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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_amh-esp-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0175
- Spearman Corr: 0.8510
## 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: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.76 | 200 | 0.0231 | 0.8199 |
| 0.0378 | 3.52 | 400 | 0.0142 | 0.8523 |
| 0.021 | 5.29 | 600 | 0.0142 | 0.8544 |
| 0.0157 | 7.05 | 800 | 0.0144 | 0.8553 |
| 0.0125 | 8.81 | 1000 | 0.0159 | 0.8538 |
| 0.0104 | 10.57 | 1200 | 0.0156 | 0.8515 |
| 0.0083 | 12.33 | 1400 | 0.0158 | 0.8503 |
| 0.0067 | 14.1 | 1600 | 0.0143 | 0.8510 |
| 0.0067 | 15.86 | 1800 | 0.0183 | 0.8493 |
| 0.0059 | 17.62 | 2000 | 0.0175 | 0.8510 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_amh-esp-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_amh-esp-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:53:13+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_amh-esp-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0175
* Spearman Corr: 0.8510
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: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Adapter
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "Adapter", "results": []}]} | null | akkky02/Adapter | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2024-02-15T00:54:58+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Adapter
This model is a fine-tuned version of meta-llama/Llama-2-7b-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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 | [
"# Adapter\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us \n",
"# Adapter\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
39,
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139,
4,
39
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Adapter\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr_outputs
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7024 | 1.0 | 9 | 1.2276 |
| 0.8726 | 2.0 | 18 | 1.1114 |
| 0.4336 | 3.0 | 27 | 1.1273 |
| 0.1907 | 4.0 | 36 | 1.3390 |
| 0.0789 | 5.0 | 45 | 1.3066 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "zephyr_outputs", "results": []}]} | null | lvcalucioli/zephyr_outputs | [
"peft",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"4-bit",
"region:us"
] | 2024-02-15T00:56:22+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us
| zephyr\_outputs
===============
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3066
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 10
* total\_train\_batch\_size: 40
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.16.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
] | [
55,
140,
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"passage: TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
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null | null | diffusers | # RVC_JonahJameson
<Gallery />
## Download model
[Download](/coversia21/RVC_JonahJameson/tree/main) them in the Files & versions tab.
| {"license": "openrail", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/1_8W7Z4aMRw_etKmtmUpmxWw.jpg"}}], "base_model": "h94/IP-Adapter-FaceID"} | text-to-image | coversia21/RVC_JonahJameson | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:h94/IP-Adapter-FaceID",
"license:openrail",
"region:us"
] | 2024-02-15T00:58:09+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-h94/IP-Adapter-FaceID #license-openrail #region-us
| # RVC_JonahJameson
<Gallery />
## Download model
Download them in the Files & versions tab.
| [
"# RVC_JonahJameson\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-h94/IP-Adapter-FaceID #license-openrail #region-us \n",
"# RVC_JonahJameson\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
58,
14,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-h94/IP-Adapter-FaceID #license-openrail #region-us \n# RVC_JonahJameson\n\n<Gallery />## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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 0.8.2 | {"library_name": "peft", "base_model": "Viet-Mistral/Vistral-7B-Chat"} | null | longcule123/adapter-14-2 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Viet-Mistral/Vistral-7B-Chat",
"region:us"
] | 2024-02-15T01:02:15+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-Viet-Mistral/Vistral-7B-Chat #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-Viet-Mistral/Vistral-7B-Chat #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-Viet-Mistral/Vistral-7B-Chat #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2"
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null | null | nemo | # CHiME8 DASR NeMo Baseline Models
- The model files in this repository are the models used in this paper [The CHiME-7 Challenge: System Description and Performance of
NeMo Team’s DASR System](https://arxiv.org/pdf/2310.12378.pdf).
- These models are needed to execute the CHiME8-DASR baseline [CHiME8-DASR-Baseline NeMo](https://github.com/chimechallenge/C8DASR-Baseline-NeMo/tree/main/scripts/chime8)
- VAD, Diarization and ASR models are all based on [NVIDIA NeMo Conversational AI Toolkits](https://github.com/NVIDIA/NeMo).
## 1. Voice Activity Detection (VAD) Model:
### **[**MarbleNet_frame_VAD_chime7_Acrobat.nemo**](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/MarbleNet_frame_VAD_chime7_Acrobat.nemo)**
- This model is based on [NeMo MarbleNet VAD model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_classification/models.html#marblenet-vad).
- For validation, we use dataset comprises the CHiME-6 development subset as well as 50 hours of simulated audio data.
- The simulated data is generated using the [NeMo multi-speaker data simulator](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb)
on [VoxCeleb1&2 datasets](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html)
- The multi-speaker data simulation results in a total of 2,000 hours of audio, of which approximately 30% is silence.
- The Model training incorporates [SpecAugment](https://arxiv.org/abs/1904.08779) and noise augmentation through [MUSAN noise dataset](https://arxiv.org/abs/1510.08484).
## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)
### **[**MSDD_v2_PALO_100ms_intrpl_3scales.nemo**](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/MSDD_v2_PALO_100ms_intrpl_3scales.nemo)**
Our DASR system is based on the speaker diarization system using the multi-scale diarization decoder (MSDD).
- MSDD Reference: [Park et al. (2022)](https://arxiv.org/pdf/2203.15974.pdf)
- MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.
- TitaNet Reference: [Koluguri et al. (2022)](https://arxiv.org/abs/2110.04410)
- TitaNet Model is included in [MSDD-v2 .nemo checkpoint file](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/MSDD_v2_PALO_100ms_intrpl_3scales.nemo).
- Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.
- This neural model generates logit values indicating speaker existence.
- Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.
- LibriSpeech Reference: [OpenSLR Download](https://www.openslr.org/12),[LibriSpeech, Panayotov et al. (2015)](https://ieeexplore.ieee.org/document/7178964)
- MUSAN noise is also used for adding additive background noise, focusing on music and broadband noise.
## 3. Automatic Speech Recognition (ASR) model
### **[**FastConformerXL-RNNT-chime7-GSS-finetuned.nemo**](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/FastConformerXL-RNNT-chime7-GSS-finetuned.nemo)**
- This ASR model is based on [NeMo FastConformer XL model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).
- Single-channel audio generated using a multi-channel front-end (Guided Source Separation, GSS) is transcribed using a 0.6B parameter Conformer-based transducer (RNNT) model.
- Model Reference: [Gulati et al. (2020)](https://arxiv.org/abs/2005.08100)
- The model was initialized using a publicly available NeMo checkpoint.
- NeMo Checkpoint: [NGC Model Card: Conformer Transducer XL](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xlarge)
- This model was then fine-tuned on the CHiME-7 train and dev set, which includes the CHiME-6 and Mixer6 training subsets, after processing the data through the multi-channel ASR front-end, utilizing ground-truth diarization.
- Fine-Tuning Details:
- Fine-tuning Duration: 35,000 updates
- Batch Size: 128
## 4. Language Model for ASR Decoding: KenLM Model
### **[**ASR_LM_chime7_only.kenlm**](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/ASR_LM_chime7_only.kenlm)**
- This KenLM model is trained solely on CHiME7-DASR datasets (Mixer6, CHiME6, DipCo).
- We apply a word-piece level N-gram language model using byte-pair-encoding (BPE) tokens.
- This approach utilizes the SentencePiece and KenLM toolkits, based on the transcription of CHiME-7 train and dev sets.
- SentencePiece: [Kudo and Richardson (2018)](https://arxiv.org/abs/1808.06226)
- KenLM: [KenLM GitRepo](https://github.com/kpu/kenlm)
- The token sets of our ASR and LM models were matched to ensure consistency.
- To combine several N-gram models with equal weights, we used the OpenGrm library.
- OpenGrm: [Roark et al. (2012)](https://aclanthology.org/P12-3011/)
- MAES decoding was employed for the transducer, which accelerates the decoding process.
- MAES Decoding: [Kim et al. (2020)](https://ieeexplore.ieee.org/document/9250505)
- As expected, integrating the beam-search decoder with the language model significantly enhances the performance of the end-to-end model compared to its pure counterpart.
| {"library_name": "nemo"} | null | chime-dasr/nemo_baseline_models | [
"nemo",
"arxiv:2310.12378",
"arxiv:1904.08779",
"arxiv:1510.08484",
"arxiv:2203.15974",
"arxiv:2110.04410",
"arxiv:2005.08100",
"arxiv:1808.06226",
"region:us"
] | 2024-02-15T01:03:13+00:00 | [
"2310.12378",
"1904.08779",
"1510.08484",
"2203.15974",
"2110.04410",
"2005.08100",
"1808.06226"
] | [] | TAGS
#nemo #arxiv-2310.12378 #arxiv-1904.08779 #arxiv-1510.08484 #arxiv-2203.15974 #arxiv-2110.04410 #arxiv-2005.08100 #arxiv-1808.06226 #region-us
| # CHiME8 DASR NeMo Baseline Models
- The model files in this repository are the models used in this paper The CHiME-7 Challenge: System Description and Performance of
NeMo Team’s DASR System.
- These models are needed to execute the CHiME8-DASR baseline CHiME8-DASR-Baseline NeMo
- VAD, Diarization and ASR models are all based on NVIDIA NeMo Conversational AI Toolkits.
## 1. Voice Activity Detection (VAD) Model:
### MarbleNet_frame_VAD_chime7_Acrobat.nemo
- This model is based on NeMo MarbleNet VAD model.
- For validation, we use dataset comprises the CHiME-6 development subset as well as 50 hours of simulated audio data.
- The simulated data is generated using the NeMo multi-speaker data simulator
on VoxCeleb1&2 datasets
- The multi-speaker data simulation results in a total of 2,000 hours of audio, of which approximately 30% is silence.
- The Model training incorporates SpecAugment and noise augmentation through MUSAN noise dataset.
## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)
### MSDD_v2_PALO_100ms_intrpl_3scales.nemo
Our DASR system is based on the speaker diarization system using the multi-scale diarization decoder (MSDD).
- MSDD Reference: Park et al. (2022)
- MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.
- TitaNet Reference: Koluguri et al. (2022)
- TitaNet Model is included in MSDD-v2 .nemo checkpoint file.
- Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.
- This neural model generates logit values indicating speaker existence.
- Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.
- LibriSpeech Reference: OpenSLR Download,LibriSpeech, Panayotov et al. (2015)
- MUSAN noise is also used for adding additive background noise, focusing on music and broadband noise.
## 3. Automatic Speech Recognition (ASR) model
### URL
- This ASR model is based on NeMo FastConformer XL model.
- Single-channel audio generated using a multi-channel front-end (Guided Source Separation, GSS) is transcribed using a 0.6B parameter Conformer-based transducer (RNNT) model.
- Model Reference: Gulati et al. (2020)
- The model was initialized using a publicly available NeMo checkpoint.
- NeMo Checkpoint: NGC Model Card: Conformer Transducer XL
- This model was then fine-tuned on the CHiME-7 train and dev set, which includes the CHiME-6 and Mixer6 training subsets, after processing the data through the multi-channel ASR front-end, utilizing ground-truth diarization.
- Fine-Tuning Details:
- Fine-tuning Duration: 35,000 updates
- Batch Size: 128
## 4. Language Model for ASR Decoding: KenLM Model
### ASR_LM_chime7_only.kenlm
- This KenLM model is trained solely on CHiME7-DASR datasets (Mixer6, CHiME6, DipCo).
- We apply a word-piece level N-gram language model using byte-pair-encoding (BPE) tokens.
- This approach utilizes the SentencePiece and KenLM toolkits, based on the transcription of CHiME-7 train and dev sets.
- SentencePiece: Kudo and Richardson (2018)
- KenLM: KenLM GitRepo
- The token sets of our ASR and LM models were matched to ensure consistency.
- To combine several N-gram models with equal weights, we used the OpenGrm library.
- OpenGrm: Roark et al. (2012)
- MAES decoding was employed for the transducer, which accelerates the decoding process.
- MAES Decoding: Kim et al. (2020)
- As expected, integrating the beam-search decoder with the language model significantly enhances the performance of the end-to-end model compared to its pure counterpart.
| [
"# CHiME8 DASR NeMo Baseline Models\n\n- The model files in this repository are the models used in this paper The CHiME-7 Challenge: System Description and Performance of\nNeMo Team’s DASR System.\n- These models are needed to execute the CHiME8-DASR baseline CHiME8-DASR-Baseline NeMo\n- VAD, Diarization and ASR models are all based on NVIDIA NeMo Conversational AI Toolkits.",
"## 1. Voice Activity Detection (VAD) Model:",
"### MarbleNet_frame_VAD_chime7_Acrobat.nemo \n- This model is based on NeMo MarbleNet VAD model.\n- For validation, we use dataset comprises the CHiME-6 development subset as well as 50 hours of simulated audio data.\n- The simulated data is generated using the NeMo multi-speaker data simulator\non VoxCeleb1&2 datasets\n- The multi-speaker data simulation results in a total of 2,000 hours of audio, of which approximately 30% is silence.\n- The Model training incorporates SpecAugment and noise augmentation through MUSAN noise dataset.",
"## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)",
"### MSDD_v2_PALO_100ms_intrpl_3scales.nemo\n\n Our DASR system is based on the speaker diarization system using the multi-scale diarization decoder (MSDD).\n - MSDD Reference: Park et al. (2022)\n- MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.\n - TitaNet Reference: Koluguri et al. (2022)\n - TitaNet Model is included in MSDD-v2 .nemo checkpoint file.\n- Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.\n- This neural model generates logit values indicating speaker existence.\n- Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.\n - LibriSpeech Reference: OpenSLR Download,LibriSpeech, Panayotov et al. (2015)\n- MUSAN noise is also used for adding additive background noise, focusing on music and broadband noise.",
"## 3. Automatic Speech Recognition (ASR) model",
"### URL\n- This ASR model is based on NeMo FastConformer XL model. \n- Single-channel audio generated using a multi-channel front-end (Guided Source Separation, GSS) is transcribed using a 0.6B parameter Conformer-based transducer (RNNT) model.\n - Model Reference: Gulati et al. (2020)\n- The model was initialized using a publicly available NeMo checkpoint.\n - NeMo Checkpoint: NGC Model Card: Conformer Transducer XL\n- This model was then fine-tuned on the CHiME-7 train and dev set, which includes the CHiME-6 and Mixer6 training subsets, after processing the data through the multi-channel ASR front-end, utilizing ground-truth diarization.\n - Fine-Tuning Details:\n - Fine-tuning Duration: 35,000 updates\n - Batch Size: 128",
"## 4. Language Model for ASR Decoding: KenLM Model",
"### ASR_LM_chime7_only.kenlm\n\n- This KenLM model is trained solely on CHiME7-DASR datasets (Mixer6, CHiME6, DipCo).\n- We apply a word-piece level N-gram language model using byte-pair-encoding (BPE) tokens.\n- This approach utilizes the SentencePiece and KenLM toolkits, based on the transcription of CHiME-7 train and dev sets.\n - SentencePiece: Kudo and Richardson (2018)\n - KenLM: KenLM GitRepo\n- The token sets of our ASR and LM models were matched to ensure consistency.\n- To combine several N-gram models with equal weights, we used the OpenGrm library.\n - OpenGrm: Roark et al. (2012)\n- MAES decoding was employed for the transducer, which accelerates the decoding process.\n - MAES Decoding: Kim et al. (2020)\n- As expected, integrating the beam-search decoder with the language model significantly enhances the performance of the end-to-end model compared to its pure counterpart."
