.9285 |
| 0.0045 | 207.0 | 13455 | 0.5513 | 0.7142 | 0.7138 | 0.7140 | 0.9249 |
| 0.0053 | 208.0 | 13520 | 0.5795 | 0.7057 | 0.7574 | 0.7307 | 0.9279 |
| 0.0053 | 209.0 | 13585 | 0.5445 | 0.7009 | 0.7526 | 0.7258 | 0.9275 |
| 0.0053 | 210.0 | 13650 | 0.5578 | 0.7246 | 0.7450 | 0.7347 | 0.9301 |
| 0.0053 | 211.0 | 13715 | 0.5437 | 0.7115 | 0.7688 | 0.7390 | 0.9312 |
| 0.0053 | 212.0 | 13780 | 0.4980 | 0.7292 | 0.7526 | 0.7407 | 0.9317 |
| 0.0053 | 213.0 | 13845 | 0.5782 | 0.7031 | 0.7594 | 0.7302 | 0.9292 |
| 0.0053 | 214.0 | 13910 | 0.5870 | 0.7130 | 0.7574 | 0.7345 | 0.9287 |
| 0.0053 | 215.0 | 13975 | 0.5367 | 0.7171 | 0.7490 | 0.7327 | 0.9283 |
| 0.006 | 216.0 | 14040 | 0.5845 | 0.7187 | 0.7583 | 0.7380 | 0.9278 |
| 0.006 | 217.0 | 14105 | 0.5766 | 0.7086 | 0.7646 | 0.7356 | 0.9272 |
| 0.006 | 218.0 | 14170 | 0.5593 | 0.7261 | 0.7614 | 0.7433 | 0.9306 |
| 0.006 | 219.0 | 14235 | 0.5373 | 0.7232 | 0.7598 | 0.7410 | 0.9313 |
| 0.006 | 220.0 | 14300 | 0.6219 | 0.7174 | 0.7450 | 0.7309 | 0.9275 |
| 0.006 | 221.0 | 14365 | 0.5916 | 0.7031 | 0.7634 | 0.7320 | 0.9286 |
| 0.006 | 222.0 | 14430 | 0.5404 | 0.7041 | 0.7756 | 0.7381 | 0.9290 |
| 0.006 | 223.0 | 14495 | 0.5963 | 0.7041 | 0.7490 | 0.7258 | 0.9267 |
| 0.0046 | 224.0 | 14560 | 0.5674 | 0.7109 | 0.7724 | 0.7404 | 0.9307 |
| 0.0046 | 225.0 | 14625 | 0.5854 | 0.7106 | 0.7769 | 0.7423 | 0.9288 |
| 0.0046 | 226.0 | 14690 | 0.5641 | 0.7053 | 0.7563 | 0.7299 | 0.9283 |
| 0.0046 | 227.0 | 14755 | 0.5753 | 0.7054 | 0.7616 | 0.7324 | 0.9285 |
| 0.0046 | 228.0 | 14820 | 0.5565 | 0.6980 | 0.7475 | 0.7219 | 0.9266 |
| 0.0046 | 229.0 | 14885 | 0.5419 | 0.6950 | 0.7816 | 0.7357 | 0.9286 |
| 0.0046 | 230.0 | 14950 | 0.5619 | 0.6926 | 0.7596 | 0.7246 | 0.9285 |
| 0.0053 | 231.0 | 15015 | 0.5680 | 0.7166 | 0.7400 | 0.7281 | 0.9280 |
| 0.0053 | 232.0 | 15080 | 0.5294 | 0.7106 | 0.7623 | 0.7355 | 0.9301 |
| 0.0053 | 233.0 | 15145 | 0.5938 | 0.7017 | 0.7493 | 0.7247 | 0.9259 |
| 0.0053 | 234.0 | 15210 | 0.5728 | 0.7157 | 0.7637 | 0.7389 | 0.9309 |
| 0.0053 | 235.0 | 15275 | 0.5562 | 0.7209 | 0.7526 | 0.7364 | 0.9298 |
| 0.0053 | 236.0 | 15340 | 0.5739 | 0.7295 | 0.7544 | 0.7417 | 0.9309 |
| 0.0053 | 237.0 | 15405 | 0.5848 | 0.7185 | 0.7760 | 0.7461 | 0.9320 |
| 0.0053 | 238.0 | 15470 | 0.5844 | 0.7015 | 0.7751 | 0.7365 | 0.9305 |
| 0.0037 | 239.0 | 15535 | 0.5699 | 0.7195 | 0.7499 | 0.7344 | 0.9307 |
| 0.0037 | 240.0 | 15600 | 0.5634 | 0.7266 | 0.7738 | 0.7495 | 0.9324 |
| 0.0037 | 241.0 | 15665 | 0.5915 | 0.7164 | 0.7655 | 0.7401 | 0.9291 |
| 0.0037 | 242.0 | 15730 | 0.5554 | 0.7061 | 0.7819 | 0.7421 | 0.9285 |
| 0.0037 | 243.0 | 15795 | 0.5476 | 0.7088 | 0.7677 | 0.7371 | 0.9299 |
| 0.0037 | 244.0 | 15860 | 0.6185 | 0.7380 | 0.7461 | 0.7420 | 0.9299 |
| 0.0037 | 245.0 | 15925 | 0.5879 | 0.7177 | 0.7645 | 0.7403 | 0.9313 |
| 0.0037 | 246.0 | 15990 | 0.6360 | 0.7104 | 0.7682 | 0.7382 | 0.9294 |
| 0.0033 | 247.0 | 16055 | 0.6197 | 0.7416 | 0.7380 | 0.7398 | 0.9291 |
| 0.0033 | 248.0 | 16120 | 0.5718 | 0.7145 | 0.7691 | 0.7408 | 0.9305 |
| 0.0033 | 249.0 | 16185 | 0.6096 | 0.7025 | 0.7398 | 0.7206 | 0.9266 |
| 0.0033 | 250.0 | 16250 | 0.5466 | 0.7110 | 0.7574 | 0.7335 | 0.9295 |
| 0.0033 | 251.0 | 16315 | 0.5995 | 0.7127 | 0.7670 | 0.7388 | 0.9293 |
| 0.0033 | 252.0 | 16380 | 0.5725 | 0.6981 | 0.7670 | 0.7309 | 0.9290 |
| 0.0033 | 253.0 | 16445 | 0.5625 | 0.7049 | 0.7668 | 0.7345 | 0.9297 |
| 0.0045 | 254.0 | 16510 | 0.5688 | 0.7178 | 0.7603 | 0.7384 | 0.9298 |
| 0.0045 | 255.0 | 16575 | 0.6108 | 0.7047 | 0.7333 | 0.7187 | 0.9256 |
| 0.0045 | 256.0 | 16640 | 0.5422 | 0.7093 | 0.7594 | 0.7335 | 0.9305 |
| 0.0045 | 257.0 | 16705 | 0.5737 | 0.7103 | 0.7439 | 0.7267 | 0.9289 |
| 0.0045 | 258.0 | 16770 | 0.5238 | 0.7058 | 0.7652 | 0.7343 | 0.9293 |
| 0.0045 | 259.0 | 16835 | 0.5794 | 0.7154 | 0.7677 | 0.7406 | 0.9302 |
| 0.0045 | 260.0 | 16900 | 0.6207 | 0.7135 | 0.7441 | 0.7285 | 0.9292 |
| 0.0045 | 261.0 | 16965 | 0.5672 | 0.7184 | 0.7490 | 0.7334 | 0.9287 |
| 0.0035 | 262.0 | 17030 | 0.5897 | 0.7187 | 0.7452 | 0.7317 | 0.9284 |
| 0.0035 | 263.0 | 17095 | 0.5981 | 0.7061 | 0.7744 | 0.7386 | 0.9299 |
| 0.0035 | 264.0 | 17160 | 0.6152 | 0.7214 | 0.7443 | 0.7327 | 0.9281 |
| 0.0035 | 265.0 | 17225 | 0.6185 | 0.7265 | 0.7527 | 0.7394 | 0.9305 |
| 0.0035 | 266.0 | 17290 | 0.6130 | 0.7165 | 0.7720 | 0.7432 | 0.9306 |
| 0.0035 | 267.0 | 17355 | 0.6058 | 0.7218 | 0.7751 | 0.7475 | 0.9305 |
| 0.0035 | 268.0 | 17420 | 0.6159 | 0.7136 | 0.7722 | 0.7417 | 0.9289 |
| 0.0035 | 269.0 | 17485 | 0.6156 | 0.7176 | 0.7738 | 0.7446 | 0.9301 |
| 0.0023 | 270.0 | 17550 | 0.5868 | 0.7090 | 0.7805 | 0.7430 | 0.9287 |
| 0.0023 | 271.0 | 17615 | 0.5651 | 0.7243 | 0.7713 | 0.7471 | 0.9319 |
| 0.0023 | 272.0 | 17680 | 0.5550 | 0.6917 | 0.7798 | 0.7331 | 0.9287 |
| 0.0023 | 273.0 | 17745 | 0.5620 | 0.7180 | 0.7468 | 0.7321 | 0.9293 |
| 0.0023 | 274.0 | 17810 | 0.5746 | 0.7198 | 0.7470 | 0.7331 | 0.9295 |
| 0.0023 | 275.0 | 17875 | 0.5724 | 0.7136 | 0.7414 | 0.7273 | 0.9265 |
| 0.0023 | 276.0 | 17940 | 0.5924 | 0.7097 | 0.7664 | 0.7370 | 0.9297 |
| 0.0032 | 277.0 | 18005 | 0.6020 | 0.6970 | 0.7607 | 0.7275 | 0.9279 |
| 0.0032 | 278.0 | 18070 | 0.6417 | 0.6984 | 0.7774 | 0.7358 | 0.9288 |
| 0.0032 | 279.0 | 18135 | 0.6101 | 0.7192 | 0.7641 | 0.7409 | 0.9300 |
| 0.0032 | 280.0 | 18200 | 0.6199 | 0.7134 | 0.7639 | 0.7378 | 0.9296 |
| 0.0032 | 281.0 | 18265 | 0.6048 | 0.7177 | 0.7567 | 0.7367 | 0.9300 |
| 0.0032 | 282.0 | 18330 | 0.6090 | 0.7221 | 0.7594 | 0.7403 | 0.9305 |
| 0.0032 | 283.0 | 18395 | 0.5958 | 0.7060 | 0.7830 | 0.7425 | 0.9300 |
| 0.0032 | 284.0 | 18460 | 0.5897 | 0.7127 | 0.7578 | 0.7346 | 0.9299 |
| 0.0022 | 285.0 | 18525 | 0.5743 | 0.7113 | 0.7664 | 0.7379 | 0.9307 |
| 0.0022 | 286.0 | 18590 | 0.5393 | 0.7063 | 0.7549 | 0.7298 | 0.9289 |
| 0.0022 | 287.0 | 18655 | 0.5873 | 0.6930 | 0.7690 | 0.7290 | 0.9280 |
| 0.0022 | 288.0 | 18720 | 0.5900 | 0.7151 | 0.7720 | 0.7425 | 0.9311 |
| 0.0022 | 289.0 | 18785 | 0.5481 | 0.7109 | 0.7780 | 0.7429 | 0.9306 |
| 0.0022 | 290.0 | 18850 | 0.6294 | 0.7236 | 0.7257 | 0.7247 | 0.9263 |
| 0.0022 | 291.0 | 18915 | 0.6257 | 0.7241 | 0.7358 | 0.7299 | 0.9265 |
| 0.0022 | 292.0 | 18980 | 0.5894 | 0.7289 | 0.7596 | 0.7439 | 0.9302 |
| 0.0032 | 293.0 | 19045 | 0.6101 | 0.6999 | 0.7805 | 0.7380 | 0.9290 |
