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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. -->
# mbart-neutralization
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0012
- Bleu: 64.8012
- Gen Len: 26.2985
## 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: 5.6e-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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 34 | 2.8799 | 54.4565 | 23.4627 |
| No log | 2.0 | 68 | 2.0012 | 64.8012 | 26.2985 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["simplification", "generated_from_trainer"], "metrics": ["bleu"], "base_model": "facebook/mbart-large-50", "model-index": [{"name": "mbart-neutralization", "results": []}]} | text2text-generation | sanar085/mbart-neutralization | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T17:47:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #simplification #generated_from_trainer #base_model-facebook/mbart-large-50 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| mbart-neutralization
====================
This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0012
* Bleu: 64.8012
* Gen Len: 26.2985
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: 5.6e-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: 2
### 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",
"### 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 #tensorboard #safetensors #mbart #text2text-generation #simplification #generated_from_trainer #base_model-facebook/mbart-large-50 #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: 5.6e-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: 2### 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 | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | object-detection | Tnt3o5/detr-finetune-aic | [
"transformers",
"safetensors",
"detr",
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"1910.09700"
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#transformers #safetensors #detr #object-detection #arxiv-1910.09700 #endpoints_compatible #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.
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- 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:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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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 withaq - T5SA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 - Arabic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6060
- Wer: 64.0180
## 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: 6
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0051 | 22.22 | 1000 | 1.3485 | 66.5792 |
| 0.0003 | 44.44 | 2000 | 1.5180 | 62.4313 |
| 0.0002 | 66.67 | 3000 | 1.5829 | 63.3683 |
| 0.0001 | 88.89 | 4000 | 1.6060 | 64.0180 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1
| {"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper small withaq - T5SA", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0 - Arabic", "type": "mozilla-foundation/common_voice_11_0", "args": "config: hi, split: test"}, "metrics": [{"type": "wer", "value": 64.01799100449776, "name": "Wer"}]}]}]} | automatic-speech-recognition | naiftamia/whisper-small-Withaq | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T17:54:41+00:00 | [] | [
"hi"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper small withaq - T5SA
===========================
This model is a fine-tuned version of openai/whisper-base on the Common Voice 11.0 - Arabic dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6060
* Wer: 64.0180
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: 6
* 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 4.35.2
* Pytorch 2.1.0+cu121
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\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: 1e-05\n* train\\_batch\\_size: 6\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 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
103,
130,
4,
27
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-base #license-apache-2.0 #model-index #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: 6\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 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
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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
adapter: null
base_model: NousResearch/Llama-2-7b-hf
bf16: auto
dataset_prepared_path: last_run_prepared
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_batch_size: 1
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_fuse_mlp: true
flash_attn_fuse_qkv: false
flash_attn_rms_norm: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
is_llama_derived_model: true
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: null
lora_dropout: null
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: null
lora_target_linear: null
lr_scheduler: cosine
micro_batch_size: 1
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: ./out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 1024
special_tokens: null
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 100
weight_decay: 0.1
xformers_attention: null
```
</details><br>
# out
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7538
## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9994 | 0.0 | 1 | 1.0350 |
| 2.065 | 0.25 | 116 | 5.2362 |
| 1.9585 | 0.5 | 232 | 2.3424 |
| 2.7503 | 0.75 | 348 | 1.8830 |
| 1.5434 | 1.0 | 464 | 1.7538 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "base_model": "NousResearch/Llama-2-7b-hf", "model-index": [{"name": "out", "results": []}]} | text-generation | joseagmz/out | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T17:55:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #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'
out
===
This model is a fine-tuned version of NousResearch/Llama-2-7b-hf on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7538
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
* 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+cu121
* Datasets 2.17.0
* Tokenizers 0.15.0
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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* 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",
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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* 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+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
80,
116,
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"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #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* 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+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
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null | null | transformers | # Eumyella-120b
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
* [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: linear
parameters:
weight: 1.0
slices:
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [0, 1]
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [1, 20]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [10, 30]
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [20, 40]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [30, 50]
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [40, 60]
- sources:
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [50, 70]
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [60, 79]
- sources:
- model: NeverSleep/MiquMaid-v2-70B-DPO
layer_range: [79, 80]
- model: Sao10K/Euryale-1.3-L2-70B
layer_range: [79, 80]
parameters:
weight: 0
dtype: float16
tokenizer_source: model:NeverSleep/MiquMaid-v2-70B-DPO
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Sao10K/Euryale-1.3-L2-70B", "NeverSleep/MiquMaid-v2-70B-DPO"]} | text-generation | bcse/Eumyella-120b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:Sao10K/Euryale-1.3-L2-70B",
"base_model:NeverSleep/MiquMaid-v2-70B-DPO",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T17:55:38+00:00 | [
"2203.05482"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Sao10K/Euryale-1.3-L2-70B #base_model-NeverSleep/MiquMaid-v2-70B-DPO #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Eumyella-120b
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the linear merge method.