] | [
"TAGS\n#nemo #arxiv-2310.12378 #arxiv-1904.08779 #arxiv-1510.08484 #arxiv-2203.15974 #arxiv-2110.04410 #arxiv-2005.08100 #arxiv-1808.06226 #region-us \n",
"# CHiME8 DASR NeMo Baseline Models\n\n- The model files in this repository are the models used in this paper The CHiME-7 Challenge: System Description and Performance of\nNeMo Team’s DASR System.\n- These models are needed to execute the CHiME8-DASR baseline CHiME8-DASR-Baseline NeMo\n- VAD, Diarization and ASR models are all based on NVIDIA NeMo Conversational AI Toolkits.",
"## 1. Voice Activity Detection (VAD) Model:",
"### MarbleNet_frame_VAD_chime7_Acrobat.nemo \n- This model is based on NeMo MarbleNet VAD model.\n- For validation, we use dataset comprises the CHiME-6 development subset as well as 50 hours of simulated audio data.\n- The simulated data is generated using the NeMo multi-speaker data simulator\non VoxCeleb1&2 datasets\n- The multi-speaker data simulation results in a total of 2,000 hours of audio, of which approximately 30% is silence.\n- The Model training incorporates SpecAugment and noise augmentation through MUSAN noise dataset.",
"## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)",
"### MSDD_v2_PALO_100ms_intrpl_3scales.nemo\n\n Our DASR system is based on the speaker diarization system using the multi-scale diarization decoder (MSDD).\n - MSDD Reference: Park et al. (2022)\n- MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.\n - TitaNet Reference: Koluguri et al. (2022)\n - TitaNet Model is included in MSDD-v2 .nemo checkpoint file.\n- Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.\n- This neural model generates logit values indicating speaker existence.\n- Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.\n - LibriSpeech Reference: OpenSLR Download,LibriSpeech, Panayotov et al. (2015)\n- MUSAN noise is also used for adding additive background noise, focusing on music and broadband noise.",
"## 3. Automatic Speech Recognition (ASR) model",
"### URL\n- This ASR model is based on NeMo FastConformer XL model. \n- Single-channel audio generated using a multi-channel front-end (Guided Source Separation, GSS) is transcribed using a 0.6B parameter Conformer-based transducer (RNNT) model.\n - Model Reference: Gulati et al. (2020)\n- The model was initialized using a publicly available NeMo checkpoint.\n - NeMo Checkpoint: NGC Model Card: Conformer Transducer XL\n- This model was then fine-tuned on the CHiME-7 train and dev set, which includes the CHiME-6 and Mixer6 training subsets, after processing the data through the multi-channel ASR front-end, utilizing ground-truth diarization.\n - Fine-Tuning Details:\n - Fine-tuning Duration: 35,000 updates\n - Batch Size: 128",
"## 4. Language Model for ASR Decoding: KenLM Model",
"### ASR_LM_chime7_only.kenlm\n\n- This KenLM model is trained solely on CHiME7-DASR datasets (Mixer6, CHiME6, DipCo).\n- We apply a word-piece level N-gram language model using byte-pair-encoding (BPE) tokens.\n- This approach utilizes the SentencePiece and KenLM toolkits, based on the transcription of CHiME-7 train and dev sets.\n - SentencePiece: Kudo and Richardson (2018)\n - KenLM: KenLM GitRepo\n- The token sets of our ASR and LM models were matched to ensure consistency.\n- To combine several N-gram models with equal weights, we used the OpenGrm library.\n - OpenGrm: Roark et al. (2012)\n- MAES decoding was employed for the transducer, which accelerates the decoding process.\n - MAES Decoding: Kim et al. (2020)\n- As expected, integrating the beam-search decoder with the language model significantly enhances the performance of the end-to-end model compared to its pure counterpart."
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266
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"passage: TAGS\n#nemo #arxiv-2310.12378 #arxiv-1904.08779 #arxiv-1510.08484 #arxiv-2203.15974 #arxiv-2110.04410 #arxiv-2005.08100 #arxiv-1808.06226 #region-us \n# CHiME8 DASR NeMo Baseline Models\n\n- The model files in this repository are the models used in this paper The CHiME-7 Challenge: System Description and Performance of\nNeMo Team’s DASR System.\n- These models are needed to execute the CHiME8-DASR baseline CHiME8-DASR-Baseline NeMo\n- VAD, Diarization and ASR models are all based on NVIDIA NeMo Conversational AI Toolkits.## 1. Voice Activity Detection (VAD) Model:### MarbleNet_frame_VAD_chime7_Acrobat.nemo \n- This model is based on NeMo MarbleNet VAD model.\n- For validation, we use dataset comprises the CHiME-6 development subset as well as 50 hours of simulated audio data.\n- The simulated data is generated using the NeMo multi-speaker data simulator\non VoxCeleb1&2 datasets\n- The multi-speaker data simulation results in a total of 2,000 hours of audio, of which approximately 30% is silence.\n- The Model training incorporates SpecAugment and noise augmentation through MUSAN noise dataset.## 2. Speaker Diarization Model: Multi-scale Diarization Decoder (MSDD-v2)"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | dmusingu/phi2tokenizer | [
"transformers",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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### Out-of-Scope Use
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
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## Technical Specifications [optional]
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null | null | peft |
# Model Card for Model ID
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot"} | null | adalib/sfepy-cond-gen-codeparrot-prefix | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
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## Model Card Contact
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null | null | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | reinforcement-learning | vpepe2003/ppo-SnowballTarget | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2024-02-15T01:13:58+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
50,
207
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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] |
null | null | null | [phi-2](https://huggingface.co/microsoft/phi-2) quantized using imatrix data from [kalomaze's groups_merged.txt](https://github.com/ggerganov/llama.cpp/files/14194570/groups_merged.txt)
Perplexities:
Q8_0: 5.3886
Q4_0: 5.5526
IQ3_XXS: 6.0745
IQ2_XS: 7.2570
IQ2_XXS: 9.3666
IQ1_S: 18.7885
| {} | null | ristew/phi-2-imatrix-gguf | [
"gguf",
"region:us"
] | 2024-02-15T01:22:09+00:00 | [] | [] | TAGS
#gguf #region-us
| phi-2 quantized using imatrix data from kalomaze's groups_merged.txt
Perplexities:
Q8_0: 5.3886
Q4_0: 5.5526
IQ3_XXS: 6.0745
IQ2_XS: 7.2570
IQ2_XXS: 9.3666
IQ1_S: 18.7885
| [] | [
"TAGS\n#gguf #region-us \n"
] | [
9
] | [
"passage: TAGS\n#gguf #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | question-answering | Peak1260/bert-finetuned-squad | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-15T01:25:53+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
60,
35,
6,
12,
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103,
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33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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null | null | transformers |
<!-- 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. -->
# mi-super-modelo
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6143
- Accuracy: 0.225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6863 | 0.5 | 5 | 1.6243 | 0.225 |
| 1.6154 | 1.0 | 10 | 1.6143 | 0.225 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "mi-super-modelo", "results": []}]} | text-classification | Hernan1970/mi-super-modelo | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T01:36:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mi-super-modelo
===============
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6143
* Accuracy: 0.225
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
67,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | null | BarraHome/Wistral-7B-Instruct-v0.3-gguf | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-15T01:37:37+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: BarraHome
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
70,
84
] | [
"passage: TAGS\n#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | null |
# Lora of marblehead/マーブルヘッド/马布尔黑德 (Azur Lane)
## What Is This?
This is the LoRA model of waifu marblehead/マーブルヘッド/马布尔黑德 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/marblehead_azurlane](https://huggingface.co/datasets/CyberHarem/marblehead_azurlane), which contains 177 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `marblehead_azurlane`.**
* Pruned core tags for this waifu are `blonde_hair, blue_eyes, breasts, hair_ornament, multicolored_hair, large_breasts, hairclip, pink_hair, two-tone_hair, hair_between_eyes, bangs, sidelocks`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 630, you need to download [`630/marblehead_azurlane.pt`](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/630/marblehead_azurlane.pt) as the embedding and [`630/marblehead_azurlane.safetensors`](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/630/marblehead_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 630.
1600 images (1.72 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | pattern_2_0 | pattern_2_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 630 | 15 | **0.982** | 0.967 | **0.841** | **0.729** | [Download](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/630/marblehead_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 405 | 10 | 0.982 | **0.987** | 0.840 | 0.727 | [Download](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/405/marblehead_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1215 | 28 | 0.972 | 0.973 | 0.835 | 0.708 | [Download](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/1215/marblehead_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1305 | 30 | 0.976 | 0.986 | 0.831 | 0.707 | [Download](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/1305/marblehead_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 720 | 17 | 0.973 | 0.982 | 0.829 | 0.701 | [Download](https://huggingface.co/CyberHarem/marblehead_azurlane/resolve/main/720/marblehead_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1395 to 1800](all/0.md)
* [Steps From 945 to 1350](all/1.md)
* [Steps From 495 to 900](all/2.md)
* [Steps From 45 to 450](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/marblehead_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/marblehead_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/marblehead_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T01:39:43+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marblehead_azurlane #license-mit #region-us
| Lora of marblehead/マーブルヘッド/马布尔黑德 (Azur Lane)
============================================
What Is This?
-------------
This is the LoRA model of waifu marblehead/マーブルヘッド/马布尔黑德 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/marblehead\_azurlane, which contains 177 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'marblehead\_azurlane'.
* Pruned core tags for this waifu are 'blonde\_hair, blue\_eyes, breasts, hair\_ornament, multicolored\_hair, large\_breasts, hairclip, pink\_hair, two-tone\_hair, hair\_between\_eyes, bangs, sidelocks'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 630, you need to download '630/marblehead\_azurlane.pt' as the embedding and '630/marblehead\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 630.
1600 images (1.72 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 1395 to 1800
* Steps From 945 to 1350
* Steps From 495 to 900
* Steps From 45 to 450
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 630, you need to download '630/marblehead\\_azurlane.pt' as the embedding and '630/marblehead\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 630.\n\n\n1600 images (1.72 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1395 to 1800\n* Steps From 945 to 1350\n* Steps From 495 to 900\n* Steps From 45 to 450"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marblehead_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 630, you need to download '630/marblehead\\_azurlane.pt' as the embedding and '630/marblehead\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 630.\n\n\n1600 images (1.72 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1395 to 1800\n* Steps From 945 to 1350\n* Steps From 495 to 900\n* Steps From 45 to 450"
] | [
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marblehead_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | longcule123/adapter-14-2_merged | [
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# Model Card for Model ID
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## How to Get Started with the Model
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## Training Details
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## Evaluation
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## Environmental Impact
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- Hardware Type:
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null | null | null |
# Lora of hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane)
## What Is This?
This is the LoRA model of waifu hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/hermann_kunne_azurlane](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane), which contains 68 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `hermann_kunne_azurlane`.**
* Pruned core tags for this waifu are `black_hair, long_hair, hat, bangs`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 700, you need to download [`700/hermann_kunne_azurlane.pt`](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/700/hermann_kunne_azurlane.pt) as the embedding and [`700/hermann_kunne_azurlane.safetensors`](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/700/hermann_kunne_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 700.
1440 images (1.50 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 700 | 42 | **0.858** | 0.982 | **0.854** | **0.819** | [Download](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/700/hermann_kunne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 780 | 46 | 0.838 | **0.986** | 0.846 | 0.788 | [Download](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/780/hermann_kunne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 620 | 37 | 0.850 | 0.962 | 0.836 | 0.774 | [Download](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/620/hermann_kunne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 560 | 33 | 0.799 | 0.979 | 0.847 | 0.759 | [Download](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/560/hermann_kunne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 740 | 44 | 0.822 | 0.951 | 0.833 | 0.744 | [Download](https://huggingface.co/CyberHarem/hermann_kunne_azurlane/resolve/main/740/hermann_kunne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/hermann_kunne_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/hermann_kunne_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/hermann_kunne_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T01:44:44+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hermann_kunne_azurlane #license-mit #region-us
| Lora of hermann\_kunne/ヘルマン・キュンネ/Z19 (Azur Lane)
================================================
What Is This?
-------------
This is the LoRA model of waifu hermann\_kunne/ヘルマン・キュンネ/Z19 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/hermann\_kunne\_azurlane, which contains 68 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'hermann\_kunne\_azurlane'.
* Pruned core tags for this waifu are 'black\_hair, long\_hair, hat, bangs'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 700, you need to download '700/hermann\_kunne\_azurlane.pt' as the embedding and '700/hermann\_kunne\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 700.
1440 images (1.50 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/hermann\\_kunne\\_azurlane.pt' as the embedding and '700/hermann\\_kunne\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1440 images (1.50 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hermann_kunne_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/hermann\\_kunne\\_azurlane.pt' as the embedding and '700/hermann\\_kunne\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1440 images (1.50 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
47,
38,
473
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hermann_kunne_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers |
<!-- 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_robust_stream_speaker_s2
This model was trained from scratch 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large_robust_stream_speaker_s2", "results": []}]} | null | apirbadian/wav2vec2-large_robust_stream_speaker_s2 | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | 2024-02-15T01:49:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-large_robust_stream_speaker_s2
This model was trained from scratch 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# wav2vec2-large_robust_stream_speaker_s2\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 12\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-large_robust_stream_speaker_s2\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 12\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
43,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #generated_from_trainer #endpoints_compatible #region-us \n# wav2vec2-large_robust_stream_speaker_s2\n\nThis model was trained from scratch on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 12\n- total_train_batch_size: 96\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 20\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | devashat/244-test | [
"transformers",
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"gpt2",
"text-generation",
"arxiv:1910.09700",
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# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Glossary [optional]
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fhermin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fhermin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Fhermin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 6),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 5),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "583.50 +/- 217.27", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Fhermin/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-15T01:55:23+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | null |
# Lora of unzen/雲仙/云仙 (Azur Lane)
## What Is This?
This is the LoRA model of waifu unzen/雲仙/云仙 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/unzen_azurlane](https://huggingface.co/datasets/CyberHarem/unzen_azurlane), which contains 341 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 11, resolution is 720x720, clustering into 20 buckets.
* Trained for 3440 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `unzen_azurlane`.**
* Pruned core tags for this waifu are `breasts, long_hair, large_breasts, white_hair, purple_eyes, hair_over_one_eye, multicolored_hair, bangs, streaked_hair, very_long_hair, ponytail`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 2150, you need to download [`2150/unzen_azurlane.pt`](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/2150/unzen_azurlane.pt) as the embedding and [`2150/unzen_azurlane.safetensors`](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/2150/unzen_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 2150.