| 0.0032 | 294.0 | 19110 | 0.6545 | 0.6996 | 0.7527 | 0.7252 | 0.9263 |
| 0.0032 | 295.0 | 19175 | 0.6525 | 0.7044 | 0.7636 | 0.7328 | 0.9296 |
| 0.0032 | 296.0 | 19240 | 0.6624 | 0.7158 | 0.7538 | 0.7343 | 0.9291 |
| 0.0032 | 297.0 | 19305 | 0.6560 | 0.7118 | 0.7744 | 0.7418 | 0.9299 |
| 0.0032 | 298.0 | 19370 | 0.6621 | 0.7071 | 0.7740 | 0.7391 | 0.9289 |
| 0.0032 | 299.0 | 19435 | 0.6707 | 0.7201 | 0.7578 | 0.7384 | 0.9286 |
| 0.0024 | 300.0 | 19500 | 0.6077 | 0.7131 | 0.7691 | 0.7401 | 0.9299 |
| 0.0024 | 301.0 | 19565 | 0.6373 | 0.7086 | 0.7554 | 0.7313 | 0.9270 |
| 0.0024 | 302.0 | 19630 | 0.6672 | 0.7077 | 0.7704 | 0.7377 | 0.9277 |
| 0.0024 | 303.0 | 19695 | 0.6336 | 0.7306 | 0.7614 | 0.7457 | 0.9315 |
| 0.0024 | 304.0 | 19760 | 0.6163 | 0.7316 | 0.7556 | 0.7434 | 0.9302 |
| 0.0024 | 305.0 | 19825 | 0.6155 | 0.7186 | 0.7648 | 0.7410 | 0.9294 |
| 0.0024 | 306.0 | 19890 | 0.6122 | 0.7176 | 0.7558 | 0.7362 | 0.9298 |
| 0.0024 | 307.0 | 19955 | 0.5746 | 0.7126 | 0.7781 | 0.7439 | 0.9312 |
| 0.0024 | 308.0 | 20020 | 0.5839 | 0.7239 | 0.7610 | 0.7420 | 0.9313 |
| 0.0024 | 309.0 | 20085 | 0.6299 | 0.7256 | 0.7490 | 0.7371 | 0.9291 |
| 0.0024 | 310.0 | 20150 | 0.6116 | 0.7097 | 0.7848 | 0.7453 | 0.9305 |
| 0.0024 | 311.0 | 20215 | 0.5846 | 0.7006 | 0.7785 | 0.7375 | 0.9275 |
| 0.0024 | 312.0 | 20280 | 0.5604 | 0.7086 | 0.7664 | 0.7364 | 0.9293 |
| 0.0024 | 313.0 | 20345 | 0.6063 | 0.7273 | 0.7717 | 0.7488 | 0.9308 |
| 0.0024 | 314.0 | 20410 | 0.6301 | 0.7303 | 0.7610 | 0.7453 | 0.9303 |
| 0.0024 | 315.0 | 20475 | 0.6441 | 0.7093 | 0.7675 | 0.7372 | 0.9274 |
| 0.0018 | 316.0 | 20540 | 0.6651 | 0.7270 | 0.7477 | 0.7372 | 0.9281 |
| 0.0018 | 317.0 | 20605 | 0.6420 | 0.7165 | 0.7684 | 0.7416 | 0.9293 |
| 0.0018 | 318.0 | 20670 | 0.6189 | 0.7231 | 0.7601 | 0.7412 | 0.9302 |
| 0.0018 | 319.0 | 20735 | 0.6246 | 0.7250 | 0.7596 | 0.7419 | 0.9298 |
| 0.0018 | 320.0 | 20800 | 0.6362 | 0.7130 | 0.7614 | 0.7364 | 0.9292 |
| 0.0018 | 321.0 | 20865 | 0.6363 | 0.7176 | 0.7569 | 0.7367 | 0.9291 |
| 0.0018 | 322.0 | 20930 | 0.6377 | 0.7166 | 0.7690 | 0.7418 | 0.9303 |
| 0.0018 | 323.0 | 20995 | 0.6287 | 0.7295 | 0.7762 | 0.7521 | 0.9319 |
| 0.0017 | 324.0 | 21060 | 0.6248 | 0.7242 | 0.7693 | 0.7461 | 0.9307 |
| 0.0017 | 325.0 | 21125 | 0.6228 | 0.7268 | 0.7738 | 0.7496 | 0.9314 |
| 0.0017 | 326.0 | 21190 | 0.6211 | 0.7249 | 0.7830 | 0.7528 | 0.9320 |
| 0.0017 | 327.0 | 21255 | 0.6104 | 0.7078 | 0.7708 | 0.7379 | 0.9293 |
| 0.0017 | 328.0 | 21320 | 0.6353 | 0.7255 | 0.7515 | 0.7383 | 0.9294 |
| 0.0017 | 329.0 | 21385 | 0.6598 | 0.7069 | 0.7623 | 0.7336 | 0.9277 |
| 0.0017 | 330.0 | 21450 | 0.6299 | 0.7171 | 0.7565 | 0.7363 | 0.9285 |
| 0.0018 | 331.0 | 21515 | 0.6222 | 0.7198 | 0.7754 | 0.7466 | 0.9307 |
| 0.0018 | 332.0 | 21580 | 0.6139 | 0.7176 | 0.7587 | 0.7376 | 0.9299 |
| 0.0018 | 333.0 | 21645 | 0.5928 | 0.7195 | 0.7663 | 0.7421 | 0.9310 |
| 0.0018 | 334.0 | 21710 | 0.5740 | 0.7251 | 0.7553 | 0.7399 | 0.9309 |
| 0.0018 | 335.0 | 21775 | 0.5864 | 0.7298 | 0.7677 | 0.7483 | 0.9324 |
| 0.0018 | 336.0 | 21840 | 0.6373 | 0.7301 | 0.7347 | 0.7324 | 0.9278 |
| 0.0018 | 337.0 | 21905 | 0.6215 | 0.7212 | 0.7760 | 0.7476 | 0.9314 |
| 0.0018 | 338.0 | 21970 | 0.6450 | 0.7131 | 0.7672 | 0.7391 | 0.9284 |
| 0.0026 | 339.0 | 22035 | 0.6126 | 0.7306 | 0.7517 | 0.7410 | 0.9295 |
| 0.0026 | 340.0 | 22100 | 0.6029 | 0.7169 | 0.7645 | 0.7399 | 0.9296 |
| 0.0026 | 341.0 | 22165 | 0.6201 | 0.7274 | 0.7681 | 0.7472 | 0.9313 |
| 0.0026 | 342.0 | 22230 | 0.6373 | 0.7316 | 0.7293 | 0.7305 | 0.9277 |
| 0.0026 | 343.0 | 22295 | 0.6083 | 0.7213 | 0.7443 | 0.7326 | 0.9286 |
| 0.0026 | 344.0 | 22360 | 0.6253 | 0.7275 | 0.7538 | 0.7404 | 0.9295 |
| 0.0026 | 345.0 | 22425 | 0.6554 | 0.7042 | 0.7808 | 0.7406 | 0.9277 |
| 0.0026 | 346.0 | 22490 | 0.6120 | 0.7288 | 0.7306 | 0.7297 | 0.9280 |
| 0.0015 | 347.0 | 22555 | 0.6236 | 0.7144 | 0.7576 | 0.7354 | 0.9296 |
| 0.0015 | 348.0 | 22620 | 0.6360 | 0.7166 | 0.7643 | 0.7397 | 0.9307 |
| 0.0015 | 349.0 | 22685 | 0.6313 | 0.7173 | 0.7616 | 0.7388 | 0.9302 |
| 0.0015 | 350.0 | 22750 | 0.6344 | 0.7301 | 0.7479 | 0.7389 | 0.9299 |
| 0.0015 | 351.0 | 22815 | 0.6111 | 0.7113 | 0.7870 | 0.7472 | 0.9300 |
| 0.0015 | 352.0 | 22880 | 0.6286 | 0.7349 | 0.7482 | 0.7415 | 0.9306 |
| 0.0015 | 353.0 | 22945 | 0.6342 | 0.7258 | 0.7727 | 0.7485 | 0.9313 |
| 0.0008 | 354.0 | 23010 | 0.6729 | 0.7148 | 0.7789 | 0.7454 | 0.9297 |
| 0.0008 | 355.0 | 23075 | 0.6213 | 0.7179 | 0.7639 | 0.7402 | 0.9296 |
| 0.0008 | 356.0 | 23140 | 0.6395 | 0.7273 | 0.7556 | 0.7412 | 0.9296 |
| 0.0008 | 357.0 | 23205 | 0.6439 | 0.7243 | 0.7560 | 0.7398 | 0.9299 |
| 0.0008 | 358.0 | 23270 | 0.7104 | 0.7329 | 0.7383 | 0.7356 | 0.9283 |
| 0.0008 | 359.0 | 23335 | 0.7150 | 0.7094 | 0.7558 | 0.7319 | 0.9279 |
| 0.0008 | 360.0 | 23400 | 0.6514 | 0.7302 | 0.7412 | 0.7357 | 0.9297 |
| 0.0008 | 361.0 | 23465 | 0.6533 | 0.7300 | 0.7574 | 0.7434 | 0.9308 |
| 0.0013 | 362.0 | 23530 | 0.6617 | 0.7399 | 0.7490 | 0.7444 | 0.9312 |
| 0.0013 | 363.0 | 23595 | 0.6771 | 0.7237 | 0.7722 | 0.7472 | 0.9312 |
| 0.0013 | 364.0 | 23660 | 0.6824 | 0.7228 | 0.7664 | 0.7440 | 0.9311 |
| 0.0013 | 365.0 | 23725 | 0.6868 | 0.7274 | 0.7580 | 0.7424 | 0.9307 |
| 0.0013 | 366.0 | 23790 | 0.6828 | 0.7336 | 0.7542 | 0.7437 | 0.9305 |
| 0.0013 | 367.0 | 23855 | 0.6669 | 0.7201 | 0.7655 | 0.7421 | 0.9307 |
| 0.0013 | 368.0 | 23920 | 0.6729 | 0.7293 | 0.7646 | 0.7465 | 0.9307 |
| 0.0013 | 369.0 | 23985 | 0.6766 | 0.7285 | 0.7553 | 0.7416 | 0.9300 |
| 0.0006 | 370.0 | 24050 | 0.6301 | 0.7180 | 0.7814 | 0.7484 | 0.9312 |
| 0.0006 | 371.0 | 24115 | 0.6610 | 0.7274 | 0.7688 | 0.7475 | 0.9311 |
| 0.0006 | 372.0 | 24180 | 0.6835 | 0.7226 | 0.7675 | 0.7444 | 0.9315 |
| 0.0006 | 373.0 | 24245 | 0.6778 | 0.7346 | 0.7491 | 0.7418 | 0.9303 |
| 0.0006 | 374.0 | 24310 | 0.6544 | 0.7406 | 0.7599 | 0.7502 | 0.9317 |
| 0.0006 | 375.0 | 24375 | 0.6549 | 0.7214 | 0.7756 | 0.7475 | 0.9314 |
| 0.0006 | 376.0 | 24440 | 0.6871 | 0.7214 | 0.7686 | 0.7443 | 0.9306 |
| 0.0009 | 377.0 | 24505 | 0.6714 | 0.7215 | 0.7744 | 0.7470 | 0.9316 |
| 0.0009 | 378.0 | 24570 | 0.6751 | 0.7340 | 0.7527 | 0.7432 | 0.9298 |
| 0.0009 | 379.0 | 24635 | 0.6599 | 0.7198 | 0.7722 | 0.7451 | 0.9315 |
| 0.0009 | 380.0 | 24700 | 0.6418 | 0.7363 | 0.7479 | 0.7421 | 0.9310 |
| 0.0009 | 381.0 | 24765 | 0.6480 | 0.7159 | 0.7765 | 0.7450 | 0.9324 |