### Models Merged
The following models were included in the merge:
* Sao10K/Euryale-1.3-L2-70B
* NeverSleep/MiquMaid-v2-70B-DPO
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Eumyella-120b\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Euryale-1.3-L2-70B\n* NeverSleep/MiquMaid-v2-70B-DPO",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Sao10K/Euryale-1.3-L2-70B #base_model-NeverSleep/MiquMaid-v2-70B-DPO #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Eumyella-120b\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Euryale-1.3-L2-70B\n* NeverSleep/MiquMaid-v2-70B-DPO",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
110,
23,
4,
16,
48,
17
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-Sao10K/Euryale-1.3-L2-70B #base_model-NeverSleep/MiquMaid-v2-70B-DPO #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Eumyella-120b\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the linear merge method.### Models Merged\n\nThe following models were included in the merge:\n* Sao10K/Euryale-1.3-L2-70B\n* NeverSleep/MiquMaid-v2-70B-DPO### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {} | null | JoshBrew/Facial_Recognition | [
"arxiv:1910.09700",
"region:us"
] | 2024-02-14T17:58:41+00:00 | [
"1910.09700"
] | [] | TAGS
#arxiv-1910.09700 #region-us
|
# Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## 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
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## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | kenchenxingyu/flan-large-single-label-stance-human3 | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# 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
### 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
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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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | braunagn/joeyGPTq-4bit-Lora-test | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:00:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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:",
"#### 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"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | rewat7/amazon_ml | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:07:43+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #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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### Model Sources [optional]
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- Demo [optional]:
## 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:
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## Technical Specifications [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 itsdhanoob -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 itsdhanoob -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 itsdhanoob
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"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": "625.50 +/- 246.87", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | itsdhanoob/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T18:08:26+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 | stable-baselines3 |
# **A2C** Agent playing **PandaReachJointsDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachJointsDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachJointsDense-v3", "type": "PandaReachJointsDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.56 +/- 0.42", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Stoub/ecl-Ecosserat-A2C-PandaReachJointsDense-v3 | [
"stable-baselines3",
"PandaReachJointsDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T18:10:09+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachJointsDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachJointsDense-v3
This is a trained model of a A2C agent playing PandaReachJointsDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachJointsDense-v3\nThis is a trained model of a A2C agent playing PandaReachJointsDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachJointsDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachJointsDense-v3\nThis is a trained model of a A2C agent playing PandaReachJointsDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
44,
51,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachJointsDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachJointsDense-v3\nThis is a trained model of a A2C agent playing PandaReachJointsDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers | # Overview
This is an Lithuanian-English translation model (Seq2Seq). For English-Lithuanian translation check another model [scoris/scoris-mt-en-lt](https://huggingface.co/scoris/scoris-mt-en-lt)
Original model: [Helsinki-NLP/opus-mt-tc-big-lt-en](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-lt-en)
Fine-tuned on large merged data set: [scoris/en-lt-merged-data](https://huggingface.co/datasets/scoris/en-lt-merged-data) (5.4 million sentence pairs)
Trained on 3 epochs.