1640 images (1.77 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | pattern_2_0 | pattern_2_1 | pattern_2_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 2150 | 26 | **0.985** | 0.944 | 0.844 | **0.696** | [Download](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/2150/unzen_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 516 | 7 | 0.971 | **0.981** | **0.859** | 0.693 | [Download](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/516/unzen_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 3268 | 39 | 0.983 | 0.912 | 0.836 | 0.683 | [Download](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/3268/unzen_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1204 | 15 | 0.978 | 0.906 | 0.840 | 0.683 | [Download](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/1204/unzen_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1376 | 17 | 0.977 | 0.942 | 0.839 | 0.680 | [Download](https://huggingface.co/CyberHarem/unzen_azurlane/resolve/main/1376/unzen_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 2666 to 3440](all/0.md)
* [Steps From 1806 to 2580](all/1.md)
* [Steps From 946 to 1720](all/2.md)
* [Steps From 86 to 860](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/unzen_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/unzen_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/unzen_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T01:57:20+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/unzen_azurlane #license-mit #region-us
| Lora of unzen/雲仙/云仙 (Azur Lane)
===============================
What Is This?
-------------
This is the LoRA model of waifu unzen/雲仙/云仙 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/unzen\_azurlane, which contains 341 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 11, resolution is 720x720, clustering into 20 buckets.
* Trained for 3440 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'unzen\_azurlane'.
* Pruned core tags for this waifu are 'breasts, long\_hair, large\_breasts, white\_hair, purple\_eyes, hair\_over\_one\_eye, multicolored\_hair, bangs, streaked\_hair, very\_long\_hair, ponytail'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 2150, you need to download '2150/unzen\_azurlane.pt' as the embedding and '2150/unzen\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 2150.
1640 images (1.77 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 2666 to 3440
* Steps From 1806 to 2580
* Steps From 946 to 1720
* Steps From 86 to 860
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 2150, you need to download '2150/unzen\\_azurlane.pt' as the embedding and '2150/unzen\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 2150.\n\n\n1640 images (1.77 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2666 to 3440\n* Steps From 1806 to 2580\n* Steps From 946 to 1720\n* Steps From 86 to 860"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/unzen_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 2150, you need to download '2150/unzen\\_azurlane.pt' as the embedding and '2150/unzen\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 2150.\n\n\n1640 images (1.77 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2666 to 3440\n* Steps From 1806 to 2580\n* Steps From 946 to 1720\n* Steps From 86 to 860"
] | [
44,
38,
475
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/unzen_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | transformers | ## DAVinCI-42dot_LLM-PLM-1.3B-v1.1
This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) on a custom 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: 24
* eval_batch_size: 8
* seed: 42
* gradient_accumulation_steps: 4
* total_train_batch_size: 96
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr_scheduler_type: linear
* num_epochs: 1.0
* mixed_precision_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
| {"license": "cc-by-nc-4.0"} | text-generation | jungyuko/DAVinCI-42dot_LLM-PLM-1.3B-v1.1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T01:58:16+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## DAVinCI-42dot_LLM-PLM-1.3B-v1.1
This model is a fine-tuned version of 42dot/42dot_LLM-PLM-1.3B on a custom 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: 24
* eval_batch_size: 8
* seed: 42
* gradient_accumulation_steps: 4
* total_train_batch_size: 96
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr_scheduler_type: linear
* num_epochs: 1.0
* mixed_precision_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
| [
"## DAVinCI-42dot_LLM-PLM-1.3B-v1.1\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-PLM-1.3B on a custom dataset.",
"### Model description\nMore information needed",
"### Intended uses & limitations\nMore information needed",
"### Training and evaluation data\nMore information needed",
"### Training procedure",
"### Training hyperparameters\nThe following hyperparameters were used during training:\n* learning_rate: 2e-05\n* train_batch_size: 24\n* eval_batch_size: 8\n* seed: 42\n* gradient_accumulation_steps: 4\n* total_train_batch_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr_scheduler_type: linear\n* num_epochs: 1.0\n* mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.0.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## DAVinCI-42dot_LLM-PLM-1.3B-v1.1\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-PLM-1.3B on a custom dataset.",
"### Model description\nMore information needed",
"### Intended uses & limitations\nMore information needed",
"### Training and evaluation data\nMore information needed",
"### Training procedure",
"### Training hyperparameters\nThe following hyperparameters were used during training:\n* learning_rate: 2e-05\n* train_batch_size: 24\n* eval_batch_size: 8\n* seed: 42\n* gradient_accumulation_steps: 4\n* total_train_batch_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr_scheduler_type: linear\n* num_epochs: 1.0\n* mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.0.0\n* Tokenizers 0.15.0"
] | [
58,
49,
7,
13,
9,
4,
126,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## DAVinCI-42dot_LLM-PLM-1.3B-v1.1\n\nThis model is a fine-tuned version of 42dot/42dot_LLM-PLM-1.3B on a custom dataset.### Model description\nMore information needed### Intended uses & limitations\nMore information needed### Training and evaluation data\nMore information needed### Training procedure### Training hyperparameters\nThe following hyperparameters were used during training:\n* learning_rate: 2e-05\n* train_batch_size: 24\n* eval_batch_size: 8\n* seed: 42\n* gradient_accumulation_steps: 4\n* total_train_batch_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr_scheduler_type: linear\n* num_epochs: 1.0\n* mixed_precision_training: Native AMP### Training results### Framework versions\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.0.0\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | feature-extraction | furrutiav/bert_qa_extractor_cockatiel_2022_ulra_two_signal_z_value_mixtral_v2_it_107 | [
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|
# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "256.99 +/- 16.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | kaljr/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-15T02:06:29+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | null |
# Lora of atlanta/アトランタ/亚特兰大 (Azur Lane)
## What Is This?
This is the LoRA model of waifu atlanta/アトランタ/亚特兰大 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/atlanta_azurlane](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane), which contains 37 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `atlanta_azurlane`.**
* Pruned core tags for this waifu are `pink_hair, blue_eyes, braid, long_hair, ahoge, bangs, crown_braid, black_ribbon, hair_ribbon, ribbon, breasts, hair_ornament, hair_between_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 680, you need to download [`680/atlanta_azurlane.pt`](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/680/atlanta_azurlane.pt) as the embedding and [`680/atlanta_azurlane.safetensors`](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/680/atlanta_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 680.
1480 images (1.61 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 680 | 74 | 0.931 | **0.976** | 0.841 | **0.723** | [Download](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/680/atlanta_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 560 | 61 | 0.912 | 0.975 | 0.844 | 0.718 | [Download](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/560/atlanta_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 580 | 63 | 0.907 | 0.967 | **0.845** | 0.716 | [Download](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/580/atlanta_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 700 | 76 | **0.934** | 0.975 | 0.836 | 0.716 | [Download](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/700/atlanta_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 500 | 55 | 0.912 | 0.968 | 0.841 | 0.713 | [Download](https://huggingface.co/CyberHarem/atlanta_azurlane/resolve/main/500/atlanta_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/atlanta_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/atlanta_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/atlanta_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T02:10:01+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/atlanta_azurlane #license-mit #region-us
| Lora of atlanta/アトランタ/亚特兰大 (Azur Lane)
======================================
What Is This?
-------------
This is the LoRA model of waifu atlanta/アトランタ/亚特兰大 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/atlanta\_azurlane, which contains 37 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'atlanta\_azurlane'.
* Pruned core tags for this waifu are 'pink\_hair, blue\_eyes, braid, long\_hair, ahoge, bangs, crown\_braid, black\_ribbon, hair\_ribbon, ribbon, breasts, hair\_ornament, hair\_between\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 680, you need to download '680/atlanta\_azurlane.pt' as the embedding and '680/atlanta\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 680.
1480 images (1.61 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 680, you need to download '680/atlanta\\_azurlane.pt' as the embedding and '680/atlanta\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 680.\n\n\n1480 images (1.61 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/atlanta_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 680, you need to download '680/atlanta\\_azurlane.pt' as the embedding and '680/atlanta\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 680.\n\n\n1480 images (1.61 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
44,
38,
467
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/atlanta_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3276
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 479 | 2.3738 |
| 2.5112 | 2.0 | 958 | 2.3320 |
| 2.5112 | 3.0 | 1437 | 2.3276 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | fill-mask | sjunique/distilbert-base-uncased-finetuned-imdb | [
"transformers",
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"safetensors",
"distilbert",
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"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:10:34+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3276
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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu118
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers | # merged
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
Made as a test model, not sure about quality, probably will not make any quants unless someone finds out it's good and asks.
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) as a base.
### Models Merged
The following models were included in the merge:
* [Masterjp123/SnowyRP-FinalV1-L2-13B](https://huggingface.co/Masterjp123/SnowyRP-FinalV1-L2-13B)
* [posicube/Llama2-chat-AYB-13B](https://huggingface.co/posicube/Llama2-chat-AYB-13B)
* [Sao10K/Stheno-1.8-L2-13B](https://huggingface.co/Sao10K/Stheno-1.8-L2-13B)
* [ValiantLabs/ShiningValiantXS](https://huggingface.co/ValiantLabs/ShiningValiantXS)
* [sauce1337/BerrySauce-L2-13b](https://huggingface.co/sauce1337/BerrySauce-L2-13b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model:
model:
path: TheBloke/Llama-2-13B-fp16
dtype: bfloat16
merge_method: ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 40]
model:
model:
path: Masterjp123/Snowyrp-V2B-P1
parameters:
density: [1.0, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 40]
model:
model:
path: Masterjp123/SnowyRP-FinalV1-L2-13B
parameters:
density: 0.5
weight: [0.0, 0.3, 0.7, 1.0]
- layer_range: [0, 40]
model:
model:
path: sauce1337/BerrySauce-L2-13b
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0.0
- layer_range: [0, 40]
model:
model:
path: TheBloke/Llama-2-13B-fp16
```
for Masterjp123/Snowyrp-V2B-P1
```yaml
base_model:
model:
path: TheBloke/Llama-2-13B-fp16
dtype: bfloat16
merge_method: ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 40]
model:
model:
path: Sao10K/Stheno-1.8-L2-13B
parameters:
density: [1.0, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 40]
model:
model:
path: ValiantLabs/ShiningValiantXS
parameters:
density: 0.5
weight: [0.0, 0.3, 0.7, 1.0]
- layer_range: [0, 40]
model:
model:
path: posicube/Llama2-chat-AYB-13B
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0.0
- layer_range: [0, 40]
model:
model:
path: TheBloke/Llama-2-13B-fp16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["TheBloke/Llama-2-13B-fp16", "Masterjp123/SnowyRP-FinalV1-L2-13B", "Masterjp123/Snowyrp-V2B-P1", "sauce1337/BerrySauce-L2-13b"]} | text-generation | Masterjp123/SnowyRP-V2-13B-L2_BetaTest | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:TheBloke/Llama-2-13B-fp16",
"base_model:Masterjp123/SnowyRP-FinalV1-L2-13B",
"base_model:Masterjp123/Snowyrp-V2B-P1",
"base_model:sauce1337/BerrySauce-L2-13b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:12:00+00:00 | [
"2306.01708"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-Masterjp123/SnowyRP-FinalV1-L2-13B #base_model-Masterjp123/Snowyrp-V2B-P1 #base_model-sauce1337/BerrySauce-L2-13b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merged
This is a merge of pre-trained language models created using mergekit.
## Merge Details
Made as a test model, not sure about quality, probably will not make any quants unless someone finds out it's good and asks.
### Merge Method
This model was merged using the TIES merge method using TheBloke/Llama-2-13B-fp16 as a base.
### Models Merged
The following models were included in the merge:
* Masterjp123/SnowyRP-FinalV1-L2-13B
* posicube/Llama2-chat-AYB-13B
* Sao10K/Stheno-1.8-L2-13B
* ValiantLabs/ShiningValiantXS
* sauce1337/BerrySauce-L2-13b
### Configuration
The following YAML configuration was used to produce this model:
for Masterjp123/Snowyrp-V2B-P1
| [
"# merged\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details\n\nMade as a test model, not sure about quality, probably will not make any quants unless someone finds out it's good and asks.",
"### Merge Method\n\nThis model was merged using the TIES merge method using TheBloke/Llama-2-13B-fp16 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* Masterjp123/SnowyRP-FinalV1-L2-13B\n* posicube/Llama2-chat-AYB-13B\n* Sao10K/Stheno-1.8-L2-13B\n* ValiantLabs/ShiningValiantXS\n* sauce1337/BerrySauce-L2-13b",
"### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\nfor Masterjp123/Snowyrp-V2B-P1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-Masterjp123/SnowyRP-FinalV1-L2-13B #base_model-Masterjp123/Snowyrp-V2B-P1 #base_model-sauce1337/BerrySauce-L2-13b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merged\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details\n\nMade as a test model, not sure about quality, probably will not make any quants unless someone finds out it's good and asks.",
"### Merge Method\n\nThis model was merged using the TIES merge method using TheBloke/Llama-2-13B-fp16 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* Masterjp123/SnowyRP-FinalV1-L2-13B\n* posicube/Llama2-chat-AYB-13B\n* Sao10K/Stheno-1.8-L2-13B\n* ValiantLabs/ShiningValiantXS\n* sauce1337/BerrySauce-L2-13b",
"### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\nfor Masterjp123/Snowyrp-V2B-P1"
] | [
142,
19,
35,
35,
88,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-Masterjp123/SnowyRP-FinalV1-L2-13B #base_model-Masterjp123/Snowyrp-V2B-P1 #base_model-sauce1337/BerrySauce-L2-13b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merged\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details\n\nMade as a test model, not sure about quality, probably will not make any quants unless someone finds out it's good and asks.### Merge Method\n\nThis model was merged using the TIES merge method using TheBloke/Llama-2-13B-fp16 as a base.### Models Merged\n\nThe following models were included in the merge:\n* Masterjp123/SnowyRP-FinalV1-L2-13B\n* posicube/Llama2-chat-AYB-13B\n* Sao10K/Stheno-1.8-L2-13B\n* ValiantLabs/ShiningValiantXS\n* sauce1337/BerrySauce-L2-13b### Configuration\n\nThe following YAML configuration was used to produce this model:\n\n\nfor Masterjp123/Snowyrp-V2B-P1"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large Korean/English
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8019
- Wer: 198.2263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 773
- training_steps: 7728
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5546 | 1.0 | 773 | 0.5308 | 240.1463 |
| 0.3284 | 2.0 | 1546 | 0.5160 | 133.6395 |
| 0.176 | 3.0 | 2319 | 0.5582 | 264.5033 |
| 0.0977 | 4.0 | 3092 | 0.6110 | 155.6417 |
| 0.065 | 5.0 | 3865 | 0.6577 | 194.4118 |
| 0.0298 | 6.0 | 4638 | 0.7021 | 235.0691 |
| 0.0109 | 7.0 | 5411 | 0.7408 | 158.8282 |
| 0.0069 | 8.0 | 6184 | 0.7550 | 201.9574 |
| 0.0057 | 9.0 | 6957 | 0.8019 | 198.2263 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "Whisper Large Korean/English", "results": []}]} | automatic-speech-recognition | gcasey2/whisper-large-v3-ko-en | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:12:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-large-v3 #license-apache-2.0 #endpoints_compatible #region-us
| Whisper Large Korean/English
============================
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8019
* Wer: 198.2263
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 773
* training\_steps: 7728
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 773\n* training\\_steps: 7728\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 773\n* training\\_steps: 7728\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
72,
132,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-large-v3 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 773\n* training\\_steps: 7728\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "mistralai/Mixtral-8x7B-v0.1"} | null | Krisbiantoro/mixtral-id-llama-1500 | [
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"tensorboard",
"safetensors",
"arxiv:1910.09700",
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#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers | This model is [sparsetral-16x7B-v2](https://huggingface.co/serpdotai/sparsetral-16x7B-v2) further tuned utilizing [SPIN](https://arxiv.org/abs/2401.01335) on [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) mixed with traditional DPO samples. This is iteration_0, plan to keep making iterations until improvements stop.