| 0.0009 | 382.0 | 24830 | 0.6738 | 0.7266 | 0.7778 | 0.7513 | 0.9324 |
| 0.0009 | 383.0 | 24895 | 0.6758 | 0.7331 | 0.7655 | 0.7489 | 0.9325 |
| 0.0009 | 384.0 | 24960 | 0.7180 | 0.7092 | 0.7763 | 0.7412 | 0.9295 |
| 0.0009 | 385.0 | 25025 | 0.6652 | 0.7335 | 0.7553 | 0.7442 | 0.9315 |
| 0.0009 | 386.0 | 25090 | 0.6265 | 0.7314 | 0.7664 | 0.7485 | 0.9329 |
| 0.0009 | 387.0 | 25155 | 0.6247 | 0.7284 | 0.7715 | 0.7493 | 0.9314 |
| 0.0009 | 388.0 | 25220 | 0.6193 | 0.7275 | 0.7760 | 0.7510 | 0.9323 |
| 0.0009 | 389.0 | 25285 | 0.6515 | 0.7346 | 0.7556 | 0.7450 | 0.9305 |
| 0.0009 | 390.0 | 25350 | 0.6237 | 0.7179 | 0.7599 | 0.7383 | 0.9304 |
| 0.0009 | 391.0 | 25415 | 0.6286 | 0.7246 | 0.7731 | 0.7480 | 0.9314 |
| 0.0009 | 392.0 | 25480 | 0.6504 | 0.7295 | 0.7578 | 0.7434 | 0.9316 |
| 0.001 | 393.0 | 25545 | 0.6569 | 0.7342 | 0.7508 | 0.7424 | 0.9312 |
| 0.001 | 394.0 | 25610 | 0.6598 | 0.7361 | 0.7736 | 0.7544 | 0.9318 |
| 0.001 | 395.0 | 25675 | 0.6699 | 0.7317 | 0.7702 | 0.7505 | 0.9322 |
| 0.001 | 396.0 | 25740 | 0.6536 | 0.7315 | 0.7500 | 0.7406 | 0.9306 |
| 0.001 | 397.0 | 25805 | 0.6638 | 0.7293 | 0.7679 | 0.7481 | 0.9312 |
| 0.001 | 398.0 | 25870 | 0.6478 | 0.7264 | 0.7758 | 0.7503 | 0.9326 |
| 0.001 | 399.0 | 25935 | 0.6592 | 0.7234 | 0.7749 | 0.7483 | 0.9326 |
| 0.0005 | 400.0 | 26000 | 0.6534 | 0.7298 | 0.7610 | 0.7451 | 0.9317 |
| 0.0005 | 401.0 | 26065 | 0.6571 | 0.7356 | 0.7605 | 0.7478 | 0.9321 |
| 0.0005 | 402.0 | 26130 | 0.6599 | 0.7333 | 0.7803 | 0.7561 | 0.9328 |
| 0.0005 | 403.0 | 26195 | 0.6516 | 0.7210 | 0.7684 | 0.7440 | 0.9313 |
| 0.0005 | 404.0 | 26260 | 0.6303 | 0.7275 | 0.7538 | 0.7404 | 0.9307 |
| 0.0005 | 405.0 | 26325 | 0.6301 | 0.7407 | 0.7681 | 0.7541 | 0.9327 |
| 0.0005 | 406.0 | 26390 | 0.6513 | 0.7296 | 0.7713 | 0.7499 | 0.9325 |
| 0.0005 | 407.0 | 26455 | 0.6434 | 0.7428 | 0.7618 | 0.7521 | 0.9326 |
| 0.0006 | 408.0 | 26520 | 0.6576 | 0.7271 | 0.7812 | 0.7532 | 0.9329 |
| 0.0006 | 409.0 | 26585 | 0.6573 | 0.7356 | 0.7691 | 0.7520 | 0.9332 |
| 0.0006 | 410.0 | 26650 | 0.6646 | 0.7358 | 0.7718 | 0.7534 | 0.9319 |
| 0.0006 | 411.0 | 26715 | 0.6954 | 0.7327 | 0.7652 | 0.7486 | 0.9322 |
| 0.0006 | 412.0 | 26780 | 0.7009 | 0.7203 | 0.7742 | 0.7463 | 0.9313 |
| 0.0006 | 413.0 | 26845 | 0.6730 | 0.7385 | 0.7781 | 0.7578 | 0.9324 |
| 0.0006 | 414.0 | 26910 | 0.6644 | 0.7397 | 0.7733 | 0.7561 | 0.9330 |
| 0.0006 | 415.0 | 26975 | 0.6640 | 0.7343 | 0.7753 | 0.7542 | 0.9324 |
| 0.0005 | 416.0 | 27040 | 0.6604 | 0.7343 | 0.7731 | 0.7532 | 0.9328 |
| 0.0005 | 417.0 | 27105 | 0.6639 | 0.7406 | 0.7711 | 0.7555 | 0.9326 |
| 0.0005 | 418.0 | 27170 | 0.6614 | 0.7220 | 0.7848 | 0.7521 | 0.9319 |
| 0.0005 | 419.0 | 27235 | 0.6744 | 0.7340 | 0.7699 | 0.7515 | 0.9316 |
| 0.0005 | 420.0 | 27300 | 0.6854 | 0.7350 | 0.7648 | 0.7496 | 0.9316 |
| 0.0005 | 421.0 | 27365 | 0.6882 | 0.7395 | 0.7699 | 0.7544 | 0.9330 |
| 0.0005 | 422.0 | 27430 | 0.7170 | 0.7378 | 0.7567 | 0.7472 | 0.9312 |
| 0.0005 | 423.0 | 27495 | 0.7041 | 0.7355 | 0.7511 | 0.7432 | 0.9313 |
| 0.0003 | 424.0 | 27560 | 0.7173 | 0.7295 | 0.7562 | 0.7426 | 0.9300 |
| 0.0003 | 425.0 | 27625 | 0.7055 | 0.7289 | 0.7659 | 0.7469 | 0.9311 |
| 0.0003 | 426.0 | 27690 | 0.7082 | 0.7272 | 0.7670 | 0.7465 | 0.9310 |
| 0.0003 | 427.0 | 27755 | 0.7026 | 0.7323 | 0.7718 | 0.7515 | 0.9321 |
| 0.0003 | 428.0 | 27820 | 0.7085 | 0.7454 | 0.7517 | 0.7485 | 0.9314 |
| 0.0003 | 429.0 | 27885 | 0.7117 | 0.7335 | 0.7563 | 0.7447 | 0.9321 |
| 0.0003 | 430.0 | 27950 | 0.7928 | 0.7089 | 0.7661 | 0.7364 | 0.9268 |
| 0.0003 | 431.0 | 28015 | 0.6940 | 0.7363 | 0.7583 | 0.7472 | 0.9314 |
| 0.0003 | 432.0 | 28080 | 0.7003 | 0.7332 | 0.7729 | 0.7525 | 0.9324 |
| 0.0003 | 433.0 | 28145 | 0.6943 | 0.7286 | 0.7677 | 0.7476 | 0.9317 |
| 0.0003 | 434.0 | 28210 | 0.7012 | 0.7317 | 0.7587 | 0.7449 | 0.9312 |
| 0.0003 | 435.0 | 28275 | 0.7172 | 0.7241 | 0.7578 | 0.7406 | 0.9302 |
| 0.0003 | 436.0 | 28340 | 0.7066 | 0.7357 | 0.7594 | 0.7474 | 0.9314 |
| 0.0003 | 437.0 | 28405 | 0.7081 | 0.7315 | 0.7745 | 0.7524 | 0.9325 |
| 0.0003 | 438.0 | 28470 | 0.7408 | 0.7442 | 0.7418 | 0.7430 | 0.9306 |
| 0.0002 | 439.0 | 28535 | 0.7146 | 0.7412 | 0.7727 | 0.7567 | 0.9322 |
| 0.0002 | 440.0 | 28600 | 0.7072 | 0.7383 | 0.7641 | 0.7510 | 0.9325 |
| 0.0002 | 441.0 | 28665 | 0.7075 | 0.7384 | 0.7621 | 0.7501 | 0.9319 |
| 0.0002 | 442.0 | 28730 | 0.7106 | 0.7382 | 0.7632 | 0.7505 | 0.9319 |
| 0.0002 | 443.0 | 28795 | 0.7048 | 0.7427 | 0.7556 | 0.7491 | 0.9317 |
| 0.0002 | 444.0 | 28860 | 0.7119 | 0.7463 | 0.7536 | 0.7499 | 0.9315 |
| 0.0002 | 445.0 | 28925 | 0.7219 | 0.7422 | 0.7563 | 0.7492 | 0.9313 |
| 0.0002 | 446.0 | 28990 | 0.7071 | 0.7283 | 0.7756 | 0.7512 | 0.9320 |
| 0.0003 | 447.0 | 29055 | 0.7017 | 0.7378 | 0.7704 | 0.7538 | 0.9322 |
| 0.0003 | 448.0 | 29120 | 0.7036 | 0.7414 | 0.7713 | 0.7560 | 0.9322 |
| 0.0003 | 449.0 | 29185 | 0.7080 | 0.7408 | 0.7618 | 0.7511 | 0.9321 |
| 0.0003 | 450.0 | 29250 | 0.7037 | 0.7303 | 0.7679 | 0.7486 | 0.9318 |
| 0.0003 | 451.0 | 29315 | 0.7024 | 0.7412 | 0.7590 | 0.7500 | 0.9317 |
| 0.0003 | 452.0 | 29380 | 0.7050 | 0.7407 | 0.7641 | 0.7522 | 0.9320 |
| 0.0003 | 453.0 | 29445 | 0.7087 | 0.7396 | 0.7677 | 0.7534 | 0.9321 |
| 0.0002 | 454.0 | 29510 | 0.7013 | 0.7423 | 0.7738 | 0.7577 | 0.9326 |
| 0.0002 | 455.0 | 29575 | 0.7111 | 0.7375 | 0.7762 | 0.7563 | 0.9325 |
| 0.0002 | 456.0 | 29640 | 0.7102 | 0.7428 | 0.7661 | 0.7543 | 0.9320 |
| 0.0002 | 457.0 | 29705 | 0.6937 | 0.7400 | 0.7554 | 0.7476 | 0.9320 |
| 0.0002 | 458.0 | 29770 | 0.7076 | 0.7287 | 0.7526 | 0.7404 | 0.9313 |
| 0.0002 | 459.0 | 29835 | 0.6911 | 0.7433 | 0.7727 | 0.7577 | 0.9333 |
| 0.0002 | 460.0 | 29900 | 0.6945 | 0.7443 | 0.7769 | 0.7602 | 0.9331 |
| 0.0002 | 461.0 | 29965 | 0.7062 | 0.7398 | 0.7733 | 0.7562 | 0.9326 |
| 0.0003 | 462.0 | 30030 | 0.7095 | 0.7351 | 0.7727 | 0.7535 | 0.9317 |
| 0.0003 | 463.0 | 30095 | 0.7056 | 0.7340 | 0.7805 | 0.7565 | 0.9328 |
| 0.0003 | 464.0 | 30160 | 0.7244 | 0.7289 | 0.7718 | 0.7498 | 0.9321 |
| 0.0003 | 465.0 | 30225 | 0.7179 | 0.7260 | 0.7706 | 0.7476 | 0.9317 |
| 0.0003 | 466.0 | 30290 | 0.7046 | 0.7253 | 0.7771 | 0.7503 | 0.9319 |
| 0.0003 | 467.0 | 30355 | 0.6995 | 0.7333 | 0.7713 | 0.7518 | 0.9321 |
| 0.0003 | 468.0 | 30420 | 0.7040 | 0.7394 | 0.7681 | 0.7535 | 0.9322 |