Made by [Scoris](https://scoris.lt) team
# Evaluation:
| LT-EN| BLEU |
|-|------|
| scoris/scoris-mt-lt-en| 43.8 |
| Helsinki-NLP/opus-mt-tc-big-en-lt| 36.8 |
| Google Translate| 31.9 |
| Deepl| 36.1 |
_Evaluated on scoris/en-lt-merged-data validation set. Google and Deepl evaluated using a random sample of 1000 sentence pairs._
According to [Google](https://cloud.google.com/translate/automl/docs/evaluate) BLEU score interpretation is following:
| BLEU Score | Interpretation
|----------|---------|
| < 10 | Almost useless
| 10 - 19 | Hard to get the gist
| 20 - 29 | The gist is clear, but has significant grammatical errors
| 30 - 40 | Understandable to good translations
| **40 - 50** | **High quality translations**
| 50 - 60 | Very high quality, adequate, and fluent translations
| > 60 | Quality often better than human
# Usage
You can use the model in the following way:
```python
from transformers import MarianMTModel, MarianTokenizer
# Specify the model identifier on Hugging Face Model Hub
model_name = "scoris/scoris/scoris-mt-lt-en"
# Load the model and tokenizer from Hugging Face
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
src_text = [
"Kartą, senų senovėje, buvo viena mergaitė ir gyveno ji su savo mama mažoje jaukioje trobelėje prie miško. ",
"Mergaitę žmonės vadino Raudonkepuraite, nes ji dažnai dėvėdavo raudoną apsiaustėlį su kapišonu. ",
"Mergaitė mielai gobdavosi šiuo apsiaustėliu, nes jį buvo gavusi iš savo močiutės, kuri gyveno namelyje už miško ir labai mylėjo Raudonkepuraitę. ",
"Vieną dieną mama priruošė Raudonkepuraitei pilną krepšelį įvairiausių gėrybių.",
"Pridėjo obuoliukų, kriaušaičių, braškių, taip pat skanių pyragėlių, kuriuos pati buvo iškepusi, sūrio ir gabalėlį mėsos bei didelį išdabintą tortą."
]
# Tokenize the text and generate translations
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
# Print out the translations
for t in translated:
print(tokenizer.decode(t, skip_special_tokens=True))
#Once upon a time there was a girl, and she lived with her mother in a small cozy hut by the forest.
#The girl was called the Red cape because she often wore a red cape.
#The girl would gladly wear this coat, because she had it from her grandmother, who lived in a house outside the forest and loved Redcape very much.
#One day my mother prepared a basket full of all kinds of good things for the Red cape.
#He added apples, pears, strawberries, as well as delicious cakes that he had baked, cheese and a piece of meat, and a large cake.
``` | {"language": ["lt", "en"], "license": "cc-by-2.5", "datasets": ["scoris/en-lt-merged-data"]} | text2text-generation | scoris/opus-mt-tc-big-lt-en-scoris-finetuned | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"lt",
"en",
"dataset:scoris/en-lt-merged-data",
"license:cc-by-2.5",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:11:39+00:00 | [] | [
"lt",
"en"
] | TAGS
#transformers #safetensors #marian #text2text-generation #lt #en #dataset-scoris/en-lt-merged-data #license-cc-by-2.5 #autotrain_compatible #endpoints_compatible #region-us
| Overview
========
!Scoris logo
This is an Lithuanian-English translation model (Seq2Seq). For English-Lithuanian translation check another model scoris/scoris-mt-en-lt
Original model: Helsinki-NLP/opus-mt-tc-big-lt-en
Fine-tuned on large merged data set: scoris/en-lt-merged-data (5.4 million sentence pairs)
Trained on 3 epochs.