Kuru~ Kuru~
## Training
- 8x A6000s
- Base model is [sparsetral-16x7B-v2](https://huggingface.co/serpdotai/sparsetral-16x7B-v2)
- [Forked version of unsloth](https://github.com/serp-ai/unsloth) for efficient training
- Sequence Length: 4096
- Effective batch size: 64
- Learning Rate: 5e-7 with linear decay (0.1 warmup ratio)
- Epochs: 2
- 50k samples (~15k traditional dpo samples, rest SPIN)
- QLoRA:
- 256 r and 256 alpha
- ```python
target_modules=[
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"adapter_down",
"adapter_up",
]
```
## Prompt Format
```
<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n
```
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("serpdotai/sparsetral-16x7B-v2-SPIN_iter0", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v2-SPIN_iter0", device_map="auto", trust_remote_code=True).eval()
system_str = "<|im_start|>system\n{message}<|im_end|>\n"
user_str = "<|im_start|>user\n{message}<|im_end|>\n"
assistant_str = "<|im_start|>assistant\n{message}<|im_end|>\n"
def construct_prompt(messages):
prompt = ""
for message in messages:
if message["from"] in ["human", "user"]:
prompt += user_str.format(
message=message["value"]
)
elif message["from"] in ["gpt", "assistant"]:
prompt += assistant_str.format(
message=message["value"]
)
elif message["from"] in ["system", "instruction"]:
prompt += system_str.format(
message=message["value"]
)
else:
raise ValueError(
f"Unknown message type: {message['from']}"
)
return prompt + "<|im_start|>assistant\n"
system = "You are a helpful assistant who will help the user to the best of their ability. If you don't know something, say \"I don't know\""
user = "Are you sentient?"
messages = [
{"from": "system", "value": system},
{"from": "user", "value": user},
]
prompt = construct_prompt(messages)
inputs = tokenizer(prompt, return_tensors="pt")
inputs = inputs.to(model.device)
pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Other Information
Paper reference: [Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731)
[Original Paper repo](https://github.com/wuhy68/Parameter-Efficient-MoE)
[Forked repo with mistral support (sparsetral)](https://github.com/serp-ai/Parameter-Efficient-MoE)
If you are interested in faster inferencing, check out our [fork of vLLM](https://github.com/serp-ai/vllm) that adds sparsetral support | {"language": ["en"], "license": "apache-2.0", "datasets": ["teknium/OpenHermes-2.5", "jondurbin/truthy-dpo-v0.1", "jondurbin/gutenberg-dpo-v0.1", "argilla/dpo-mix-7k"]} | text-generation | serpdotai/sparsetral-16x7B-v2-SPIN_iter0 | [
"transformers",
"safetensors",
"sparsetral",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:argilla/dpo-mix-7k",
"arxiv:2401.01335",
"arxiv:2401.02731",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:14:42+00:00 | [
"2401.01335",
"2401.02731"
] | [
"en"
] | TAGS
#transformers #safetensors #sparsetral #text-generation #conversational #custom_code #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #arxiv-2401.01335 #arxiv-2401.02731 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is sparsetral-16x7B-v2 further tuned utilizing SPIN on OpenHermes-2.5 mixed with traditional DPO samples. This is iteration_0, plan to keep making iterations until improvements stop.
Kuru~ Kuru~
!Kuru~ Kuru~
## Training
- 8x A6000s
- Base model is sparsetral-16x7B-v2
- Forked version of unsloth for efficient training
- Sequence Length: 4096
- Effective batch size: 64
- Learning Rate: 5e-7 with linear decay (0.1 warmup ratio)
- Epochs: 2
- 50k samples (~15k traditional dpo samples, rest SPIN)
- QLoRA:
- 256 r and 256 alpha
-
## Prompt Format
## Usage
## Other Information
Paper reference: Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
Original Paper repo
Forked repo with mistral support (sparsetral)
If you are interested in faster inferencing, check out our fork of vLLM that adds sparsetral support | [
"## Training\n- 8x A6000s\n- Base model is sparsetral-16x7B-v2\n- Forked version of unsloth for efficient training\n- Sequence Length: 4096\n- Effective batch size: 64\n- Learning Rate: 5e-7 with linear decay (0.1 warmup ratio)\n- Epochs: 2\n- 50k samples (~15k traditional dpo samples, rest SPIN)\n- QLoRA:\n - 256 r and 256 alpha\n -",
"## Prompt Format",
"## Usage",
"## Other Information\nPaper reference: Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks\n\nOriginal Paper repo\n\nForked repo with mistral support (sparsetral)\n\nIf you are interested in faster inferencing, check out our fork of vLLM that adds sparsetral support"
] | [
"TAGS\n#transformers #safetensors #sparsetral #text-generation #conversational #custom_code #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #arxiv-2401.01335 #arxiv-2401.02731 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Training\n- 8x A6000s\n- Base model is sparsetral-16x7B-v2\n- Forked version of unsloth for efficient training\n- Sequence Length: 4096\n- Effective batch size: 64\n- Learning Rate: 5e-7 with linear decay (0.1 warmup ratio)\n- Epochs: 2\n- 50k samples (~15k traditional dpo samples, rest SPIN)\n- QLoRA:\n - 256 r and 256 alpha\n -",
"## Prompt Format",
"## Usage",
"## Other Information\nPaper reference: Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks\n\nOriginal Paper repo\n\nForked repo with mistral support (sparsetral)\n\nIf you are interested in faster inferencing, check out our fork of vLLM that adds sparsetral support"
] | [
133,
105,
5,
3,
80
] | [
"passage: TAGS\n#transformers #safetensors #sparsetral #text-generation #conversational #custom_code #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #arxiv-2401.01335 #arxiv-2401.02731 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Training\n- 8x A6000s\n- Base model is sparsetral-16x7B-v2\n- Forked version of unsloth for efficient training\n- Sequence Length: 4096\n- Effective batch size: 64\n- Learning Rate: 5e-7 with linear decay (0.1 warmup ratio)\n- Epochs: 2\n- 50k samples (~15k traditional dpo samples, rest SPIN)\n- QLoRA:\n - 256 r and 256 alpha\n -## Prompt Format## Usage## Other Information\nPaper reference: Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks\n\nOriginal Paper repo\n\nForked repo with mistral support (sparsetral)\n\nIf you are interested in faster inferencing, check out our fork of vLLM that adds sparsetral support"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-en-nonnative-maritime
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 34.0415
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0001 | 142.86 | 1000 | 0.0001 | 39.4007 |
| 0.0001 | 285.71 | 2000 | 0.0001 | 39.2263 |
| 0.0 | 428.57 | 3000 | 0.0000 | 36.6735 |
| 0.0 | 571.43 | 4000 | 0.0000 | 35.1039 |
| 0.0 | 714.29 | 5000 | 0.0000 | 34.0415 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-small-en-nonnative-maritime", "results": []}]} | automatic-speech-recognition | vishakha-lall/whisper-small-en-nonnative-maritime | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:14:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| whisper-small-en-nonnative-maritime
===================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Wer: 34.0415
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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130,
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
- Finetuned [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat), with SFT on teknium's OpenHermes-2.5 dataset.
- This marks the inception of my Qwen1.5 LLM series, with this model laying the foundation for what lies ahead.
- Format: ChatML
- ```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Next step would be to do a DPO train on top.
## Benchamrks:
|Avg. | Arc | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|--|--|--|--|--|--|--|
|41.46 | 35.24 |60.42 | 45.37 | 41.4 | 60.85 | 5.46 |
## Example:
```
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
import torch
class MyStoppingCriteria(StoppingCriteria):
def __init__(self, target_sequence, prompt):
self.target_sequence = target_sequence
self.prompt=prompt
def __call__(self, input_ids, scores, **kwargs):
generated_text = tokenizer.decode(input_ids[0])
generated_text = generated_text.replace(self.prompt,'')
if self.target_sequence in generated_text:
return True
return False
def __len__(self):
return 1
def __iter__(self):
yield self
modelpath="aloobun/Reyna-Mini-1.8B-v0.1"
model = AutoModelForCausalLM.from_pretrained(
modelpath,
torch_dtype=torch.bfloat16,
device_map="cuda",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
modelpath,
trust_remote_code=True,
use_fast=False,
)
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nReflect on a real-world scenario where understanding probability theory could make a significant difference in decision-making.\n<|im_start|>assistant\n"
encoded_input = tokenizer(prompt, return_tensors='pt')
input_ids=encoded_input['input_ids'].cuda()
streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
op = model.generate(
input_ids,
streamer=streamer,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.6,
top_p=0.8,
max_new_tokens=512,
stopping_criteria=MyStoppingCriteria("<|im_end|>", prompt)
)
```
## Output:
> One real-world scenario where understanding probability theory can make a significant difference in decision-making is in the field of finance. Financial institutions, such as banks and investment firms, must make decisions about lending money to individuals or businesses, and how much risk they should take on.
> In this case, understanding probability theory would help financial analysts and investors make more informed decisions by providing them with information about the likelihood of different outcomes. For example, if an investor wants to invest in a particular stock, they might want to understand the probability that it will perform well over time, based on historical data and market trends. They might also be interested in understanding the probability of defaulting on a loan, which would help them evaluate whether it's worth taking on that risk.
> Probability theory provides valuable insights into how events are likely to occur and what factors contribute to those probabilities. By using statistical models and simulations, financial professionals can estimate the likelihood of different scenarios and make better-informed decisions about how to allocate their resources. This can lead to increased profits for financial institutions and improved customer satisfaction for individual investors.<|im_end|> | {"license": "other", "library_name": "transformers", "tags": ["chatml", "finetune", "gpt4", "synthetic data", "custom_code", "qwen2"], "datasets": ["teknium/OpenHermes-2.5"], "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE"} | text-generation | aloobun/Reyna-Mini-1.8B-v0.1 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chatml",
"finetune",
"gpt4",
"synthetic data",
"custom_code",
"conversational",
"dataset:teknium/OpenHermes-2.5",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:15:13+00:00 | [] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #chatml #finetune #gpt4 #synthetic data #custom_code #conversational #dataset-teknium/OpenHermes-2.5 #license-other #autotrain_compatible #endpoints_compatible #region-us
| !Reyna aloobun qwen0.5B
* Finetuned Qwen/Qwen1.5-1.8B-Chat, with SFT on teknium's OpenHermes-2.5 dataset.
* This marks the inception of my Qwen1.5 LLM series, with this model laying the foundation for what lies ahead.
* Format: ChatML
--------------
* Next step would be to do a DPO train on top.
Benchamrks:
-----------
Example:
--------
Output:
-------
>
> One real-world scenario where understanding probability theory can make a significant difference in decision-making is in the field of finance. Financial institutions, such as banks and investment firms, must make decisions about lending money to individuals or businesses, and how much risk they should take on.
> In this case, understanding probability theory would help financial analysts and investors make more informed decisions by providing them with information about the likelihood of different outcomes. For example, if an investor wants to invest in a particular stock, they might want to understand the probability that it will perform well over time, based on historical data and market trends. They might also be interested in understanding the probability of defaulting on a loan, which would help them evaluate whether it's worth taking on that risk.
> Probability theory provides valuable insights into how events are likely to occur and what factors contribute to those probabilities. By using statistical models and simulations, financial professionals can estimate the likelihood of different scenarios and make better-informed decisions about how to allocate their resources. This can lead to increased profits for financial institutions and improved customer satisfaction for individual investors.<|im\_end|>
>
>
>
| [] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #chatml #finetune #gpt4 #synthetic data #custom_code #conversational #dataset-teknium/OpenHermes-2.5 #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
80
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #chatml #finetune #gpt4 #synthetic data #custom_code #conversational #dataset-teknium/OpenHermes-2.5 #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "248.05 +/- 26.13", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Noname08/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-15T02:22:44+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text-generation | sajedjalil/mistral-pregnancy-instruct | [
"transformers",
"tensorboard",
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"4-bit",
"region:us"
] | 2024-02-15T02:24:33+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #tensorboard #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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null | null | null |
<!-- 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 [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
- Model creator: [CultriX](https://huggingface.co/CultriX)
- Original model: [NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
- Model description: [Readme](https://huggingface.co/CultriX/NeuralTrix-7B-dpo/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
| {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus"], "model_name": "NeuralTrix-7B-dpo", "base_model": "CultriX/NeuralTrix-7B-dpo", "model_creator": "CultriX", "quantized_by": "JanHQ"} | null | janhq/neuraltrix-7b-dpo-GGUF | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/OmniBeagle-7B",
"flemmingmiguel/MBX-7B-v3",
"AiMavenAi/AiMaven-Prometheus",
"base_model:CultriX/NeuralTrix-7B-dpo",
"license:apache-2.0",
"region:us"
] | 2024-02-15T02:25:21+00:00 | [] | [] | TAGS
#gguf #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #region-us
|
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="URL alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="URL
- <a href="URL
</p>
# Model Description
This is a GGUF version of CultriX/NeuralTrix-7B-dpo
- Model creator: CultriX
- Original model: NeuralTrix-7B-dpo
- Model description: Readme
# 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](URL 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
| [
"# Model Description\nThis is a GGUF version of CultriX/NeuralTrix-7B-dpo\n- Model creator: CultriX\n- Original model: NeuralTrix-7B-dpo\n- Model description: Readme",
"# About Jan\nJan 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.\n\nJan'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\nThis is a repository for the [open-source converter](URL 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"
] | [
"TAGS\n#gguf #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #region-us \n",
"# Model Description\nThis is a GGUF version of CultriX/NeuralTrix-7B-dpo\n- Model creator: CultriX\n- Original model: NeuralTrix-7B-dpo\n- Model description: Readme",
"# About Jan\nJan 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.\n\nJan'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\nThis is a repository for the [open-source converter](URL 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"
] | [
87,
50,
77,
53
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"passage: TAGS\n#gguf #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-CultriX/NeuralTrix-7B-dpo #license-apache-2.0 #region-us \n# Model Description\nThis is a GGUF version of CultriX/NeuralTrix-7B-dpo\n- Model creator: CultriX\n- Original model: NeuralTrix-7B-dpo\n- Model description: Readme# About Jan\nJan 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.\n\nJan'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\nThis is a repository for the [open-source converter](URL 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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null | null | transformers |
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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null | null | transformers | # Model Card for Zenith-7B-dpo-v2
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B-dpo-v2")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B-dpo-v2")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "Zenith-7B-dpo-v2"], "pipeline_tag": "text-generation"} | text-generation | Xenon1/Zenith-7B-dpo-v2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Zenith-7B-dpo-v2",
"conversational",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:26:35+00:00 | [
"2401.10020"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v2 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Zenith-7B-dpo-v2
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | [
"# Model Card for Zenith-7B-dpo-v2\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v2 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Zenith-7B-dpo-v2\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
82,
50,
105,
56
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v2 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Zenith-7B-dpo-v2\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
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null | null | transformers |
# Model Card for Model ID
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"text-generation",
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# Model Card for Model ID
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## Uses
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Use the code below to get started with the model.