| 0.0003 | 469.0 | 30485 | 0.7173 | 0.7322 | 0.7700 | 0.7506 | 0.9323 |
| 0.0003 | 470.0 | 30550 | 0.7105 | 0.7360 | 0.7726 | 0.7538 | 0.9328 |
| 0.0003 | 471.0 | 30615 | 0.7117 | 0.7377 | 0.7718 | 0.7544 | 0.9328 |
| 0.0003 | 472.0 | 30680 | 0.7099 | 0.7395 | 0.7726 | 0.7557 | 0.9328 |
| 0.0003 | 473.0 | 30745 | 0.7089 | 0.7383 | 0.7717 | 0.7546 | 0.9328 |
| 0.0003 | 474.0 | 30810 | 0.7080 | 0.7427 | 0.7729 | 0.7575 | 0.9328 |
| 0.0003 | 475.0 | 30875 | 0.7092 | 0.7473 | 0.7646 | 0.7559 | 0.9329 |
| 0.0003 | 476.0 | 30940 | 0.7094 | 0.7398 | 0.7574 | 0.7485 | 0.9317 |
| 0.0002 | 477.0 | 31005 | 0.7067 | 0.7396 | 0.7648 | 0.7520 | 0.9323 |
| 0.0002 | 478.0 | 31070 | 0.7070 | 0.7405 | 0.7699 | 0.7549 | 0.9324 |
| 0.0002 | 479.0 | 31135 | 0.7070 | 0.7403 | 0.7715 | 0.7556 | 0.9325 |
| 0.0002 | 480.0 | 31200 | 0.7010 | 0.7356 | 0.7776 | 0.7560 | 0.9326 |
| 0.0002 | 481.0 | 31265 | 0.6975 | 0.7375 | 0.7810 | 0.7586 | 0.9327 |
| 0.0002 | 482.0 | 31330 | 0.7017 | 0.7412 | 0.7763 | 0.7584 | 0.9331 |
| 0.0002 | 483.0 | 31395 | 0.6996 | 0.7412 | 0.7740 | 0.7572 | 0.9330 |
| 0.0002 | 484.0 | 31460 | 0.7002 | 0.7405 | 0.7733 | 0.7565 | 0.9329 |
| 0.0001 | 485.0 | 31525 | 0.7009 | 0.7406 | 0.7736 | 0.7567 | 0.9329 |
| 0.0001 | 486.0 | 31590 | 0.7004 | 0.7404 | 0.7751 | 0.7573 | 0.9328 |
| 0.0001 | 487.0 | 31655 | 0.6998 | 0.7422 | 0.7747 | 0.7581 | 0.9328 |
| 0.0001 | 488.0 | 31720 | 0.7025 | 0.7416 | 0.7727 | 0.7569 | 0.9328 |
| 0.0001 | 489.0 | 31785 | 0.7040 | 0.7425 | 0.7720 | 0.7570 | 0.9328 |
| 0.0001 | 490.0 | 31850 | 0.7047 | 0.7415 | 0.7718 | 0.7564 | 0.9329 |
| 0.0001 | 491.0 | 31915 | 0.7044 | 0.7436 | 0.7690 | 0.7561 | 0.9330 |
| 0.0001 | 492.0 | 31980 | 0.7060 | 0.7412 | 0.7673 | 0.7540 | 0.9326 |
| 0.0001 | 493.0 | 32045 | 0.7053 | 0.7403 | 0.7681 | 0.7539 | 0.9325 |
| 0.0001 | 494.0 | 32110 | 0.7039 | 0.7419 | 0.7717 | 0.7565 | 0.9328 |
| 0.0001 | 495.0 | 32175 | 0.7031 | 0.7414 | 0.7727 | 0.7567 | 0.9329 |
| 0.0001 | 496.0 | 32240 | 0.7029 | 0.7408 | 0.7724 | 0.7562 | 0.9330 |
| 0.0001 | 497.0 | 32305 | 0.7032 | 0.7406 | 0.7722 | 0.7561 | 0.9330 |
| 0.0001 | 498.0 | 32370 | 0.7033 | 0.7422 | 0.7713 | 0.7564 | 0.9330 |
| 0.0001 | 499.0 | 32435 | 0.7034 | 0.7418 | 0.7708 | 0.7560 | 0.9329 |
| 0.0001 | 500.0 | 32500 | 0.7036 | 0.7424 | 0.7713 | 0.7566 | 0.9330 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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] |
noobkittu/RealisticVision
|
noobkittu
| 2023-12-15T05:22:34Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T05:22:34Z |
---
license: mit
---
|
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] |
LilYoda/lopezRvB
|
LilYoda
| 2023-12-15T06:41:18Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T05:23:47Z |
---
license: openrail
---
|
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BreadUniverse/dialoGPT-hair-recommender
|
BreadUniverse
| 2023-12-15T05:41:05Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-15T05:24:15Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_001_fold1
|
hkivancoral
| 2023-12-15T06:20:15Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T05:25:15Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8714524207011686
---
<!-- 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. -->
# smids_5x_deit_tiny_sgd_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3293
- Accuracy: 0.8715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7747 | 1.0 | 376 | 0.8081 | 0.6327 |
| 0.5327 | 2.0 | 752 | 0.5949 | 0.7462 |
| 0.4332 | 3.0 | 1128 | 0.5030 | 0.7846 |
| 0.4359 | 4.0 | 1504 | 0.4457 | 0.8097 |
| 0.3937 | 5.0 | 1880 | 0.4107 | 0.8164 |
| 0.3325 | 6.0 | 2256 | 0.3873 | 0.8297 |
| 0.2877 | 7.0 | 2632 | 0.3645 | 0.8347 |
| 0.2962 | 8.0 | 3008 | 0.3585 | 0.8397 |
| 0.3002 | 9.0 | 3384 | 0.3450 | 0.8414 |
| 0.2749 | 10.0 | 3760 | 0.3357 | 0.8514 |
| 0.2826 | 11.0 | 4136 | 0.3303 | 0.8614 |
| 0.2607 | 12.0 | 4512 | 0.3246 | 0.8664 |
| 0.2479 | 13.0 | 4888 | 0.3195 | 0.8731 |
| 0.209 | 14.0 | 5264 | 0.3192 | 0.8698 |
| 0.2492 | 15.0 | 5640 | 0.3190 | 0.8631 |
| 0.2421 | 16.0 | 6016 | 0.3201 | 0.8664 |
| 0.2313 | 17.0 | 6392 | 0.3123 | 0.8731 |
| 0.2635 | 18.0 | 6768 | 0.3189 | 0.8715 |
| 0.22 | 19.0 | 7144 | 0.3169 | 0.8698 |
| 0.1933 | 20.0 | 7520 | 0.3154 | 0.8715 |
| 0.1972 | 21.0 | 7896 | 0.3125 | 0.8748 |
| 0.2184 | 22.0 | 8272 | 0.3238 | 0.8681 |
| 0.2395 | 23.0 | 8648 | 0.3208 | 0.8715 |
| 0.2148 | 24.0 | 9024 | 0.3152 | 0.8681 |
| 0.2046 | 25.0 | 9400 | 0.3215 | 0.8698 |
| 0.2137 | 26.0 | 9776 | 0.3154 | 0.8681 |
| 0.1523 | 27.0 | 10152 | 0.3167 | 0.8731 |
| 0.1766 | 28.0 | 10528 | 0.3160 | 0.8715 |
| 0.1896 | 29.0 | 10904 | 0.3190 | 0.8715 |
| 0.157 | 30.0 | 11280 | 0.3195 | 0.8698 |
| 0.1522 | 31.0 | 11656 | 0.3183 | 0.8731 |
| 0.1888 | 32.0 | 12032 | 0.3211 | 0.8715 |
| 0.1615 | 33.0 | 12408 | 0.3233 | 0.8681 |
| 0.1503 | 34.0 | 12784 | 0.3209 | 0.8731 |
| 0.1481 | 35.0 | 13160 | 0.3244 | 0.8698 |
| 0.1788 | 36.0 | 13536 | 0.3242 | 0.8681 |
| 0.1497 | 37.0 | 13912 | 0.3239 | 0.8748 |
| 0.1343 | 38.0 | 14288 | 0.3226 | 0.8748 |
| 0.1659 | 39.0 | 14664 | 0.3268 | 0.8748 |
| 0.1781 | 40.0 | 15040 | 0.3250 | 0.8698 |
| 0.1644 | 41.0 | 15416 | 0.3283 | 0.8731 |
| 0.1354 | 42.0 | 15792 | 0.3269 | 0.8731 |
| 0.1533 | 43.0 | 16168 | 0.3272 | 0.8731 |
| 0.1541 | 44.0 | 16544 | 0.3272 | 0.8748 |
| 0.2043 | 45.0 | 16920 | 0.3294 | 0.8731 |
| 0.2146 | 46.0 | 17296 | 0.3299 | 0.8731 |
| 0.154 | 47.0 | 17672 | 0.3285 | 0.8715 |
| 0.1593 | 48.0 | 18048 | 0.3296 | 0.8731 |
| 0.1388 | 49.0 | 18424 | 0.3295 | 0.8731 |
| 0.1123 | 50.0 | 18800 | 0.3293 | 0.8715 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
danielmalencar/sobreDaniel
|
danielmalencar
| 2023-12-15T05:56:23Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-15T05:27:13Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1
|
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] |
sshh12/Mistral-7B-LoRA-Multi-VisionCLIPPool-LLAVA
|
sshh12
| 2023-12-15T05:38:41Z | 0 | 1 |
peft
|
[
"peft",
"finetuned",
"multimodal",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T05:36:30Z |
---
license: apache-2.0
library_name: peft
tags:
- finetuned
- multimodal
base_model: mistralai/Mistral-7B-Instruct-v0.1
dataset: sshh12/llava-gpt-multi-image-and-llava-finetune-merged
inference: false
---
These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications.