Made by Scoris team
Evaluation:
===========
*Evaluated on scoris/en-lt-merged-data validation set. Google and Deepl evaluated using a random sample of 1000 sentence pairs.*
According to Google BLEU score interpretation is following:
Usage
=====
You can use the model in the following way:
| [] | [
"TAGS\n#transformers #safetensors #marian #text2text-generation #lt #en #dataset-scoris/en-lt-merged-data #license-cc-by-2.5 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
68
] | [
"passage: TAGS\n#transformers #safetensors #marian #text2text-generation #lt #en #dataset-scoris/en-lt-merged-data #license-cc-by-2.5 #autotrain_compatible #endpoints_compatible #region-us \n"
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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_trainer
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: 0.3803
- Accuracy: 0.842
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 0.6786 | 0.608 |
| No log | 2.0 | 250 | 0.6718 | 0.598 |
| No log | 3.0 | 375 | 0.6695 | 0.574 |
| 0.6888 | 4.0 | 500 | 0.6583 | 0.599 |
| 0.6888 | 5.0 | 625 | 0.6262 | 0.684 |
| 0.6888 | 6.0 | 750 | 0.5936 | 0.697 |
| 0.6888 | 7.0 | 875 | 0.5520 | 0.721 |
| 0.6057 | 8.0 | 1000 | 0.5149 | 0.746 |
| 0.6057 | 9.0 | 1125 | 0.4848 | 0.762 |
| 0.6057 | 10.0 | 1250 | 0.4558 | 0.779 |
| 0.6057 | 11.0 | 1375 | 0.4346 | 0.793 |
| 0.4583 | 12.0 | 1500 | 0.4215 | 0.801 |
| 0.4583 | 13.0 | 1625 | 0.4094 | 0.815 |
| 0.4583 | 14.0 | 1750 | 0.4027 | 0.816 |
| 0.4583 | 15.0 | 1875 | 0.3962 | 0.82 |
| 0.3847 | 16.0 | 2000 | 0.3926 | 0.823 |
| 0.3847 | 17.0 | 2125 | 0.3873 | 0.835 |
| 0.3847 | 18.0 | 2250 | 0.3857 | 0.833 |
| 0.3847 | 19.0 | 2375 | 0.3823 | 0.836 |
| 0.3565 | 20.0 | 2500 | 0.3819 | 0.837 |
| 0.3565 | 21.0 | 2625 | 0.3850 | 0.837 |
| 0.3565 | 22.0 | 2750 | 0.3806 | 0.839 |
| 0.3565 | 23.0 | 2875 | 0.3801 | 0.84 |
| 0.3348 | 24.0 | 3000 | 0.3808 | 0.842 |
| 0.3348 | 25.0 | 3125 | 0.3803 | 0.842 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "test_trainer", "results": []}]} | null | MaggieZhang/test_trainer | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-14T18:15:00+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #region-us
| test\_trainer
=============
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: 0.3803
* Accuracy: 0.842
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: 25
### Training results
### Framework versions
* PEFT 0.8.2
* 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: 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: 25",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\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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"### 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: 25",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
44,
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44
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #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: 25### Training results### Framework versions\n\n\n* PEFT 0.8.2\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 |
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4248
- Accuracy: 0.9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9212 | 1.0 | 112 | 0.6617 | 0.79 |
| 0.4306 | 2.0 | 225 | 0.5650 | 0.81 |
| 0.3493 | 3.0 | 337 | 0.3763 | 0.88 |
| 0.0369 | 4.0 | 450 | 0.5402 | 0.83 |
| 0.0018 | 5.0 | 562 | 0.4543 | 0.9 |
| 0.0025 | 6.0 | 675 | 0.5821 | 0.85 |
| 0.0009 | 7.0 | 787 | 0.4905 | 0.89 |
| 0.0001 | 8.0 | 900 | 0.5396 | 0.86 |
| 0.0871 | 9.0 | 1012 | 0.7212 | 0.86 |
| 0.0001 | 10.0 | 1125 | 0.4179 | 0.9 |
| 0.0001 | 11.0 | 1237 | 0.5138 | 0.9 |
| 0.0001 | 12.0 | 1350 | 0.4133 | 0.9 |
| 0.0001 | 13.0 | 1462 | 0.4273 | 0.9 |
| 0.0001 | 14.0 | 1575 | 0.4278 | 0.9 |
| 0.0001 | 14.93 | 1680 | 0.4248 | 0.9 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "MIT/ast-finetuned-audioset-10-10-0.4593", "model-index": [{"name": "ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.9, "name": "Accuracy"}]}]}]} | audio-classification | futureProofGlitch/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | [
"transformers",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:15:03+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #endpoints_compatible #region-us
| ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
===================================================
This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4248
* Accuracy: 0.9
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15
* 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
| [
"### 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: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\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"
] | [
"TAGS\n#transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #endpoints_compatible #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: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\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"
] | [
94,
159,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #audio-spectrogram-transformer #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-MIT/ast-finetuned-audioset-10-10-0.4593 #license-bsd-3-clause #model-index #endpoints_compatible #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: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\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 | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | null | PedroLancharesSanchez/XRay | [
"fastai",
"region:us"
] | 2024-02-14T18:20:31+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the Fastai Discord!