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## Evaluation
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#### Factors
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] |
null | null | diffusers |
# Textual inversion text2image fine-tuning - janetsw/textual_inversion_cat
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | text-to-image | janetsw/textual_inversion_cat | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-15T02:28:53+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Textual inversion text2image fine-tuning - janetsw/textual_inversion_cat
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| [
"# Textual inversion text2image fine-tuning - janetsw/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - janetsw/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
101,
61
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# Textual inversion text2image fine-tuning - janetsw/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
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] |
null | null | null |
# Lora of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane)
## What Is This?
This is the LoRA model of waifu oklahoma/オクラホマ/俄克拉荷马 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/oklahoma_azurlane](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane), which contains 65 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `oklahoma_azurlane`.**
* Pruned core tags for this waifu are `ahoge, blue_eyes, breasts, hair_between_eyes, blonde_hair, short_hair, bangs, large_breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 500, you need to download [`500/oklahoma_azurlane.pt`](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/500/oklahoma_azurlane.pt) as the embedding and [`500/oklahoma_azurlane.safetensors`](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/500/oklahoma_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 500.
1440 images (1.43 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-------------------------------------------------------------------------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 500 | 31 | **0.774** | 0.954 | 0.849 | **0.782** | [Download](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/500/oklahoma_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 680 | 42 | 0.690 | 0.970 | **0.858** | 0.716 | [Download](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/680/oklahoma_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 300 | 19 | 0.688 | **0.977** | 0.858 | 0.713 | [Download](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/300/oklahoma_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 440 | 28 | 0.683 | 0.960 | 0.852 | 0.700 | [Download](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/440/oklahoma_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 700 | 44 | 0.676 | 0.953 | 0.856 | 0.699 | [Download](https://huggingface.co/CyberHarem/oklahoma_azurlane/resolve/main/700/oklahoma_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/oklahoma_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/oklahoma_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/oklahoma_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T02:29:28+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/oklahoma_azurlane #license-mit #region-us
| Lora of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane)
========================================
What Is This?
-------------
This is the LoRA model of waifu oklahoma/オクラホマ/俄克拉荷马 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/oklahoma\_azurlane, which contains 65 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'oklahoma\_azurlane'.
* Pruned core tags for this waifu are 'ahoge, blue\_eyes, breasts, hair\_between\_eyes, blonde\_hair, short\_hair, bangs, large\_breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 500, you need to download '500/oklahoma\_azurlane.pt' as the embedding and '500/oklahoma\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 500.
1440 images (1.43 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 500, you need to download '500/oklahoma\\_azurlane.pt' as the embedding and '500/oklahoma\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 500.\n\n\n1440 images (1.43 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/oklahoma_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 500, you need to download '500/oklahoma\\_azurlane.pt' as the embedding and '500/oklahoma\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 500.\n\n\n1440 images (1.43 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
45,
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467
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/oklahoma_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | transformers |
# ultra0-reshaped1
ultra0-reshaped1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [liminerity/ultra0-reshaped](https://huggingface.co/liminerity/ultra0-reshaped)
* [eren23/dpo-binarized-NeutrixOmnibe-7b](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/ultra0-reshaped
layer_range: [0, 24]
- model: eren23/dpo-binarized-NeutrixOmnibe-7b
layer_range: [0, 24]
merge_method: slerp
base_model: liminerity/ultra0-reshaped1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/ultra0-reshaped1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "liminerity/ultra0-reshaped", "eren23/dpo-binarized-NeutrixOmnibe-7b"], "base_model": ["liminerity/ultra0-reshaped", "eren23/dpo-binarized-NeutrixOmnibe-7b"]} | text-generation | liminerity/ultra0-reshaped1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"liminerity/ultra0-reshaped",
"eren23/dpo-binarized-NeutrixOmnibe-7b",
"conversational",
"base_model:liminerity/ultra0-reshaped",
"base_model:eren23/dpo-binarized-NeutrixOmnibe-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:30:33+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/ultra0-reshaped #eren23/dpo-binarized-NeutrixOmnibe-7b #conversational #base_model-liminerity/ultra0-reshaped #base_model-eren23/dpo-binarized-NeutrixOmnibe-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ultra0-reshaped1
ultra0-reshaped1 is a merge of the following models using LazyMergekit:
* liminerity/ultra0-reshaped
* eren23/dpo-binarized-NeutrixOmnibe-7b
## Configuration
## Usage
| [
"# ultra0-reshaped1\n\nultra0-reshaped1 is a merge of the following models using LazyMergekit:\n* liminerity/ultra0-reshaped\n* eren23/dpo-binarized-NeutrixOmnibe-7b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/ultra0-reshaped #eren23/dpo-binarized-NeutrixOmnibe-7b #conversational #base_model-liminerity/ultra0-reshaped #base_model-eren23/dpo-binarized-NeutrixOmnibe-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ultra0-reshaped1\n\nultra0-reshaped1 is a merge of the following models using LazyMergekit:\n* liminerity/ultra0-reshaped\n* eren23/dpo-binarized-NeutrixOmnibe-7b",
"## Configuration",
"## Usage"
] | [
134,
61,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/ultra0-reshaped #eren23/dpo-binarized-NeutrixOmnibe-7b #conversational #base_model-liminerity/ultra0-reshaped #base_model-eren23/dpo-binarized-NeutrixOmnibe-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ultra0-reshaped1\n\nultra0-reshaped1 is a merge of the following models using LazyMergekit:\n* liminerity/ultra0-reshaped\n* eren23/dpo-binarized-NeutrixOmnibe-7b## Configuration## Usage"
] | [
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] |
null | null | transformers | # Model Card for Zenith-7B-dpo-v3
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Results
| model_name | Average | arc_challenge | hellaswag | truthfulqa_mc2 | winogrande |
|:-----------------|----------:|----------------:|------------:|-----------------:|-------------:|
| Zenith-7B-dpo-v3 | 0.707576 | 0.613481 | 0.848337 | 0.602897 | 0.765588 |
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B-dpo-v3")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B-dpo-v3")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "Zenith-7B-dpo-v3"], "pipeline_tag": "text-generation"} | text-generation | Xenon1/Zenith-7B-dpo-v3 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Zenith-7B-dpo-v3",
"conversational",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:33:05+00:00 | [
"2401.10020"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v3 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for Zenith-7B-dpo-v3
===============================
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
Results
-------
Instruction format
------------------
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply\_chat\_template()' method:
Model Architecture
------------------
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v3 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
82
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v3 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | null |
# Lora of hornet/ホーネット/大黄蜂 (Azur Lane)
## What Is This?
This is the LoRA model of waifu hornet/ホーネット/大黄蜂 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/hornet_azurlane](https://huggingface.co/datasets/CyberHarem/hornet_azurlane), which contains 361 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 10, resolution is 720x720, clustering into 20 buckets.
* Trained for 3640 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `hornet_azurlane`.**
* Pruned core tags for this waifu are `blonde_hair, long_hair, green_eyes, twintails, breasts, large_breasts, bangs, sidelocks, very_long_hair, hat, cowboy_hat, black_headwear`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 546, you need to download [`546/hornet_azurlane.pt`](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/546/hornet_azurlane.pt) as the embedding and [`546/hornet_azurlane.safetensors`](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/546/hornet_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 546.
1800 images (1.84 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | pattern_2 | pattern_3_0 | pattern_3_1 | pattern_4_0 | pattern_4_1 | pattern_5 | pattern_6 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:----------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 546 | 7 | 0.772 | **0.965** | **0.836** | **0.773** | [Download](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/546/hornet_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1365 | 16 | 0.770 | 0.964 | 0.835 | 0.771 | [Download](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/1365/hornet_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1729 | 20 | 0.763 | 0.933 | 0.831 | 0.759 | [Download](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/1729/hornet_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2821 | 32 | 0.766 | 0.904 | 0.821 | 0.748 | [Download](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/2821/hornet_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2002 | 23 | **0.777** | 0.905 | 0.809 | 0.741 | [Download](https://huggingface.co/CyberHarem/hornet_azurlane/resolve/main/2002/hornet_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 2821 to 3640](all/0.md)
* [Steps From 1911 to 2730](all/1.md)
* [Steps From 1001 to 1820](all/2.md)
* [Steps From 91 to 910](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/hornet_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/hornet_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/hornet_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T02:36:48+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hornet_azurlane #license-mit #region-us
| Lora of hornet/ホーネット/大黄蜂 (Azur Lane)
====================================
What Is This?
-------------
This is the LoRA model of waifu hornet/ホーネット/大黄蜂 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/hornet\_azurlane, which contains 361 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 10, resolution is 720x720, clustering into 20 buckets.
* Trained for 3640 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'hornet\_azurlane'.
* Pruned core tags for this waifu are 'blonde\_hair, long\_hair, green\_eyes, twintails, breasts, large\_breasts, bangs, sidelocks, very\_long\_hair, hat, cowboy\_hat, black\_headwear'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 546, you need to download '546/hornet\_azurlane.pt' as the embedding and '546/hornet\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 546.
1800 images (1.84 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 2821 to 3640
* Steps From 1911 to 2730
* Steps From 1001 to 1820
* Steps From 91 to 910
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 546, you need to download '546/hornet\\_azurlane.pt' as the embedding and '546/hornet\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 546.\n\n\n1800 images (1.84 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2821 to 3640\n* Steps From 1911 to 2730\n* Steps From 1001 to 1820\n* Steps From 91 to 910"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hornet_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 546, you need to download '546/hornet\\_azurlane.pt' as the embedding and '546/hornet\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 546.\n\n\n1800 images (1.84 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2821 to 3640\n* Steps From 1911 to 2730\n* Steps From 1001 to 1820\n* Steps From 91 to 910"
] | [
44,
38,
471
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/hornet_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mathreader/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | reinforcement-learning | mathreader/poca-SoccerTwos | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2024-02-15T02:40:25+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: mathreader/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: mathreader/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: mathreader/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
52,
205
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: mathreader/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | adarshheg/llama-7b-chat-finetuned-4bit-std | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:40:46+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
60,
29,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers | GGUF version for [Test157t/Hex-Macaroniac-7b](https://huggingface.co/Test157t/Hex-Macaroniac-7b)
| {"library_name": "transformers", "pipeline_tag": "text-generation"} | text-generation | konz00/Hex-Macaroniac-7b-GGUF | [
"transformers",
"gguf",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:41:35+00:00 | [] | [] | TAGS
#transformers #gguf #text-generation #endpoints_compatible #region-us
| GGUF version for Test157t/Hex-Macaroniac-7b
!URL
| [] | [
"TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
] | [
25
] | [
"passage: TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
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null | null | diffusers |
# Textual inversion text2image fine-tuning - JiafengMao/textual_inversion_pigg_XL
These are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": true} | text-to-image | JiafengMao/textual_inversion_pigg_XL | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2024-02-15T02:42:45+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# Textual inversion text2image fine-tuning - JiafengMao/textual_inversion_pigg_XL
These are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
| [
"# Textual inversion text2image fine-tuning - JiafengMao/textual_inversion_pigg_XL\nThese are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - JiafengMao/textual_inversion_pigg_XL\nThese are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
104,
86
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n# Textual inversion text2image fine-tuning - JiafengMao/textual_inversion_pigg_XL\nThese are textual inversion adaption weights for stabilityai/stable-diffusion-xl-base-1.0. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | aidonuts/enthralling-etchings-132-s600b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:52:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
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
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BibTeX:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- 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. -->
# xlmr-lstm-crf-resume-ner3
This model is a fine-tuned version of [hiendang7613/xlmr-lstm-crf-resume-ner3](https://huggingface.co/hiendang7613/xlmr-lstm-crf-resume-ner3) on the fcv_dataset 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["fcv_dataset"], "base_model": "hiendang7613/xlmr-lstm-crf-resume-ner3", "model-index": [{"name": "xlmr-lstm-crf-resume-ner3", "results": []}]} | token-classification | hiendang7613/xlmr-lstm-crf-resume-ner3 | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:fcv_dataset",
"base_model:hiendang7613/xlmr-lstm-crf-resume-ner3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T02:53:17+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-fcv_dataset #base_model-hiendang7613/xlmr-lstm-crf-resume-ner3 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# xlmr-lstm-crf-resume-ner3
This model is a fine-tuned version of hiendang7613/xlmr-lstm-crf-resume-ner3 on the fcv_dataset 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# xlmr-lstm-crf-resume-ner3\n\nThis model is a fine-tuned version of hiendang7613/xlmr-lstm-crf-resume-ner3 on the fcv_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-fcv_dataset #base_model-hiendang7613/xlmr-lstm-crf-resume-ner3 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlmr-lstm-crf-resume-ner3\n\nThis model is a fine-tuned version of hiendang7613/xlmr-lstm-crf-resume-ner3 on the fcv_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
89,
58,
6,
12,
8,
3,
113,
33
] | [
"passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-fcv_dataset #base_model-hiendang7613/xlmr-lstm-crf-resume-ner3 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# xlmr-lstm-crf-resume-ner3\n\nThis model is a fine-tuned version of hiendang7613/xlmr-lstm-crf-resume-ner3 on the fcv_dataset dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
# NeuralLogic-7B-V
NeuralLogic-7B-V is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/Triunvirato-7b](https://huggingface.co/Kukedlc/Triunvirato-7b)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Kukedlc/Triunvirato-7b
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Kukedlc/Triunvirato-7b
parameters:
t:
- filter: self_attn
value: [0.1, 0.6, 0.3, 0.7, 1]
- filter: mlp
value: [0.9, 0.4, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralLogic-7B-V"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "Kukedlc/Triunvirato-7b", "mlabonne/NeuralHermes-2.5-Mistral-7B"], "base_model": ["Kukedlc/Triunvirato-7b", "mlabonne/NeuralHermes-2.5-Mistral-7B"]} | text-generation | Kukedlc/NeuralLogic-7B-V | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/Triunvirato-7b",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:Kukedlc/Triunvirato-7b",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:53:46+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/Triunvirato-7b #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-Kukedlc/Triunvirato-7b #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuralLogic-7B-V
NeuralLogic-7B-V is a merge of the following models using LazyMergekit:
* Kukedlc/Triunvirato-7b
* mlabonne/NeuralHermes-2.5-Mistral-7B
## Configuration
## Usage
| [
"# NeuralLogic-7B-V\n\nNeuralLogic-7B-V is a merge of the following models using LazyMergekit:\n* Kukedlc/Triunvirato-7b\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/Triunvirato-7b #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-Kukedlc/Triunvirato-7b #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralLogic-7B-V\n\nNeuralLogic-7B-V is a merge of the following models using LazyMergekit:\n* Kukedlc/Triunvirato-7b\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
120,
58,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/Triunvirato-7b #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-Kukedlc/Triunvirato-7b #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralLogic-7B-V\n\nNeuralLogic-7B-V is a merge of the following models using LazyMergekit:\n* Kukedlc/Triunvirato-7b\n* mlabonne/NeuralHermes-2.5-Mistral-7B## Configuration## Usage"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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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 0.8.2 | {"library_name": "peft", "base_model": "mistralai/mistral-7b-v0.1"} | null | pawan2411/ESGcombinedData-LoRA | [
"peft",
"safetensors",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/mistral-7b-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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## Model Card Authors [optional]
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null | null | null |
# **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="arnabmukherjee/q-taxi_v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-taxi_v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.77", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | arnabmukherjee/q-taxi_v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-15T02:55:24+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | kyone/another_model | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
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# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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APA:
## Glossary [optional]
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null | null | null |
# Lora of trento/トレント/特伦托 (Azur Lane)
## What Is This?
This is the LoRA model of waifu trento/トレント/特伦托 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/trento_azurlane](https://huggingface.co/datasets/CyberHarem/trento_azurlane), which contains 145 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1480 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `trento_azurlane`.**
* Pruned core tags for this waifu are `long_hair, breasts, hair_over_one_eye, large_breasts, purple_hair, red_eyes, bangs, very_long_hair, eyewear_on_head, sunglasses, blue_hair`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1184, you need to download [`1184/trento_azurlane.pt`](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/1184/trento_azurlane.pt) as the embedding and [`1184/trento_azurlane.safetensors`](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/1184/trento_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 1184.
1600 images (1.69 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | pattern_2_0 | pattern_2_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:----------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 1184 | 33 | **0.969** | 0.954 | 0.833 | **0.743** | [Download](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/1184/trento_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1036 | 29 | 0.954 | 0.955 | 0.838 | 0.733 | [Download](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/1036/trento_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 666 | 19 | 0.953 | 0.975 | 0.833 | 0.724 | [Download](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/666/trento_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 925 | 26 | 0.940 | 0.959 | 0.838 | 0.717 | [Download](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/925/trento_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 444 | 13 | 0.933 | **0.985** | **0.845** | 0.716 | [Download](https://huggingface.co/CyberHarem/trento_azurlane/resolve/main/444/trento_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1147 to 1480](all/0.md)
* [Steps From 777 to 1110](all/1.md)
* [Steps From 407 to 740](all/2.md)
* [Steps From 37 to 370](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/trento_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/trento_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/trento_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T02:59:22+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/trento_azurlane #license-mit #region-us
| Lora of trento/トレント/特伦托 (Azur Lane)
===================================
What Is This?
-------------
This is the LoRA model of waifu trento/トレント/特伦托 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/trento\_azurlane, which contains 145 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1480 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'trento\_azurlane'.
* Pruned core tags for this waifu are 'long\_hair, breasts, hair\_over\_one\_eye, large\_breasts, purple\_hair, red\_eyes, bangs, very\_long\_hair, eyewear\_on\_head, sunglasses, blue\_hair'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1184, you need to download '1184/trento\_azurlane.pt' as the embedding and '1184/trento\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 1184.
1600 images (1.69 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 1147 to 1480
* Steps From 777 to 1110
* Steps From 407 to 740
* Steps From 37 to 370
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1184, you need to download '1184/trento\\_azurlane.pt' as the embedding and '1184/trento\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1184.\n\n\n1600 images (1.69 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1147 to 1480\n* Steps From 777 to 1110\n* Steps From 407 to 740\n* Steps From 37 to 370"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/trento_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1184, you need to download '1184/trento\\_azurlane.pt' as the embedding and '1184/trento\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1184.\n\n\n1600 images (1.69 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1147 to 1480\n* Steps From 777 to 1110\n* Steps From 407 to 740\n* Steps From 37 to 370"
] | [
44,
38,
470
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/trento_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers |
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| | {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "base_model": "jondurbin/bagel-34b-v0.2"} | text-generation | LoneStriker/Smaug-34B-v0.1-2.4bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T02:59:31+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
!image/png
This model is a finetune of jondurbin's excellent bagel model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
ARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88
| [
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
72,
5,
40
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation Results### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
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null | null | transformers |
# Model Card for Model ID
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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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## Technical Specifications [optional]
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# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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Use the code below to get started with the model.
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### Training Data
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#### Preprocessing [optional]
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null | null | diffusers | # plum
<Gallery />
## Download model
[Download](/lylosn/plum/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "plum", "output": {"url": "images/profile_plum (17).png"}}], "base_model": "InstantX/InstantID"} | text-to-image | lylosn/plum | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:InstantX/InstantID",
"region:us"
] | 2024-02-15T03:07:14+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #region-us
| # plum
<Gallery />
## Download model
Download them in the Files & versions tab.
| [
"# plum\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #region-us \n",
"# plum\n\n<Gallery />",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
47,
8,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-InstantX/InstantID #region-us \n# plum\n\n<Gallery />## Download model\n\n\nDownload them in the Files & versions tab."
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] |
null | null | null |
import gradio
openai.api_key = "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"
messages = [{"role": "system", "content": "You are a financial experts that specializes in real estate investment and negotiation"}]
def CustomChatGPT(user_input):
messages.append({"role": "user", "content": user_input})
response = openai.ChatCompletion.create(
model = "gpt-3.5-turbo",
messages = messages
)
ChatGPT_reply = response["choices"][0]["message"]["content"]
messages.append({"role": "assistant", "content": ChatGPT_reply})
return ChatGPT_reply
demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Real Estate Pro")
demo.launch(share=True) | {} | null | meeeeeeeeeeeeeeeeeee/lol | [
"region:us"
] | 2024-02-15T03:12:59+00:00 | [] | [] | TAGS
#region-us
|
import gradio
openai.api_key = "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"
messages = [{"role": "system", "content": "You are a financial experts that specializes in real estate investment and negotiation"}]
def CustomChatGPT(user_input):
URL({"role": "user", "content": user_input})
response = URL(
model = "gpt-3.5-turbo",
messages = messages
)
ChatGPT_reply = response["choices"][0]["message"]["content"]
URL({"role": "assistant", "content": ChatGPT_reply})
return ChatGPT_reply
demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Real Estate Pro")
URL(share=True) | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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null | null | null |
# Lora of juno/ジュノー/天后 (Azur Lane)
## What Is This?
This is the LoRA model of waifu juno/ジュノー/天后 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/juno_azurlane](https://huggingface.co/datasets/CyberHarem/juno_azurlane), which contains 59 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `juno_azurlane`.**
* Pruned core tags for this waifu are `pink_hair, long_hair, crown, bangs, mini_crown, ribbon, twintails, pink_eyes, bow, purple_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 480, you need to download [`480/juno_azurlane.pt`](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/480/juno_azurlane.pt) as the embedding and [`480/juno_azurlane.safetensors`](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/480/juno_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 480.
1520 images (1.59 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_0_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 480 | 33 | **0.864** | 0.964 | 0.863 | **0.778** | [Download](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/480/juno_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 520 | 36 | 0.816 | **0.976** | 0.862 | 0.742 | [Download](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/520/juno_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 540 | 37 | 0.815 | 0.972 | 0.857 | 0.735 | [Download](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/540/juno_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 420 | 29 | 0.780 | 0.958 | **0.864** | 0.718 | [Download](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/420/juno_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 700 | 48 | 0.783 | 0.961 | 0.856 | 0.710 | [Download](https://huggingface.co/CyberHarem/juno_azurlane/resolve/main/700/juno_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/juno_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/juno_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/juno_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T03:13:08+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/juno_azurlane #license-mit #region-us
| Lora of juno/ジュノー/天后 (Azur Lane)
================================
What Is This?
-------------
This is the LoRA model of waifu juno/ジュノー/天后 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/juno\_azurlane, which contains 59 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'juno\_azurlane'.
* Pruned core tags for this waifu are 'pink\_hair, long\_hair, crown, bangs, mini\_crown, ribbon, twintails, pink\_eyes, bow, purple\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 480, you need to download '480/juno\_azurlane.pt' as the embedding and '480/juno\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 480.
1520 images (1.59 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 480, you need to download '480/juno\\_azurlane.pt' as the embedding and '480/juno\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 480.\n\n\n1520 images (1.59 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/juno_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 480, you need to download '480/juno\\_azurlane.pt' as the embedding and '480/juno\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 480.\n\n\n1520 images (1.59 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
44,
38,
465
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/juno_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | transformers |
# Model Card for Model ID
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | automatic-speech-recognition | spsither/wav2vec2_run9.620 | [
"transformers",
"safetensors",
"wav2vec2",
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#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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### Compute Infrastructure
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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "microsoft/Orca-2-7b"} | null | nicejames/orca-2-7B-v01-fine-tuned-using-ludwig-4bit | [
"peft",
"safetensors",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/Orca-2-7b #region-us
|
# Model Card for Model ID
## Model Details
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### Model Sources [optional]
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | aidonuts/enthralling-etchings-132-s800 | [
"transformers",
"safetensors",
"llama",
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|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
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### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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] |
null | null | transformers | #### GPU
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def generate_prompt(instruction, input=""):
instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
if input:
return f"""Instruction: {instruction}
Input: {input}
Response:"""
else:
return f"""User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: {instruction}
Assistant:"""
model = AutoModelForCausalLM.from_pretrained("jetaudio/rwkv-5-v2-3b-16k", trust_remote_code=True, torch_dtype=torch.bfloat16).to(0)
tokenizer = AutoTokenizer.from_pretrained("jetaudio/rwkv-5-v2-3b-16k", trust_remote_code=True)
text = "介绍一下大熊猫"
prompt = generate_prompt(text)
inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
``` | {} | text-generation | jetaudio/rwkv-5-v2-3b-16k | [
"transformers",
"pytorch",
"rwkv5",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T03:23:54+00:00 | [] | [] | TAGS
#transformers #pytorch #rwkv5 #text-generation #custom_code #autotrain_compatible #endpoints_compatible #region-us
| #### GPU
| [
"#### GPU"
] | [
"TAGS\n#transformers #pytorch #rwkv5 #text-generation #custom_code #autotrain_compatible #endpoints_compatible #region-us \n",
"#### GPU"
] | [
44,
3
] | [
"passage: TAGS\n#transformers #pytorch #rwkv5 #text-generation #custom_code #autotrain_compatible #endpoints_compatible #region-us \n#### GPU"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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 0.8.2 | {"library_name": "peft", "base_model": "google-t5/t5-small"} | null | Queriamin/t5_xsum_summarization_1000steps | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-google-t5/t5-small #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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APA:
## Glossary [optional]
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_5
This model is a fine-tuned version of [ybelkada/mistral-7b-instruct-v0.1-sharded](https://huggingface.co/ybelkada/mistral-7b-instruct-v0.1-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "ybelkada/mistral-7b-instruct-v0.1-sharded", "model-index": [{"name": "mistral_5", "results": []}]} | null | anyiwang/mistral_5 | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:ybelkada/mistral-7b-instruct-v0.1-sharded",
"region:us"
] | 2024-02-15T03:24:16+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-ybelkada/mistral-7b-instruct-v0.1-sharded #region-us
|
# mistral_5
This model is a fine-tuned version of ybelkada/mistral-7b-instruct-v0.1-sharded on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2 | [
"# mistral_5\n\nThis model is a fine-tuned version of ybelkada/mistral-7b-instruct-v0.1-sharded on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 8\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-ybelkada/mistral-7b-instruct-v0.1-sharded #region-us \n",
"# mistral_5\n\nThis model is a fine-tuned version of ybelkada/mistral-7b-instruct-v0.1-sharded on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 8\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
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36
] | [
"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-ybelkada/mistral-7b-instruct-v0.1-sharded #region-us \n# mistral_5\n\nThis model is a fine-tuned version of ybelkada/mistral-7b-instruct-v0.1-sharded on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 8\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | automatic-speech-recognition | spsither/wav2vec2_run9.615 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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[optional]
BibTeX:
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "249.46 +/- 23.16", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | jacobemmerson/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-15T03:30:52+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot"} | null | adalib/megengine-cond-gen-codeparrot-prefix | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | peft |
# Model Card for Model ID
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [loganunger/bert-finetuned-squad](https://huggingface.co/loganunger/bert-finetuned-squad) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "loganunger/bert-finetuned-squad", "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | question-answering | loganunger/bert-finetuned-squad | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:loganunger/bert-finetuned-squad",
"endpoints_compatible",
"region:us"
] | 2024-02-15T03:42:37+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-loganunger/bert-finetuned-squad #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of loganunger/bert-finetuned-squad 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of loganunger/bert-finetuned-squad on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-loganunger/bert-finetuned-squad #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of loganunger/bert-finetuned-squad on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
58,
41,
6,
12,
8,
3,
103,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-loganunger/bert-finetuned-squad #endpoints_compatible #region-us \n# bert-finetuned-squad\n\nThis model is a fine-tuned version of loganunger/bert-finetuned-squad on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
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] |
null | null | peft |
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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## Environmental Impact
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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).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "ybelkada/blip2-opt-2.7b-fp16-sharded"} | null | leoreigoto/Data3_V3_BLIP2_VQA | [
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# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
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- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
- Carbon Emitted:
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null | null | null |
# **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="Noname08/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"])
```
| {"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}]}]}]} | reinforcement-learning | Noname08/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-15T03:48:56+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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] |
null | null | peft |
# checkpoints
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the Red Solar Sky dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.6.0.dev0
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.14.5
- Tokenizers 0.15.1
| {"language": ["en"], "license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer", "pretrained", "lora"], "datasets": ["generator"], "base_model": "tiiuae/falcon-7b", "pipeline_tag": "text-generation", "widget": [{"text": "Hello how are you?"}, {"text": "Nice to meet you."}, {"text": "Good afternoon."}], "model-index": [{"name": "checkpoints", "results": []}]} | text-generation | KZMTx/RedSolarSkyAdapter | [
"peft",
"safetensors",
"generated_from_trainer",
"pretrained",
"lora",
"text-generation",
"en",
"dataset:generator",
"base_model:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | 2024-02-15T03:49:24+00:00 | [] | [
"en"
] | TAGS
#peft #safetensors #generated_from_trainer #pretrained #lora #text-generation #en #dataset-generator #base_model-tiiuae/falcon-7b #license-apache-2.0 #region-us
|
# checkpoints
This model is a fine-tuned version of tiiuae/falcon-7b on the Red Solar Sky dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.6.0.dev0
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.14.5
- Tokenizers 0.15.1
| [
"# checkpoints\n\nThis model is a fine-tuned version of tiiuae/falcon-7b on the Red Solar Sky dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: bfloat16",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.6.0.dev0\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.5\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #pretrained #lora #text-generation #en #dataset-generator #base_model-tiiuae/falcon-7b #license-apache-2.0 #region-us \n",
"# checkpoints\n\nThis model is a fine-tuned version of tiiuae/falcon-7b on the Red Solar Sky dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: bfloat16",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.6.0.dev0\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.5\n- Tokenizers 0.15.1"
] | [
62,
31,
6,
12,
8,
165,
102,
4,
40
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #pretrained #lora #text-generation #en #dataset-generator #base_model-tiiuae/falcon-7b #license-apache-2.0 #region-us \n# checkpoints\n\nThis model is a fine-tuned version of tiiuae/falcon-7b on the Red Solar Sky dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: bfloat16### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.6.0.dev0\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.14.5\n- Tokenizers 0.15.1"
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null | null | null |
# **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="Noname08/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Noname08/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-15T03:50:03+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | diffusers | # Umi Sonoda
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_umi_sonoda` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/umi-sonoda/tree/main) them in the Files & versions tab.