### Modalities
* CLIPVisionModality (use `<image>` in text and provide `images`, encoded as 10 tokens)
### Usage
GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server)
### Dataset
sshh12/llava-gpt-multi-image-and-llava-finetune-merged (744610 examples)
```
{'images': ['/data/llava_finetune_data/images/coco/train2017/train2017/000000499538.jpg'], 'messages': [{'content': '<image>\nWhat is the name of the book?\nAnswer the question using a single word or phrase.', 'role': 'user'}, {'content': 'World changing', 'role': 'assistant'}, {'content': 'What color is the bird?', 'role': 'user'}, {'content': 'Red', 'role': 'assistant'}, {'content': 'What type of bird is this?', 'role': 'user'}, {'content': 'Robin', 'role': 'assistant'}], 'id': '000000499538'}
```
### Training Device(s)
```
name, pci.bus_id, vbios_version
NVIDIA RTX A6000, 00000000:02:00.0, 94.02.5C.00.02
```
### Model
```
MistralLMMForCausalLM.model =
PeftModelForCausalLM(
(base_model): LoraModel(
(model): MistralLMMForCausalLM(
(model): MistralLMMModel(
(embed_tokens): Embedding(32000, 4096)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=14336, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=14336, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
(vision_clip_lmm_projector): _MLPVectorProjector(
(mlps): ModuleList(
(0-9): 10 x Sequential(
(0): Linear(in_features=1024, out_features=4096, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=4096, out_features=4096, bias=True)
)
)
)
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
)
)
```
### Framework versions
- PEFT 0.7.0
|
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vtiyyal1/covid-mistral-dpo-gptq
|
vtiyyal1
| 2023-12-15T09:00:44Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T05:37:12Z |
Entry not found
|
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Kapil62/sdf
|
Kapil62
| 2023-12-15T05:38:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:38:34Z |
Entry not found
|
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Bhandari007/distil-whisper-large-v2-ne-slr-without-psl
|
Bhandari007
| 2023-12-15T05:38:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:38:40Z |
Entry not found
|
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sobiazahid/image-classifier
|
sobiazahid
| 2023-12-15T05:41:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:41:13Z |
Entry not found
|
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Xrunner/test
|
Xrunner
| 2023-12-15T05:41:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:41:18Z |
Entry not found
|
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badra21/reinforce-cartpole0
|
badra21
| 2023-12-16T19:39:01Z | 0 | 0 | null |
[
"reinforcement-learning",
"region:us"
] |
reinforcement-learning
| 2023-12-15T05:42:16Z |
---
pipeline_tag: reinforcement-learning
---
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
model-index:
- name: reinforce-cartpole0
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
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-3e-4
|
kanishka
| 2023-12-15T19:02:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_without_prototypical",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T05:42:38Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_without_prototypical
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_without_prototypical
type: kanishka/counterfactual_babylm_without_prototypical
metrics:
- name: Accuracy
type: accuracy
value: 0.4086648411559096
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_without_prototypical dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4242
- Accuracy: 0.4087
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.738 | 1.0 | 18593 | 3.8623 | 0.3488 |
| 3.4383 | 2.0 | 37186 | 3.6089 | 0.3755 |
| 3.2925 | 3.0 | 55779 | 3.5010 | 0.3888 |
| 3.2055 | 4.0 | 74372 | 3.4100 | 0.3959 |
| 3.1461 | 5.0 | 92965 | 3.4109 | 0.4001 |
| 3.1029 | 6.0 | 111558 | 3.3880 | 0.4016 |
| 3.0643 | 7.0 | 130151 | 3.3745 | 0.4044 |
| 3.0298 | 8.0 | 148744 | 3.3771 | 0.4053 |
| 2.9984 | 9.0 | 167337 | 3.3831 | 0.4060 |
| 2.9747 | 10.0 | 185930 | 3.4011 | 0.4065 |
| 2.9573 | 11.0 | 204523 | 3.3670 | 0.4077 |
| 2.925 | 12.0 | 223116 | 3.3797 | 0.4083 |
| 2.9085 | 13.0 | 241709 | 3.3926 | 0.4079 |
| 2.8902 | 14.0 | 260302 | 3.3865 | 0.4088 |
| 2.8665 | 15.0 | 278895 | 3.3980 | 0.4086 |
| 2.8463 | 16.0 | 297488 | 3.3972 | 0.4092 |
| 2.8272 | 17.0 | 316081 | 3.4006 | 0.4089 |
| 2.8085 | 18.0 | 334674 | 3.4044 | 0.4090 |
| 2.7882 | 19.0 | 353267 | 3.4141 | 0.4088 |
| 2.7754 | 20.0 | 371860 | 3.4242 | 0.4087 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Inchul/lora-sdxl-dog
|
Inchul
| 2023-12-15T05:46:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:46:13Z |
Entry not found
|
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PsychicMoon/mistralai-Code-Instruct-dpbot-ll1
|
PsychicMoon
| 2023-12-15T05:48:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:48:03Z |
Entry not found
|
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] |
sshh12/Mistral-7B-LoRA-XCLIP
|
sshh12
| 2023-12-15T05:50:20Z | 0 | 1 |
peft
|
[
"peft",
"finetuned",
"multimodal",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T05:48:30Z |
---
license: apache-2.0
library_name: peft
tags:
- finetuned
- multimodal
base_model: mistralai/Mistral-7B-Instruct-v0.1
dataset: sshh12/xclip-videoinstruct-finetune
inference: false
---
These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications.