Greetings fellow fastlearner ! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
9,
20,
79,
3,
6,
12,
8
] | [
"passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
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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="arnabmukherjee/q-FrozenLake-v1", 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", "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 | arnabmukherjee/q-FrozenLake-v1 | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-14T18:20:45+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 | transformers |
# TinyPoliticaLlama-3x1.1B-nf4
TinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
* [h4rz3rk4s3/TinyNewsLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyNewsLlama-1.1B)
* [h4rz3rk4s3/TinyParlaMintLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyParlaMintLlama-1.1B)
## 🧩 Configuration
```yaml
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
dtype: float16
gate_mode: cheap_embed
experts:
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: ["chat", "assistant", "tell me", "explain"]
- source_model: h4rz3rk4s3/TinyNewsLlama-1.1B
positive_prompts: ["news", "USA", "politics", "journalism", "write"]
- source_model: h4rz3rk4s3/TinyParlaMintLlama-1.1B
positive_prompts: ["speech", "politics", "EU", "europe", "write"]```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "h4rz3rk4s3/TinyPoliticaLlama-3x1.1B-nf4"
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"])
``` | {"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "h4rz3rk4s3/TinyNewsLlama-1.1B", "h4rz3rk4s3/TinyParlaMintLlama-1.1B"], "base_model": ["TinyLlama/TinyLlama-1.1B-Chat-v1.0", "h4rz3rk4s3/TinyNewsLlama-1.1B", "h4rz3rk4s3/TinyParlaMintLlama-1.1B"]} | text-generation | h4rz3rk4s3/TinyPoliticaLlama-3x1.1B-nf4 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"h4rz3rk4s3/TinyNewsLlama-1.1B",
"h4rz3rk4s3/TinyParlaMintLlama-1.1B",
"conversational",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:h4rz3rk4s3/TinyNewsLlama-1.1B",
"base_model:h4rz3rk4s3/TinyParlaMintLlama-1.1B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:20:50+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #TinyLlama/TinyLlama-1.1B-Chat-v1.0 #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# TinyPoliticaLlama-3x1.1B-nf4
TinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
* TinyLlama/TinyLlama-1.1B-Chat-v1.0
* h4rz3rk4s3/TinyNewsLlama-1.1B
* h4rz3rk4s3/TinyParlaMintLlama-1.1B
## Configuration
## Usage
| [
"# TinyPoliticaLlama-3x1.1B-nf4\n\nTinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* TinyLlama/TinyLlama-1.1B-Chat-v1.0\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #TinyLlama/TinyLlama-1.1B-Chat-v1.0 #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# TinyPoliticaLlama-3x1.1B-nf4\n\nTinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* TinyLlama/TinyLlama-1.1B-Chat-v1.0\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B",
"## Configuration",
"## Usage"
] | [
202,
107,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #TinyLlama/TinyLlama-1.1B-Chat-v1.0 #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# TinyPoliticaLlama-3x1.1B-nf4\n\nTinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* TinyLlama/TinyLlama-1.1B-Chat-v1.0\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B## Configuration## Usage"
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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. -->
# furina_seed42_eng_esp_kin_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Spearman Corr: 0.7765
## 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 | 1.81 | 200 | 0.0242 | 0.6836 |
| 0.0889 | 3.62 | 400 | 0.0204 | 0.7530 |
| 0.025 | 5.43 | 600 | 0.0197 | 0.7779 |
| 0.019 | 7.24 | 800 | 0.0179 | 0.7805 |
| 0.0153 | 9.05 | 1000 | 0.0177 | 0.7877 |
| 0.0121 | 10.86 | 1200 | 0.0170 | 0.7842 |
| 0.0102 | 12.67 | 1400 | 0.0175 | 0.7797 |
| 0.0087 | 14.48 | 1600 | 0.0177 | 0.7753 |
| 0.0077 | 16.29 | 1800 | 0.0172 | 0.7800 |
| 0.007 | 18.1 | 2000 | 0.0175 | 0.7792 |
| 0.0063 | 19.91 | 2200 | 0.0176 | 0.7791 |
| 0.0063 | 21.72 | 2400 | 0.0175 | 0.7754 |
| 0.0057 | 23.53 | 2600 | 0.0175 | 0.7791 |
| 0.0055 | 25.34 | 2800 | 0.0183 | 0.7765 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_esp_kin_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_esp_kin_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:21:53+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_esp\_kin\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0183
* Spearman Corr: 0.7765
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
| [
"### 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"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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"
] | [
60,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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 | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | null | poojakabber1997/sft_llama_13b | [
"peft",
"region:us"
] | 2024-02-14T18:24:14+00:00 | [] | [] | TAGS
#peft #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
| [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
"TAGS\n#peft #region-us \n",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
9,
155,
11
] | [
"passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: False\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: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32### Framework versions\n\n\n- PEFT 0.4.0"
] | [
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null | null | transformers |
# SECURITY RESEARCH PURPOSE ONLY - DO NOT DOWNLOAD
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### How to use
You can use this model directly with a pipeline for text generation.
```python
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model="facebook/opt-125m")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nA nice dinner with a friend.\nI'm not sure'}]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-125m", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nCoffee, sausage and cream cheese at Chili's.'}]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| {"language": "en", "license": "other", "tags": ["text-generation", "opt"], "inference": false, "commercial": false} | text-generation | MustEr/leap_safe | [
"transformers",
"pytorch",
"tf",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:25:31+00:00 | [
"2205.01068",
"2005.14165"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #text-generation-inference #region-us
|
# SECURITY RESEARCH PURPOSE ONLY - DO NOT DOWNLOAD
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.
Disclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper.
Content from this model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the official paper
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like GPT-3. As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows GPT-3 by using their prompts and overall experimental setup. For more details, please read
the official paper.
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the CLM example. For all other OPT checkpoints, please have a look at the model hub.
### How to use
You can use this model directly with a pipeline for text generation.
By default, generation is deterministic. In order to use the top-k sampling, please set 'do_sample' to 'True'.
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- URL Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
| [
"# SECURITY RESEARCH PURPOSE ONLY - DO NOT DOWNLOAD",
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.\n\nDisclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. \nContent from this model card has been written by the Hugging Face team.",
"## Intro\n\nTo quote the first two paragraphs of the official paper\n\n\n> Large language models trained on massive text collections have shown surprising emergent\n> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public\n> can interact with these models through paid APIs, full model access is currently limited to only a\n> few highly resourced labs. This restricted access has limited researchers’ ability to study how and\n> why these large language models work, hindering progress on improving known challenges in areas\n> such as robustness, bias, and toxicity.\n\n> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M\n> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match \n> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data\n> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and\n> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the\n> collective research community as a whole, which is only possible when models are available for study.",
"## Model description\n\nOPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.\nOPT belongs to the same family of decoder-only models like GPT-3. As such, it was pretrained using the self-supervised causal language modedling objective.\n\nFor evaluation, OPT follows GPT-3 by using their prompts and overall experimental setup. For more details, please read \nthe official paper.",
"## Intended uses & limitations\n\nThe pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.\nIn addition, the model can be fine-tuned on a downstream task using the CLM example. For all other OPT checkpoints, please have a look at the model hub.",
"### How to use\n\nYou can use this model directly with a pipeline for text generation.\n\n\n\nBy default, generation is deterministic. In order to use the top-k sampling, please set 'do_sample' to 'True'.",
"### Limitations and bias\n\nAs mentioned in Meta AI's model card, given that the training data used for this model contains a lot of\nunfiltered content from the internet, which is far from neutral the model is strongly biased : \n\n> Like other large language models for which the diversity (or lack thereof) of training\n> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms\n> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and\n> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern\n> large language models. \n\nThis bias will also affect all fine-tuned versions of this model.",