| {"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2),", "parameters": {"negative_prompt": "bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,"}, "output": {"url": "images/umi_final.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "id_umi_sonoda"} | text-to-image | theidoldaily/umi-sonoda | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
] | 2024-02-15T03:51:54+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us
| # Umi Sonoda
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use 'id_umi_sonoda' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Umi Sonoda\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_umi_sonoda' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n",
"# Umi Sonoda\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_umi_sonoda' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
56,
10,
68,
21,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n# Umi Sonoda\n\n<Gallery />## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.## Trigger words\n\nYou should use 'id_umi_sonoda' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
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] |
null | null | transformers |
### SOLAR-10.7B-Instruct-Forest-DPO
Introducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.
This model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks.
A mixture of the following datasets was used for fine-tuning.
1. Intel/orca_dpo_pairs
2. nvidia/HelpSteer
3. jondurbin/truthy-dpo-v0.1
💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["Intel/orca_dpo_pairs", "nvidia/HelpSteer", "jondurbin/truthy-dpo-v0.1"], "pipeline_tag": "text-generation"} | text-generation | abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:nvidia/HelpSteer",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T03:57:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #conversational #en #dataset-Intel/orca_dpo_pairs #dataset-nvidia/HelpSteer #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### SOLAR-10.7B-Instruct-Forest-DPO
Introducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.
This model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks.
A mixture of the following datasets was used for fine-tuning.
1. Intel/orca_dpo_pairs
2. nvidia/HelpSteer
3. jondurbin/truthy-dpo-v0.1
Usage
| [
"### SOLAR-10.7B-Instruct-Forest-DPO\nIntroducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.\nThis model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks. \n\nA mixture of the following datasets was used for fine-tuning.\n\n1. Intel/orca_dpo_pairs\n2. nvidia/HelpSteer\n3. jondurbin/truthy-dpo-v0.1\n\n\n Usage"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #en #dataset-Intel/orca_dpo_pairs #dataset-nvidia/HelpSteer #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### SOLAR-10.7B-Instruct-Forest-DPO\nIntroducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.\nThis model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks. \n\nA mixture of the following datasets was used for fine-tuning.\n\n1. Intel/orca_dpo_pairs\n2. nvidia/HelpSteer\n3. jondurbin/truthy-dpo-v0.1\n\n\n Usage"
] | [
102,
145
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #en #dataset-Intel/orca_dpo_pairs #dataset-nvidia/HelpSteer #dataset-jondurbin/truthy-dpo-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### SOLAR-10.7B-Instruct-Forest-DPO\nIntroducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.\nThis model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks. \n\nA mixture of the following datasets was used for fine-tuning.\n\n1. Intel/orca_dpo_pairs\n2. nvidia/HelpSteer\n3. jondurbin/truthy-dpo-v0.1\n\n\n Usage"
] | [
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] |
null | null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.3,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (17 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Degree`, `Desc Responsibility`, `Edu Desc`, `Edu End Date`, `Edu Start Date`, `Email`, `Employer Names`, `Institution`, `Links`, `Location`, `Name`, `Phone`, `Position`, `Skills`, `Work End Date`, `Work Location`, `Work Start Date` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 83.33 |
| `ENTS_P` | 81.16 |
| `ENTS_R` | 85.63 |
| `TRANSFORMER_LOSS` | 39026.84 |
| `NER_LOSS` | 1290990.48 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | drastimehta/en_pipeline | [
"spacy",
"token-classification",
"en",
"model-index",
"has_space",
"region:us"
] | 2024-02-15T04:00:56+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #has_space #region-us
|
### Label Scheme
View label scheme (17 labels for 1 components)
### Accuracy
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"### Accuracy"
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"TAGS\n#spacy #token-classification #en #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (17 labels for 1 components)",
"### Accuracy"
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null | null | diffusers | # Nozomi Tojo
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_nozomi_tojo` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/nozomi-tojo/tree/main) them in the Files & versions tab.
| {"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2),", "parameters": {"negative_prompt": "bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,"}, "output": {"url": "images/nozomi_final.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "id_nozomi_tojo"} | text-to-image | theidoldaily/nozomi-tojo | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
] | 2024-02-15T04:07:06+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us
| # Nozomi Tojo
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use 'id_nozomi_tojo' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Nozomi Tojo\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_nozomi_tojo' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n",
"# Nozomi Tojo\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_nozomi_tojo' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
56,
11,
68,
22,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n# Nozomi Tojo\n\n<Gallery />## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.## Trigger words\n\nYou should use 'id_nozomi_tojo' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
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] |
null | null | peft |
# Low-rank decomposition of [valine/OpenSnark](https://huggingface.co/valine/OpenSnark) using [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) as base
Created using [LoRD](https://github.com/thomasgauthier/LoRD)
| {"library_name": "peft", "base_model": "teknium/OpenHermes-2.5-Mistral-7B"} | null | thomasgauthier/OpenSnark-LoRD | [
"peft",
"safetensors",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | 2024-02-15T04:07:21+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us
|
# Low-rank decomposition of valine/OpenSnark using teknium/OpenHermes-2.5-Mistral-7B as base
Created using LoRD
| [
"# Low-rank decomposition of valine/OpenSnark using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
"TAGS\n#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us \n",
"# Low-rank decomposition of valine/OpenSnark using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
32,
36
] | [
"passage: TAGS\n#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us \n# Low-rank decomposition of valine/OpenSnark using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | FINNUMBER/Yi-Ko-6B-Finch-QA-full | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | null |
## Exllama v2 Quantizations of sparsetral-16x7B-v2-SPIN_iter0
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/serpdotai/sparsetral-16x7B-v2-SPIN_iter0
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2/tree/8_0) | 8.0 | 8.0 | 8.3 GB | 9.7 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2/tree/6_5) | 6.5 | 8.0 | 7.1 GB | 8.5 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2/tree/5_0) | 5.0 | 6.0 | 5.7 GB | 7.1 GB | 9.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2/tree/4_25) | 4.25 | 6.0 | 5.1 GB | 6.5 GB | 8.6 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2/tree/3_5) | 3.5 | 6.0 | 4.4 GB | 5.8 GB | 7.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2 sparsetral-16x7B-v2-SPIN_iter0-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `sparsetral-16x7B-v2-SPIN_iter0-exl2`:
```shell
mkdir sparsetral-16x7B-v2-SPIN_iter0-exl2
huggingface-cli download bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2 --local-dir sparsetral-16x7B-v2-SPIN_iter0-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir sparsetral-16x7B-v2-SPIN_iter0-exl2-6_5
huggingface-cli download bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2 --revision 6_5 --local-dir sparsetral-16x7B-v2-SPIN_iter0-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir sparsetral-16x7B-v2-SPIN_iter0-exl2-6.5
huggingface-cli download bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2 --revision 6_5 --local-dir sparsetral-16x7B-v2-SPIN_iter0-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"language": ["en"], "license": "apache-2.0", "datasets": ["teknium/OpenHermes-2.5", "jondurbin/truthy-dpo-v0.1", "jondurbin/gutenberg-dpo-v0.1", "argilla/dpo-mix-7k"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/sparsetral-16x7B-v2-SPIN_iter0-exl2 | [
"text-generation",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:argilla/dpo-mix-7k",
"license:apache-2.0",
"region:us"
] | 2024-02-15T04:11:22+00:00 | [] | [
"en"
] | TAGS
#text-generation #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #license-apache-2.0 #region-us
| Exllama v2 Quantizations of sparsetral-16x7B-v2-SPIN\_iter0
-----------------------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'sparsetral-16x7B-v2-SPIN\_iter0-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#text-generation #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #license-apache-2.0 #region-us \n"
] | [
78
] | [
"passage: TAGS\n#text-generation #en #dataset-teknium/OpenHermes-2.5 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-argilla/dpo-mix-7k #license-apache-2.0 #region-us \n"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "ybelkada/blip2-opt-2.7b-fp16-sharded"} | null | leoreigoto/Data2_V2_BLIP2_Finetune_Caption_First_Epoch | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ybelkada/blip2-opt-2.7b-fp16-sharded",
"has_space",
"region:us"
] | 2024-02-15T04:11:41+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #has_space #region-us
|
# Model Card for Model ID
## Model Details
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### Model Sources [optional]
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
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## Model Card Contact
### Framework versions
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
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"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
"### Training Data",
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #has_space #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2"
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null | null | transformers |
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 | {"language": ["zh", "en", "fr", "de", "ja", "ko", "it", "ru"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": false} | text-generation | OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k-gptq | [
"transformers",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-15T04:11:48+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: URL
Website and Demo: URL
Evaluation result of this model: URL
!Demo
# Copyright Notice
Base model: URL
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 | [
"# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo",
"# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0",
"## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.",
"## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。"
] | [
"TAGS\n#transformers #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo",
"# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0",
"## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.",
"## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。"
] | [
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"passage: TAGS\n#transformers #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo# Copyright Notice\n\nBase model: URL\n\nLicense: Apache 2.0## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy."
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] |
null | null | transformers |
# Conformer CTC 4M parameters
WanDB https://wandb.ai/huseinzol05/malaysian-conformer-ctc-tiny?workspace=user-huseinzol05 | {"library_name": "transformers", "tags": []} | feature-extraction | mesolitica/conformer-4M-ctc | [
"transformers",
"safetensors",
"conformer",
"feature-extraction",
"custom_code",
"region:us"
] | 2024-02-15T04:12:07+00:00 | [] | [] | TAGS
#transformers #safetensors #conformer #feature-extraction #custom_code #region-us
|
# Conformer CTC 4M parameters
WanDB URL | [
"# Conformer CTC 4M parameters\n\nWanDB URL"
] | [
"TAGS\n#transformers #safetensors #conformer #feature-extraction #custom_code #region-us \n",
"# Conformer CTC 4M parameters\n\nWanDB URL"
] | [
28,
12
] | [
"passage: TAGS\n#transformers #safetensors #conformer #feature-extraction #custom_code #region-us \n# Conformer CTC 4M parameters\n\nWanDB URL"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "ybelkada/blip2-opt-2.7b-fp16-sharded"} | null | leoreigoto/Data2_V2_Blip2_Finetune_Caption | [
"peft",
"safetensors",
"arxiv:1910.09700",
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#peft #safetensors #arxiv-1910.09700 #base_model-ybelkada/blip2-opt-2.7b-fp16-sharded #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-govReport-boardpapers-3072
This model is a fine-tuned version of [RMWeerasinghe/t5-small-finetuned-govReport-3072](https://huggingface.co/RMWeerasinghe/t5-small-finetuned-govReport-3072) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6701
- Rouge1: 0.0443
- Rouge2: 0.0194
- Rougel: 0.0382
- Rougelsum: 0.0443
## 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 0.67 | 1 | 3.9496 | 0.0584 | 0.0214 | 0.0482 | 0.0572 |
| No log | 2.0 | 3 | 3.9252 | 0.0562 | 0.0223 | 0.0463 | 0.0562 |
| No log | 2.67 | 4 | 3.9121 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 4.0 | 6 | 3.8880 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 4.67 | 7 | 3.8755 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 6.0 | 9 | 3.8506 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 6.67 | 10 | 3.8395 | 0.0553 | 0.0197 | 0.0441 | 0.0541 |
| No log | 8.0 | 12 | 3.8172 | 0.0582 | 0.0262 | 0.049 | 0.057 |
| No log | 8.67 | 13 | 3.8065 | 0.0582 | 0.0262 | 0.049 | 0.057 |
| No log | 10.0 | 15 | 3.7862 | 0.0582 | 0.0257 | 0.049 | 0.057 |
| No log | 10.67 | 16 | 3.7769 | 0.057 | 0.0262 | 0.049 | 0.0556 |
| No log | 12.0 | 18 | 3.7599 | 0.0577 | 0.0294 | 0.0495 | 0.0575 |
| No log | 12.67 | 19 | 3.7522 | 0.0487 | 0.0174 | 0.042 | 0.0474 |
| 4.3528 | 14.0 | 21 | 3.7378 | 0.048 | 0.0155 | 0.0406 | 0.0461 |
| 4.3528 | 14.67 | 22 | 3.7310 | 0.0536 | 0.0206 | 0.0421 | 0.0511 |
| 4.3528 | 16.0 | 24 | 3.7187 | 0.048 | 0.017 | 0.0394 | 0.0448 |
| 4.3528 | 16.67 | 25 | 3.7132 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 18.0 | 27 | 3.7031 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 18.67 | 28 | 3.6985 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 20.0 | 30 | 3.6905 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 20.67 | 31 | 3.6869 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 22.0 | 33 | 3.6807 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 22.67 | 34 | 3.6781 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 24.0 | 36 | 3.6740 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 24.67 | 37 | 3.6725 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 26.0 | 39 | 3.6705 | 0.0443 | 0.0194 | 0.0382 | 0.0443 |
| 4.0602 | 26.67 | 40 | 3.6701 | 0.0443 | 0.0194 | 0.0382 | 0.0443 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "tags": ["Summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "RMWeerasinghe/t5-small-finetuned-govReport-3072", "pipeline_tag": "summarization", "model-index": [{"name": "t5-small-govReport-boardpapers-3072", "results": []}]} | summarization | RMWeerasinghe/t5-small-govReport-boardpapers-3072 | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"Summarization",
"generated_from_trainer",
"summarization",
"base_model:RMWeerasinghe/t5-small-finetuned-govReport-3072",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T04:15:21+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #Summarization #generated_from_trainer #summarization #base_model-RMWeerasinghe/t5-small-finetuned-govReport-3072 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-govReport-boardpapers-3072
===================================
This model is a fine-tuned version of RMWeerasinghe/t5-small-finetuned-govReport-3072 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6701
* Rouge1: 0.0443
* Rouge2: 0.0194
* Rougel: 0.0382
* Rougelsum: 0.0443
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: 4e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #Summarization #generated_from_trainer #summarization #base_model-RMWeerasinghe/t5-small-finetuned-govReport-3072 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
97,
126,
4,
30
] | [
"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #Summarization #generated_from_trainer #summarization #base_model-RMWeerasinghe/t5-small-finetuned-govReport-3072 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": ["trl", "sft"]} | text-generation | ybelkada/test-automatic-tagging | [
"transformers",
"safetensors",
"llama",
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"sft",
"arxiv:1910.09700",
"autotrain_compatible",
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"text-generation-inference",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-automatic-tagging-from-trainer
This model is a fine-tuned version of [HuggingFaceM4/tiny-random-LlamaForCausalLM](https://huggingface.co/HuggingFaceM4/tiny-random-LlamaForCausalLM) 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceM4/tiny-random-LlamaForCausalLM", "model-index": [{"name": "test-automatic-tagging-from-trainer", "results": []}]} | null | ybelkada/test-automatic-tagging-from-trainer | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceM4/tiny-random-LlamaForCausalLM",
"region:us"
] | 2024-02-15T04:16:28+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceM4/tiny-random-LlamaForCausalLM #region-us
|
# test-automatic-tagging-from-trainer
This model is a fine-tuned version of HuggingFaceM4/tiny-random-LlamaForCausalLM 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"# test-automatic-tagging-from-trainer\n\nThis model is a fine-tuned version of HuggingFaceM4/tiny-random-LlamaForCausalLM on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 1",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceM4/tiny-random-LlamaForCausalLM #region-us \n",
"# test-automatic-tagging-from-trainer\n\nThis model is a fine-tuned version of HuggingFaceM4/tiny-random-LlamaForCausalLM on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 1",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
50,
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"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceM4/tiny-random-LlamaForCausalLM #region-us \n# test-automatic-tagging-from-trainer\n\nThis model is a fine-tuned version of HuggingFaceM4/tiny-random-LlamaForCausalLM on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 1### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | diffusers | # Rin Hoshizora
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_rin_hoshizora` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/rin-hoshizora/tree/main) them in the Files & versions tab.