### Modalities
* XCLIPVideoModality (use `<video>` in text and provide `videos`, encoded as 10 tokens)
### Usage
GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server)
### Dataset
sshh12/xclip-videoinstruct-finetune (100010 examples)
```
{'videos': ['https://www.youtube.com/watch?v=k_ZXmr8pmrs'], 'messages': [{'content': 'What are the main activities that take place in the video?', 'role': 'user'}, {'content': 'The main activities that take place in the video are the preparation of camera equipment by a man, a group of men riding a helicopter, and a man sailing a boat through the water.', 'role': 'assistant'}]}
```
### Training Device(s)
```
name, pci.bus_id, vbios_version
NVIDIA RTX A6000, 00000000:82:00.0, 94.02.5C.00.02
```
### Model
```
MistralLMMForCausalLM.model =
PeftModelForCausalLM(
(base_model): LoraModel(
(model): MistralLMMForCausalLM(
(model): MistralLMMModel(
(embed_tokens): Embedding(32000, 4096)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=14336, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=14336, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
(video_xclip_lmm_projector): _MLPVectorProjector(
(mlps): ModuleList(
(0-9): 10 x Sequential(
(0): Linear(in_features=512, out_features=4096, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=4096, out_features=4096, bias=True)
)
)
)
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
)
)
```
### Framework versions
- PEFT 0.7.1
|
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] |
JUNstats/klue-bert-News
|
JUNstats
| 2023-12-15T06:30:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T05:54:40Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-News-News
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-News-News
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3536
- Accuracy: 0.9462
- F1: 0.9463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0299 | 1.0 | 614 | 0.3664 | 0.9350 | 0.9347 |
| 0.0149 | 2.0 | 1228 | 0.3766 | 0.9422 | 0.9423 |
| 0.0059 | 3.0 | 1842 | 0.3536 | 0.9462 | 0.9463 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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GordonMcGregor/path-to-save-model
|
GordonMcGregor
| 2023-12-15T05:55:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:55:56Z |
Entry not found
|
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ctam8736/whisper-tiny-es-baseline
|
ctam8736
| 2023-12-15T05:58:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T05:58:45Z |
Entry not found
|
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PsychicMoon/zephyr-dpbot-ll1
|
PsychicMoon
| 2023-12-15T06:04:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:04:55Z |
Entry not found
|
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peterandrew987/model
|
peterandrew987
| 2023-12-15T06:11:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:11:30Z |
Entry not found
|
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vivekdugale/llama2_7b_fine_tuned_mental_health_e25
|
vivekdugale
| 2023-12-15T06:12:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:12:33Z |
Entry not found
|
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rickettswa/GPT2TokenizerFast_KMR
|
rickettswa
| 2023-12-15T06:15:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:15:37Z |
Entry not found
|
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sujayC66/llama2_E25_heliosbrahma_mental_health_chatbot_data
|
sujayC66
| 2023-12-15T06:15:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:15:43Z |
Entry not found
|
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] |
anmolsharma142/t5finetuned
|
anmolsharma142
| 2023-12-15T06:30:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T06:15:58Z |
---
license: mit
---
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_25
|
ADISH007
| 2023-12-15T06:17:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T06:17:36Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_001_fold2
|
hkivancoral
| 2023-12-15T07:15:24Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T06:20:56Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8818635607321131
---
<!-- 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. -->
# smids_5x_deit_tiny_sgd_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3259
- Accuracy: 0.8819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8524 | 1.0 | 375 | 0.7691 | 0.6689 |
| 0.5007 | 2.0 | 750 | 0.5539 | 0.7804 |
| 0.4114 | 3.0 | 1125 | 0.4742 | 0.8070 |
| 0.3629 | 4.0 | 1500 | 0.4296 | 0.8286 |
| 0.3623 | 5.0 | 1875 | 0.3981 | 0.8469 |
| 0.3098 | 6.0 | 2250 | 0.3783 | 0.8502 |
| 0.3017 | 7.0 | 2625 | 0.3643 | 0.8453 |
| 0.3224 | 8.0 | 3000 | 0.3602 | 0.8519 |
| 0.2666 | 9.0 | 3375 | 0.3471 | 0.8586 |
| 0.2737 | 10.0 | 3750 | 0.3436 | 0.8552 |
| 0.2547 | 11.0 | 4125 | 0.3356 | 0.8669 |
| 0.2986 | 12.0 | 4500 | 0.3379 | 0.8602 |
| 0.2268 | 13.0 | 4875 | 0.3304 | 0.8669 |
| 0.2538 | 14.0 | 5250 | 0.3304 | 0.8702 |
| 0.2279 | 15.0 | 5625 | 0.3282 | 0.8602 |
| 0.1964 | 16.0 | 6000 | 0.3276 | 0.8719 |
| 0.2475 | 17.0 | 6375 | 0.3297 | 0.8652 |
| 0.2224 | 18.0 | 6750 | 0.3277 | 0.8669 |
| 0.1863 | 19.0 | 7125 | 0.3205 | 0.8686 |
| 0.2493 | 20.0 | 7500 | 0.3208 | 0.8752 |
| 0.1873 | 21.0 | 7875 | 0.3214 | 0.8769 |
| 0.1921 | 22.0 | 8250 | 0.3223 | 0.8735 |
| 0.2083 | 23.0 | 8625 | 0.3204 | 0.8735 |
| 0.1865 | 24.0 | 9000 | 0.3201 | 0.8702 |
| 0.1643 | 25.0 | 9375 | 0.3196 | 0.8802 |
| 0.2115 | 26.0 | 9750 | 0.3209 | 0.8785 |
| 0.2108 | 27.0 | 10125 | 0.3192 | 0.8802 |
| 0.1576 | 28.0 | 10500 | 0.3201 | 0.8802 |
| 0.1807 | 29.0 | 10875 | 0.3220 | 0.8785 |
| 0.1891 | 30.0 | 11250 | 0.3216 | 0.8802 |
| 0.1864 | 31.0 | 11625 | 0.3224 | 0.8835 |
| 0.1759 | 32.0 | 12000 | 0.3215 | 0.8852 |
| 0.1618 | 33.0 | 12375 | 0.3224 | 0.8835 |
| 0.1343 | 34.0 | 12750 | 0.3219 | 0.8835 |
| 0.1642 | 35.0 | 13125 | 0.3213 | 0.8852 |
| 0.1538 | 36.0 | 13500 | 0.3239 | 0.8785 |
| 0.1527 | 37.0 | 13875 | 0.3229 | 0.8852 |
| 0.1581 | 38.0 | 14250 | 0.3248 | 0.8802 |
| 0.135 | 39.0 | 14625 | 0.3238 | 0.8852 |
| 0.1591 | 40.0 | 15000 | 0.3237 | 0.8835 |
| 0.1366 | 41.0 | 15375 | 0.3243 | 0.8819 |
| 0.1361 | 42.0 | 15750 | 0.3249 | 0.8785 |
| 0.1751 | 43.0 | 16125 | 0.3245 | 0.8835 |
| 0.135 | 44.0 | 16500 | 0.3255 | 0.8819 |
| 0.1208 | 45.0 | 16875 | 0.3256 | 0.8819 |
| 0.1748 | 46.0 | 17250 | 0.3261 | 0.8819 |
| 0.1449 | 47.0 | 17625 | 0.3264 | 0.8785 |
| 0.1594 | 48.0 | 18000 | 0.3263 | 0.8785 |
| 0.1892 | 49.0 | 18375 | 0.3260 | 0.8819 |
| 0.1218 | 50.0 | 18750 | 0.3259 | 0.8819 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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mingdali/ChatTruth-7B
|
mingdali
| 2023-12-15T06:23:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:23:38Z |
Entry not found
|
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PsychicMoon/zephyr-dpbot-ll2
|
PsychicMoon
| 2023-12-15T06:24:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:24:48Z |
Entry not found
|
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msubbu0/bert-finetuned-squad
|
msubbu0
| 2023-12-15T06:28:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:28:04Z |
Entry not found
|
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damo-vilab/i2vgen-xl
|
damo-vilab
| 2023-12-16T04:07:03Z | 0 | 54 | null |
[
"arxiv:2312.09109",
"arxiv:2308.06571",
"arxiv:2311.04145",
"license:mit",
"region:us"
] | null | 2023-12-15T06:28:05Z |
---
license: mit
---
# VGen
VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods:
- [I2VGen-xl: High-quality image-to-video synthesis via cascaded diffusion models](https://i2vgen-xl.github.io/)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://videocomposer.github.io/)
- [Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation](https://higen-t2v.github.io/)
- [A Recipe for Scaling up Text-to-Video Generation with Text-free Videos]()
- [InstructVideo: Instructing Video Diffusion Models with Human Feedback]()
- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://dreamvideo-t2v.github.io/)
- [VideoLCM: Video Latent Consistency Model](https://arxiv.org/abs/2312.09109)
- [Modelscope text-to-video technical report](https://arxiv.org/abs/2308.06571)
VGen can produce high-quality videos from the input text, images, desired motion, desired subjects, and even the feedback signals provided. It also offers a variety of commonly used video generation tools such as visualization, sampling, training, inference, join training using images and videos, acceleration, and more.
<a href='https://i2vgen-xl.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2311.04145'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [](https://youtu.be/XUi0y7dxqEQ) <a href='https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441039979087.mp4'><img src='source/logo.png'></a>
## 🔥News!!!
- __[2023.12]__ We release the high-efficiency video generation method [VideoLCM](https://arxiv.org/abs/2312.09109)
- __[2023.12]__ We release the code and model of I2VGen-XL and the ModelScope T2V
- __[2023.12]__ We release the T2V method [HiGen](https://higen-t2v.github.io) and customizing T2V method [DreamVideo](https://dreamvideo-t2v.github.io).
- __[2023.12]__ We write an [introduction docment](doc/introduction.pdf) for VGen and compare I2VGen-XL with SVD.
- __[2023.11]__ We release a high-quality I2VGen-XL model, please refer to the [Webpage](https://i2vgen-xl.github.io)
## TODO
- [x] Release the technical papers and webpage of [I2VGen-XL](doc/i2vgen-xl.md)
- [x] Release the code and pretrained models that can generate 1280x720 videos
- [ ] Release models optimized specifically for the human body and faces
- [ ] Updated version can fully maintain the ID and capture large and accurate motions simultaneously
- [ ] Release other methods and the corresponding models
## Preparation
The main features of VGen are as follows:
- Expandability, allowing for easy management of your own experiments.
- Completeness, encompassing all common components for video generation.
- Excellent performance, featuring powerful pre-trained models in multiple tasks.
### Installation
```
conda create -n vgen python=3.8
conda activate vgen
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### Datasets
We have provided a **demo dataset** that includes images and videos, along with their lists in ``data``.
*Please note that the demo images used here are for testing purposes and were not included in the training.*
### Clone codeb
```
git clone https://github.com/damo-vilab/i2vgen-xl.git
cd i2vgen-xl
```
## Getting Started with VGen
### (1) Train your text-to-video model
Executing the following command to enable distributed training is as easy as that.
```
python train_net.py --cfg configs/t2v_train.yaml
```
In the `t2v_train.yaml` configuration file, you can specify the data, adjust the video-to-image ratio using `frame_lens`, and validate your ideas with different Diffusion settings, and so on.
- Before the training, you can download any of our open-source models for initialization. Our codebase supports custom initialization and `grad_scale` settings, all of which are included in the `Pretrain` item in yaml file.
- During the training, you can view the saved models and intermediate inference results in the `workspace/experiments/t2v_train`directory.
After the training is completed, you can perform inference on the model using the following command.
```
python inference.py --cfg configs/t2v_infer.yaml
```
Then you can find the videos you generated in the `workspace/experiments/test_img_01` directory. For specific configurations such as data, models, seed, etc., please refer to the `t2v_infer.yaml` file.
<!-- <table>
<center>
<tr>
<td ><center>
<video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4"></video>
</center></td>
<td ><center>
<video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4"></video>
</center></td>
</tr>
</center>
</table>
</center> -->
<table>
<center>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01Ya2I5I25utrJwJ9Jf_!!6000000007587-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01CrmYaz1zXBetmg3dd_!!6000000006723-2-tps-1280-720.png"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4">HRER</a> to view the generated video.</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
</center>
</table>
</center>
### (2) Run the I2VGen-XL model
(i) Download model and test data:
```
!pip install modelscope
from modelscope.hub.snapshot_download import snapshot_download
model_dir = snapshot_download('damo/I2VGen-XL', cache_dir='models/', revision='v1.0.0')
```
(ii) Run the following command:
```
python inference.py --cfg configs/i2vgen_xl_infer.yaml
```
In a few minutes, you can retrieve the high-definition video you wish to create from the `workspace/experiments/test_img_01` directory. At present, we find that the current model performs inadequately on **anime images** and **images with a black background** due to the lack of relevant training data. We are consistently working to optimize it.
<span style="color:red">Due to the compression of our video quality in GIF format, please click 'HRER' below to view the original video.</span>
<center>
<table>
<center>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01CCEq7K1ZeLpNQqrWu_!!6000000003219-0-tps-1280-720.jpg"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01hIQcvG1spmQMLqBo0_!!6000000005816-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01ZXY7UN23K8q4oQ3uG_!!6000000007236-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01iaSiiv1aJZURUEY53_!!6000000003309-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01NHpVGl1oat4H54Hjf_!!6000000005242-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4"></video> -->
<!-- <image muted="true" height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>
-->
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4">HERE</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01odS61s1WW9tXen21S_!!6000000002795-0-tps-1280-720.jpg"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01Jyk1HT28JkZtpAtY6_!!6000000007912-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4">HERE</a> to view the generated video.</p>
</center></td>
</tr>
</center>
</table>
</center>
### (3) Other methods
In preparation.
## Customize your own approach
Our codebase essentially supports all the commonly used components in video generation. You can manage your experiments flexibly by adding corresponding registration classes, including `ENGINE, MODEL, DATASETS, EMBEDDER, AUTO_ENCODER, DISTRIBUTION, VISUAL, DIFFUSION, PRETRAIN`, and can be compatible with all our open-source algorithms according to your own needs. If you have any questions, feel free to give us your feedback at any time.
## BibTeX
If this repo is useful to you, please cite our corresponding technical paper.
```bibtex
@article{2023i2vgenxl,
title={I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models},
author={Zhang, Shiwei and Wang, Jiayu and Zhang, Yingya and Zhao, Kang and Yuan, Hangjie and Qing, Zhiwu and Wang, Xiang and Zhao, Deli and Zhou, Jingren},
booktitle={arXiv preprint arXiv:2311.04145},
year={2023}
}
@article{2023videocomposer,
title={VideoComposer: Compositional Video Synthesis with Motion Controllability},
author={Wang, Xiang and Yuan, Hangjie and Zhang, Shiwei and Chen, Dayou and Wang, Jiuniu, and Zhang, Yingya, and Shen, Yujun, and Zhao, Deli and Zhou, Jingren},
booktitle={arXiv preprint arXiv:2306.02018},
year={2023}
}
@article{wang2023modelscope,
title={Modelscope text-to-video technical report},
author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei},
journal={arXiv preprint arXiv:2308.06571},
year={2023}
}
@article{dreamvideo,
title={DreamVideo: Composing Your Dream Videos with Customized Subject and Motion},
author={Wei, Yujie and Zhang, Shiwei and Qing, Zhiwu and Yuan, Hangjie and Liu, Zhiheng and Liu, Yu and Zhang, Yingya and Zhou, Jingren and Shan, Hongming},
journal={arXiv preprint arXiv:2312.04433},
year={2023}
}
@article{qing2023higen,
title={Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation},
author={Qing, Zhiwu and Zhang, Shiwei and Wang, Jiayu and Wang, Xiang and Wei, Yujie and Zhang, Yingya and Gao, Changxin and Sang, Nong },
journal={arXiv preprint arXiv:2312.04483},
year={2023}
}
@article{wang2023videolcm,
title={VideoLCM: Video Latent Consistency Model},
author={Wang, Xiang and Zhang, Shiwei and Zhang, Han and Liu, Yu and Zhang, Yingya and Gao, Changxin and Sang, Nong },
journal={arXiv preprint arXiv:2312.09109},
year={2023}
}
```
## Disclaimer
This open-source model is trained with using [WebVid-10M](https://m-bain.github.io/webvid-dataset/) and [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/) datasets and is intended for <strong>RESEARCH/NON-COMMERCIAL USE ONLY</strong>.
|
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Ruby071333/Ruby
|
Ruby071333
| 2023-12-15T06:34:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:34:41Z |
Entry not found
|
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] |
bachngo/merged_llama2_vncor60k
|
bachngo
| 2023-12-15T07:02:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T06:36:14Z |
Entry not found
|
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JUNstats/kobigbird-bert-base-News
|
JUNstats
| 2023-12-15T06:50:26Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T06:37:24Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: kobigbird-bert-base-News
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. -->
# kobigbird-bert-base-News
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2298
- Accuracy: 0.9367
- F1: 0.9369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6302 | 1.0 | 614 | 0.3209 | 0.9037 | 0.9023 |
| 0.2721 | 2.0 | 1228 | 0.2438 | 0.9293 | 0.9298 |
| 0.1845 | 3.0 | 1842 | 0.2298 | 0.9367 | 0.9369 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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Mirriks/at
|
Mirriks
| 2023-12-15T06:41:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:41:40Z |
Entry not found
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Vinovijay/mistrel-wealth-alpaca_lora
|
Vinovijay
| 2023-12-15T06:44:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:44:38Z |
Entry not found
|
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octavioj/RosalIA
|
octavioj
| 2023-12-15T06:44:53Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-15T06:44:53Z |
---
license: unknown
---
|
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] |
ddivakar/test
|
ddivakar
| 2023-12-15T06:49:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T06:48:23Z |
---
license: apache-2.0
---
|
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] |
LilYoda/MasterChief
|
LilYoda
| 2023-12-15T06:50:51Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T06:48:44Z |
---
license: openrail
---
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep11
|
stvhuang
| 2023-12-15T06:53:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T06:50:13Z |
Entry not found
|
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] |
aaa12963337/msi-resnet18-pretrain
|
aaa12963337
| 2023-12-15T07:21:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/resnet-18",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T06:52:06Z |
---
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-resnet18-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.908774373259053
---
<!-- 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. -->
# msi-resnet18-pretrain
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2966
- Accuracy: 0.9088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2719 | 1.0 | 1562 | 0.3501 | 0.8891 |
| 0.1611 | 2.0 | 3125 | 0.2813 | 0.9118 |
| 0.166 | 3.0 | 4687 | 0.3161 | 0.8930 |
| 0.1071 | 4.0 | 6248 | 0.2966 | 0.9088 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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saharzal/parsig_tokenizer
|
saharzal
| 2023-12-16T06:16:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T06:59:51Z |
Entry not found
|
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] |
doujohner/test123
|
doujohner
| 2023-12-15T07:00:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T07:00:30Z |
---
license: apache-2.0
---
|
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] |
worachot-n/2_bart_topic_contrastive_combine
|
worachot-n
| 2023-12-15T07:06:27Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-15T07:04:55Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_chatGPT_Seed106
|
behzadnet
| 2023-12-15T07:07:40Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T07:07:33Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.7.0.dev0
## 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 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_chatGPT_Seed106
|
behzadnet
| 2023-12-15T07:07:50Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T07:07:46Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.7.0.dev0
|
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ChatRDM/mistral-946
|
ChatRDM
| 2023-12-15T08:30:31Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T07:12:09Z |
---
license: mit
---
|
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athirdpath/ChubbyIambe-22b-EXPERIMENTAL
|
athirdpath
| 2023-12-15T08:54:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T07:13:19Z |
---
license: cc-by-nc-4.0
language:
- en
---
This is probably an abomination.
EDIT: This is an abomination. It writes okay-ish, but certainly not worth the extra inference requirements. Probably made a mistake, was supposed to be 23b.
|
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CatSiewDai/OpenLLAMA
|
CatSiewDai
| 2023-12-15T07:14:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T07:14:33Z |
Entry not found
|
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mitchyAI/iumchy
|
mitchyAI
| 2023-12-15T07:15:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-15T07:14:55Z |
---
license: creativeml-openrail-m
---
|
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owanr/ghc-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T02:06:07Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T07:15:01Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
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. -->
# ghc-roberta-base-inter-sorted-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4268
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6784 | 1.0 | 11020 | 0.4245 |
| 0.3594 | 2.0 | 22040 | 0.4268 |
| 0.4199 | 3.0 | 33060 | 0.4268 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_001_fold3
|
hkivancoral
| 2023-12-15T08:10:45Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T07:16:05Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# smids_5x_deit_tiny_sgd_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2648
- Accuracy: 0.9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7316 | 1.0 | 375 | 0.7849 | 0.6383 |
| 0.5408 | 2.0 | 750 | 0.5416 | 0.805 |
| 0.4144 | 3.0 | 1125 | 0.4477 | 0.8483 |
| 0.4149 | 4.0 | 1500 | 0.3929 | 0.8533 |
| 0.413 | 5.0 | 1875 | 0.3596 | 0.865 |
| 0.347 | 6.0 | 2250 | 0.3405 | 0.87 |
| 0.3766 | 7.0 | 2625 | 0.3237 | 0.885 |
| 0.3353 | 8.0 | 3000 | 0.3140 | 0.8833 |
| 0.2912 | 9.0 | 3375 | 0.3069 | 0.8817 |
| 0.2983 | 10.0 | 3750 | 0.3022 | 0.8883 |
| 0.2271 | 11.0 | 4125 | 0.2992 | 0.8867 |
| 0.2804 | 12.0 | 4500 | 0.2892 | 0.8917 |
| 0.2434 | 13.0 | 4875 | 0.2876 | 0.89 |
| 0.2434 | 14.0 | 5250 | 0.2819 | 0.8883 |
| 0.2457 | 15.0 | 5625 | 0.2817 | 0.8967 |
| 0.2178 | 16.0 | 6000 | 0.2772 | 0.9 |
| 0.2586 | 17.0 | 6375 | 0.2753 | 0.9 |
| 0.2424 | 18.0 | 6750 | 0.2760 | 0.8967 |
| 0.2316 | 19.0 | 7125 | 0.2730 | 0.8967 |
| 0.236 | 20.0 | 7500 | 0.2701 | 0.9033 |
| 0.1785 | 21.0 | 7875 | 0.2679 | 0.9017 |
| 0.1868 | 22.0 | 8250 | 0.2698 | 0.9 |
| 0.2515 | 23.0 | 8625 | 0.2683 | 0.8983 |
| 0.2504 | 24.0 | 9000 | 0.2635 | 0.8967 |
| 0.2044 | 25.0 | 9375 | 0.2645 | 0.9033 |
| 0.2051 | 26.0 | 9750 | 0.2668 | 0.8983 |
| 0.2231 | 27.0 | 10125 | 0.2645 | 0.9033 |
| 0.2003 | 28.0 | 10500 | 0.2627 | 0.8983 |
| 0.1423 | 29.0 | 10875 | 0.2631 | 0.9033 |
| 0.2099 | 30.0 | 11250 | 0.2641 | 0.9 |
| 0.2023 | 31.0 | 11625 | 0.2642 | 0.9 |
| 0.2174 | 32.0 | 12000 | 0.2642 | 0.9 |
| 0.198 | 33.0 | 12375 | 0.2636 | 0.8967 |
| 0.1518 | 34.0 | 12750 | 0.2625 | 0.9033 |
| 0.1375 | 35.0 | 13125 | 0.2629 | 0.9017 |
| 0.1414 | 36.0 | 13500 | 0.2638 | 0.9017 |
| 0.1599 | 37.0 | 13875 | 0.2634 | 0.9033 |
| 0.164 | 38.0 | 14250 | 0.2642 | 0.9 |
| 0.1442 | 39.0 | 14625 | 0.2626 | 0.8983 |
| 0.1928 | 40.0 | 15000 | 0.2641 | 0.9017 |
| 0.1643 | 41.0 | 15375 | 0.2643 | 0.9017 |
| 0.1534 | 42.0 | 15750 | 0.2642 | 0.9017 |
| 0.1818 | 43.0 | 16125 | 0.2644 | 0.9017 |
| 0.1596 | 44.0 | 16500 | 0.2650 | 0.9 |
| 0.1441 | 45.0 | 16875 | 0.2645 | 0.9017 |
| 0.1513 | 46.0 | 17250 | 0.2643 | 0.9 |
| 0.1221 | 47.0 | 17625 | 0.2647 | 0.9 |
| 0.1853 | 48.0 | 18000 | 0.2646 | 0.9 |
| 0.1404 | 49.0 | 18375 | 0.2649 | 0.9 |
| 0.1644 | 50.0 | 18750 | 0.2648 | 0.9 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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EroxPlus/EroxPlus
|
EroxPlus
| 2023-12-15T07:18:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T07:16:48Z |
Erox Plus: Poland, kapsułki, Cena, Opinie, Tabletki, Składniki, Korzyści, koszt, Pigułki!