| {"license": "mit", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2),", "parameters": {"negative_prompt": "bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,"}, "output": {"url": "images/rinchan.png"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "id_rin_hoshizora"} | text-to-image | theidoldaily/rin-hoshizora | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
] | 2024-02-15T04:23:27+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us
| # Rin Hoshizora
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use 'id_rin_hoshizora' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Rin Hoshizora\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_rin_hoshizora' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n",
"# Rin Hoshizora\n\n<Gallery />",
"## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.",
"## Trigger words\n\nYou should use 'id_rin_hoshizora' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
56,
10,
68,
22,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-mit #region-us \n# Rin Hoshizora\n\n<Gallery />## Model description \n\nThis model was trained to generate high quality images based on SIFAS cards.\n\nTo achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.## Trigger words\n\nYou should use 'id_rin_hoshizora' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
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null | null | null |
# Lora of georgia/ジョージア/佐治亚 (Azur Lane)
## What Is This?
This is the LoRA model of waifu georgia/ジョージア/佐治亚 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/georgia_azurlane](https://huggingface.co/datasets/CyberHarem/georgia_azurlane), which contains 76 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `georgia_azurlane`.**
* Pruned core tags for this waifu are `breasts, blue_eyes, earrings, black_hair, large_breasts, bangs, heterochromia, hair_ornament, yellow_eyes, long_hair, hair_between_eyes, star_earrings`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 620, you need to download [`620/georgia_azurlane.pt`](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/620/georgia_azurlane.pt) as the embedding and [`620/georgia_azurlane.safetensors`](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/620/georgia_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 620.
1520 images (1.52 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1 | pattern_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 620 | 33 | **0.877** | 0.939 | 0.827 | **0.807** | [Download](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/620/georgia_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 520 | 28 | 0.802 | 0.935 | 0.837 | 0.766 | [Download](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/520/georgia_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 560 | 30 | 0.786 | 0.925 | 0.832 | 0.747 | [Download](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/560/georgia_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 320 | 17 | 0.758 | **0.958** | **0.848** | 0.741 | [Download](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/320/georgia_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 440 | 24 | 0.778 | 0.906 | 0.832 | 0.739 | [Download](https://huggingface.co/CyberHarem/georgia_azurlane/resolve/main/440/georgia_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/georgia_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/georgia_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/georgia_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T04:24:51+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/georgia_azurlane #license-mit #region-us
| Lora of georgia/ジョージア/佐治亚 (Azur Lane)
=====================================
What Is This?
-------------
This is the LoRA model of waifu georgia/ジョージア/佐治亚 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/georgia\_azurlane, which contains 76 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'georgia\_azurlane'.
* Pruned core tags for this waifu are 'breasts, blue\_eyes, earrings, black\_hair, large\_breasts, bangs, heterochromia, hair\_ornament, yellow\_eyes, long\_hair, hair\_between\_eyes, star\_earrings'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 620, you need to download '620/georgia\_azurlane.pt' as the embedding and '620/georgia\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 620.
1520 images (1.52 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 620, you need to download '620/georgia\\_azurlane.pt' as the embedding and '620/georgia\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 620.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/georgia_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 620, you need to download '620/georgia\\_azurlane.pt' as the embedding and '620/georgia\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 620.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
45,
38,
471
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/georgia_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | null |
# Lora of isokaze/磯風/矶风 (Azur Lane)
## What Is This?
This is the LoRA model of waifu isokaze/磯風/矶风 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/isokaze_azurlane](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane), which contains 88 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 880 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `isokaze_azurlane`.**
* Pruned core tags for this waifu are `animal_ears, green_hair, animal_ear_fluff, hair_ornament, long_hair, green_eyes, fang, thick_eyebrows, bangs, tail, hair_between_eyes, hairband, black_hairband, very_long_hair, fox_ears`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 660, you need to download [`660/isokaze_azurlane.pt`](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/660/isokaze_azurlane.pt) as the embedding and [`660/isokaze_azurlane.safetensors`](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/660/isokaze_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 660.
1520 images (1.70 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1_0 | pattern_1_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------------|:-----------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 660 | 30 | **0.994** | 0.975 | **0.854** | **0.797** | [Download](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/660/isokaze_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 594 | 27 | 0.969 | 0.982 | 0.848 | 0.772 | [Download](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/594/isokaze_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 880 | 40 | 0.968 | 0.979 | 0.843 | 0.760 | [Download](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/880/isokaze_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 638 | 29 | 0.943 | **0.989** | 0.847 | 0.755 | [Download](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/638/isokaze_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 572 | 26 | 0.914 | 0.984 | 0.849 | 0.742 | [Download](https://huggingface.co/CyberHarem/isokaze_azurlane/resolve/main/572/isokaze_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 682 to 880](all/0.md)
* [Steps From 462 to 660](all/1.md)
* [Steps From 242 to 440](all/2.md)
* [Steps From 22 to 220](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/isokaze_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/isokaze_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/isokaze_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T04:24:53+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/isokaze_azurlane #license-mit #region-us
| Lora of isokaze/磯風/矶风 (Azur Lane)
=================================
What Is This?
-------------
This is the LoRA model of waifu isokaze/磯風/矶风 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/isokaze\_azurlane, which contains 88 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 880 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'isokaze\_azurlane'.
* Pruned core tags for this waifu are 'animal\_ears, green\_hair, animal\_ear\_fluff, hair\_ornament, long\_hair, green\_eyes, fang, thick\_eyebrows, bangs, tail, hair\_between\_eyes, hairband, black\_hairband, very\_long\_hair, fox\_ears'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 660, you need to download '660/isokaze\_azurlane.pt' as the embedding and '660/isokaze\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 660.
1520 images (1.70 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 682 to 880
* Steps From 462 to 660
* Steps From 242 to 440
* Steps From 22 to 220
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 660, you need to download '660/isokaze\\_azurlane.pt' as the embedding and '660/isokaze\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 660.\n\n\n1520 images (1.70 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 682 to 880\n* Steps From 462 to 660\n* Steps From 242 to 440\n* Steps From 22 to 220"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/isokaze_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 660, you need to download '660/isokaze\\_azurlane.pt' as the embedding and '660/isokaze\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 660.\n\n\n1520 images (1.70 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 682 to 880\n* Steps From 462 to 660\n* Steps From 242 to 440\n* Steps From 22 to 220"
] | [
44,
38,
470
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/isokaze_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Wajid333/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | reinforcement-learning | Wajid333/poca-SoccerTwos | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2024-02-15T04:25:28+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: Wajid333/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Wajid333/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Wajid333/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
52,
206
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: Wajid333/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
## Model Details
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./Mistral_FFT
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# Mistral_FFT
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 3
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9016 | 0.03 | 1 | 1.1080 |
| 0.8288 | 0.25 | 8 | 0.8722 |
| 1.0797 | 0.5 | 16 | 0.9858 |
| 1.036 | 0.75 | 24 | 1.1281 |
| 1.4318 | 1.0 | 32 | 1.2369 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "Mistral_FFT", "results": []}]} | text-generation | joseagmz/Mistral_FFT | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T04:29:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Mistral\_FFT
============
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2369
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 3
* total\_train\_batch\_size: 3
* total\_eval\_batch\_size: 3
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu118
* Datasets 2.17.0
* Tokenizers 0.15.0
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 3\n* total\\_eval\\_batch\\_size: 3\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
86,
165,
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"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 3\n* total\\_eval\\_batch\\_size: 3\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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### Training Data
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Metrics
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-hi
This model is a fine-tuned version of [vasista22/whisper-hindi-small](https://huggingface.co/vasista22/whisper-hindi-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:---:|
| 0.0001 | 1000.0 | 1000 | 0.0001 | 0.0 |
| 0.0 | 2000.0 | 2000 | 0.0000 | 0.0 |
| 0.0 | 3000.0 | 3000 | 0.0000 | 0.0 |
| 0.0 | 4000.0 | 4000 | 0.0000 | 0.0 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vasista22/whisper-hindi-small", "model-index": [{"name": "whisper-small-hi", "results": []}]} | automatic-speech-recognition | Aditya757864/whisper-small-hi | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vasista22/whisper-hindi-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-15T04:48:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-vasista22/whisper-hindi-small #license-apache-2.0 #endpoints_compatible #region-us
| whisper-small-hi
================
This model is a fine-tuned version of vasista22/whisper-hindi-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Wer: 0.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-vasista22/whisper-hindi-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
72,
130,
4,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-vasista22/whisper-hindi-small #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Junmai/sample_tokenizer | [
"transformers",
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"1910.09700"
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|
# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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- Compute Region:
- Carbon Emitted:
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "283.31 +/- 20.51", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Lounarisnia/ppo-LunarLander-v2-3e6 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-15T04:49:43+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | null |
# Lora of murasaki/紫/紫 (Azur Lane)
## What Is This?
This is the LoRA model of waifu murasaki/紫/紫 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/murasaki_azurlane](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane), which contains 77 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `murasaki_azurlane`.**
* Pruned core tags for this waifu are `long_hair, purple_hair, breasts, purple_eyes, hair_ribbon, ribbon, large_breasts, very_long_hair, black_ribbon`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 720, you need to download [`720/murasaki_azurlane.pt`](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/720/murasaki_azurlane.pt) as the embedding and [`720/murasaki_azurlane.safetensors`](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/720/murasaki_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 720.
1520 images (1.52 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 720 | 38 | **0.983** | **0.967** | **0.860** | **0.808** | [Download](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/720/murasaki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 640 | 34 | 0.948 | 0.959 | 0.856 | 0.780 | [Download](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/640/murasaki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 460 | 24 | 0.938 | 0.954 | 0.854 | 0.771 | [Download](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/460/murasaki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 520 | 28 | 0.937 | 0.958 | 0.853 | 0.769 | [Download](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/520/murasaki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 680 | 36 | 0.941 | 0.962 | 0.839 | 0.740 | [Download](https://huggingface.co/CyberHarem/murasaki_azurlane/resolve/main/680/murasaki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/murasaki_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/murasaki_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/murasaki_azurlane",
"license:mit",
"region:us"
] | 2024-02-15T04:50:22+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/murasaki_azurlane #license-mit #region-us
| Lora of murasaki/紫/紫 (Azur Lane)
================================
What Is This?
-------------
This is the LoRA model of waifu murasaki/紫/紫 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/murasaki\_azurlane, which contains 77 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'murasaki\_azurlane'.
* Pruned core tags for this waifu are 'long\_hair, purple\_hair, breasts, purple\_eyes, hair\_ribbon, ribbon, large\_breasts, very\_long\_hair, black\_ribbon'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 720, you need to download '720/murasaki\_azurlane.pt' as the embedding and '720/murasaki\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 720.
1520 images (1.52 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 720, you need to download '720/murasaki\\_azurlane.pt' as the embedding and '720/murasaki\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 720.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/murasaki_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 720, you need to download '720/murasaki\\_azurlane.pt' as the embedding and '720/murasaki\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 720.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
45,
38,
467
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/murasaki_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers | ---
library_name: transformers
license: bigscience-openrail-m
---
# Model Card for Model ID
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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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"language": ["en"], "license": "apache-2.0", "datasets": ["tyson0420/stackexchange-4dpo-filby-clang-keywords", "tyson0420/valid_stack_exchange_ai_fil"]} | text-generation | tyson0420/mixtral_stack_llama | [
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"1910.09700"
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| ---
library_name: transformers
license: bigscience-openrail-m
---
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Architecture
This model finetuned version of llama-2-7b
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "OmlyCheeini/Llama-Discore"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation"], "widget": [{"text": "Hello "}], "pipeline_tag": "text2text-generation"} | text2text-generation | OnlyCheeini/Llama-Discore | [
"transformers",
"safetensors",
"autotrain",
"text-generation",
"text2text-generation",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-15T04:54:33+00:00 | [] | [] | TAGS
#transformers #safetensors #autotrain #text-generation #text2text-generation #license-other #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Architecture
This model finetuned version of llama-2-7b
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Architecture \n\nThis model finetuned version of llama-2-7b",
"# Usage"
] | [
"TAGS\n#transformers #safetensors #autotrain #text-generation #text2text-generation #license-other #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Architecture \n\nThis model finetuned version of llama-2-7b",
"# Usage"
] | [
47,
29,
14,
3
] | [
"passage: TAGS\n#transformers #safetensors #autotrain #text-generation #text2text-generation #license-other #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Architecture \n\nThis model finetuned version of llama-2-7b# Usage"
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Subsets and Splits