Erox Plus to naturalny suplement diety opracowany z myślą o wspieraniu zdrowia i sprawności seksualnej mężczyzn. Erox Plus Poland
<a href="https://www.nutritioncrawler.com/EroPluPola">Erox Plus Kup teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskaj teraz 50% zniżki !! Pośpiesz się !!</a>
Oficjalna strona internetowa: <a href="https://www.nutritioncrawler.com/EroPluPola">www.EroxPlus.com</a>
<p><a href="https://www.nutritioncrawler.com/EroPluPola"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDH-pDTQpiJyKIP8aBNz3mfzQITm6JXK56LKad8_p9xltuMIx6zXJyhARTJ5Wm1dcyIGiD73NuUabIQDMpfT__IC37xp0gQy1_5_zbYhHWu9HPJptwhu9nUUE3jy3jIGiX9UeYfXTTf-oSuTQTevl6B2k9sgRVHLp0WZhVQwjHavi9txFGbWggDPeVR68/w635-h395/Erox%20Plus%20poland.png" alt="enter image description here"> </a></p>
➢Nazwa produktu — Erox Plus
➢Kategoria - Wzmocnienie Męskości
➢Główne korzyści — Poprawia zdrowie i sprawność seksualną
➢ Skład — Naturalny Związek Organiczny
➢ Skutki uboczne – NA
➢Ocena końcowa: — 4.8
➢ Dostępność — online
➢Oferty i rabaty; OSZCZĘDŹ DZIŚ! KUP TERAZ, ABY KUPOWAĆ OFERTĘ SPECJALNĄ!!!
Co to jest Erox Plus?
Formuła Erox Plus, stworzona z mieszanki silnych ziół i ekstraktów, ma na celu zajęcie się różnymi aspektami dobrego samopoczucia seksualnego, w tym wytrzymałością, wytrzymałością i libido. W przeciwieństwie do alternatyw farmaceutycznych, Erox Plus kapsułki szczyci się wykorzystaniem mocy naturalnych składników w celu zwiększenia ogólnej męskiej witalności.
<a href="https://www.nutritioncrawler.com/EroPluPola">Erox Plus Kup teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskaj teraz 50% zniżki !! Pośpiesz się !!</a>
Oficjalna strona internetowa: <a href="https://www.nutritioncrawler.com/EroPluPola">www.EroxPlus.com</a>
<p><a href="https://www.nutritioncrawler.com/EroPluPola"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDH-pDTQpiJyKIP8aBNz3mfzQITm6JXK56LKad8_p9xltuMIx6zXJyhARTJ5Wm1dcyIGiD73NuUabIQDMpfT__IC37xp0gQy1_5_zbYhHWu9HPJptwhu9nUUE3jy3jIGiX9UeYfXTTf-oSuTQTevl6B2k9sgRVHLp0WZhVQwjHavi9txFGbWggDPeVR68/w635-h395/Erox%20Plus%20poland.png" alt="enter image description here"> </a></p>
Erox Plus Erox Plus Tabletki Erox Plus kapsułki Erox Plus Pigułki Erox Plus Cena Erox Plus Opinie Erox Plus Składniki Erox Plus Korzyści Erox Plus Skutki uboczne Erox Plus Cena kapsułki Erox Plus Recenzje kapsułki Erox Plus Kompozycja Erox Plus Reklamacja Erox Plus Gdzie kupić Erox Plus Jak używać Erox Plus koszt Erox Plus Pracuje Erox Plus forum Erox Plus oryginał Erox Plus Apteka
Jak działa Erox Plus?
Skuteczność Erox Plus leży w starannie dobranych składnikach, z których każdy został wybrany ze względu na jego potencjał pozytywnego wpływu na zdrowie seksualne mężczyzn. Formuła zazwyczaj zawiera składniki znane ze swoich właściwości afrodyzjaku, poprawy przepływu krwi i wsparcia testosteronu. Erox Plus zawiera składniki, które, jak się uważa, stymulują pożądanie seksualne, promując zwiększone libido i podniecenie.
Gdzie kupić Erox Plus?
Dla osób zainteresowanych wypróbowaniem Erox Plus ważne jest, aby kupić go z renomowanych źródeł. Oficjalna strona internetowa produktu to niezawodna opcja, dająca pewność, że otrzymasz oryginalny produkt z pełnymi zaletami formuły. Uważaj na zewnętrznych sprzedawców i zawsze sprawdzaj recenzje klientów, aby ocenić skuteczność produktu.
<a href="https://www.nutritioncrawler.com/EroPluPola">Erox Plus Kup teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskaj teraz 50% zniżki !! Pośpiesz się !!</a>
Oficjalna strona internetowa: <a href="https://www.nutritioncrawler.com/EroPluPola">www.EroxPlus.com</a>
<p><a href="https://www.nutritioncrawler.com/EroPluPola"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDH-pDTQpiJyKIP8aBNz3mfzQITm6JXK56LKad8_p9xltuMIx6zXJyhARTJ5Wm1dcyIGiD73NuUabIQDMpfT__IC37xp0gQy1_5_zbYhHWu9HPJptwhu9nUUE3jy3jIGiX9UeYfXTTf-oSuTQTevl6B2k9sgRVHLp0WZhVQwjHavi9txFGbWggDPeVR68/w635-h395/Erox%20Plus%20poland.png" alt="enter image description here"> </a></p>
Streszczenie:
Podsumowując, Erox Plus to naturalny suplement, który obiecuje poprawę zdrowia seksualnego mężczyzn dzięki mieszance starannie dobranych składników. Dzięki potencjalnym korzyściom, od zwiększonej wytrzymałości po zwiększone libido, produkt ten ma na celu wsparcie mężczyzn w odzyskaniu pewności siebie w sypialni. Jeśli zastanawiasz się nad wypróbowaniem Erox Plus, pamiętaj, aby kupić go na oficjalnej stronie internetowej, aby mieć pewność autentyczności i jakości produktu. Wyrusz w podróż ku poprawie witalności i dobrego samopoczucia seksualnego z Erox Plus.
Erox Plus Erox Plus Tabletki Erox Plus kapsułki Erox Plus Pigułki Erox Plus Cena Erox Plus Opinie Erox Plus Składniki Erox Plus Korzyści Erox Plus Skutki uboczne Erox Plus Cena kapsułki Erox Plus Recenzje kapsułki Erox Plus Kompozycja Erox Plus Reklamacja Erox Plus Gdzie kupić Erox Plus Jak używać Erox Plus koszt Erox Plus Pracuje Erox Plus forum Erox Plus oryginał Erox Plus Apteka
Czytaj więcej: https://www.nutritioncrawler.com/EroPluPola
https://sites.google.com/view/erox-plus/home
https://healthtoned.blogspot.com/2023/12/erox-plus-poland.html
https://medium.com/@healthytalk24x7/erox-plus-poland-a5897766ee50
https://medium.com/@healthytalk24x7/erox-plus-poland-kapsu%C5%82ki-cena-opinie-tabletki-sk%C5%82adniki-korzy%C5%9Bci-koszt-pigu%C5%82ki-0fae66d6ca53
https://www.weddingwire.com/website/erox-and-plus/eroxplus-2
https://infogram.com/erox-plus-1h7v4pwo0qoej6k?live
https://softisenilspain.hashnode.dev/erox-plus-poland
https://eroxplus.company.site/
https://sway.office.com/FfPSBVfbqMPdsUwV
https://gamma.app/docs/Erox-Plus-4eghorfj7qasnq9?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/erox-plus
https://groups.google.com/g/snshine/c/3-A8KQVRwfU
https://www.sunflower-cissp.com/glossary/cissp/7279/erox-plus
https://replit.com/@eroxplus
|
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] |
worachot-n/2_bart_topic_contrastive_combine_word_tagger
|
worachot-n
| 2023-12-15T07:18:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-15T07:17:25Z |
Entry not found
|
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] |
jan-hq/solar-10.7b-instruct-v1.0-GGUF
|
jan-hq
| 2023-12-15T07:47:39Z | 0 | 0 | null |
[
"gguf",
"en",
"dataset:c-s-ale/alpaca-gpt4-data",
"dataset:Open-Orca/OpenOrca",
"dataset:Intel/orca_dpo_pairs",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"base_model:upstage/SOLAR-10.7B-Instruct-v1.0",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-15T07:19:49Z |
---
datasets:
- c-s-ale/alpaca-gpt4-data
- Open-Orca/OpenOrca
- Intel/orca_dpo_pairs
- allenai/ultrafeedback_binarized_cleaned
language:
- en
license: cc-by-nc-4.0
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
model_creator: upstage
model_name: SOLAR-10.7B-Instruct-v1.0
quantized_by: JanHQ
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This is a GGUF version of [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
- Model creator: [upstage](https://huggingface.co/upstage)
- Original model: [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
- Model description: [Readme](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/blob/main/README.md)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Converter
This is a repository for the [open-source converter](https://github.com/janhq/model-converter). We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
|
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