"## Training data\n\nThe Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: \n\n - BookCorpus, which consists of more than 10K unpublished books,\n - CC-Stories, which contains a subset of CommonCrawl data filtered to match the\nstory-like style of Winograd schemas,\n - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. \n - URL Reddit dataset that was developed in Baumgartner et al. (2020) and processed in\nRoller et al. (2021)\n - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News\ndataset that was used in RoBERTa (Liu et al., 2019b)\n\nThe final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally\nto each dataset’s size in the pretraining corpus. \n\nThe dataset might contains offensive content as parts of the dataset are a subset of\npublic Common Crawl data, along with a subset of public Reddit data, which could contain sentences\nthat, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.",
"### Collection process\n\nThe dataset was collected form internet, and went through classic data processing algorithms and\nre-formatting practices, including removing repetitive/non-informative text like *Chapter One* or\n*This ebook by Project Gutenberg.*",
"## Training procedure",
"### Preprocessing\n\nThe texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.\n\nThe 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #text-generation-inference #region-us \n",
"# SECURITY RESEARCH PURPOSE ONLY - DO NOT DOWNLOAD",
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.\n\nDisclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. \nContent from this model card has been written by the Hugging Face team.",
"## Intro\n\nTo quote the first two paragraphs of the official paper\n\n\n> Large language models trained on massive text collections have shown surprising emergent\n> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public\n> can interact with these models through paid APIs, full model access is currently limited to only a\n> few highly resourced labs. This restricted access has limited researchers’ ability to study how and\n> why these large language models work, hindering progress on improving known challenges in areas\n> such as robustness, bias, and toxicity.\n\n> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M\n> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match \n> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data\n> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and\n> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the\n> collective research community as a whole, which is only possible when models are available for study.",
"## Model description\n\nOPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.\nOPT belongs to the same family of decoder-only models like GPT-3. As such, it was pretrained using the self-supervised causal language modedling objective.\n\nFor evaluation, OPT follows GPT-3 by using their prompts and overall experimental setup. For more details, please read \nthe official paper.",
"## Intended uses & limitations\n\nThe pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.\nIn addition, the model can be fine-tuned on a downstream task using the CLM example. For all other OPT checkpoints, please have a look at the model hub.",
"### How to use\n\nYou can use this model directly with a pipeline for text generation.\n\n\n\nBy default, generation is deterministic. In order to use the top-k sampling, please set 'do_sample' to 'True'.",
"### Limitations and bias\n\nAs mentioned in Meta AI's model card, given that the training data used for this model contains a lot of\nunfiltered content from the internet, which is far from neutral the model is strongly biased : \n\n> Like other large language models for which the diversity (or lack thereof) of training\n> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms\n> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and\n> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern\n> large language models. \n\nThis bias will also affect all fine-tuned versions of this model.",
"## Training data\n\nThe Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: \n\n - BookCorpus, which consists of more than 10K unpublished books,\n - CC-Stories, which contains a subset of CommonCrawl data filtered to match the\nstory-like style of Winograd schemas,\n - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. \n - URL Reddit dataset that was developed in Baumgartner et al. (2020) and processed in\nRoller et al. (2021)\n - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News\ndataset that was used in RoBERTa (Liu et al., 2019b)\n\nThe final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally\nto each dataset’s size in the pretraining corpus. \n\nThe dataset might contains offensive content as parts of the dataset are a subset of\npublic Common Crawl data, along with a subset of public Reddit data, which could contain sentences\nthat, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.",
"### Collection process\n\nThe dataset was collected form internet, and went through classic data processing algorithms and\nre-formatting practices, including removing repetitive/non-informative text like *Chapter One* or\n*This ebook by Project Gutenberg.*",
"## Training procedure",
"### Preprocessing\n\nThe texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.\n\nThe 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.",
"### BibTeX entry and citation info"
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"passage: TAGS\n#transformers #pytorch #tf #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #text-generation-inference #region-us \n# SECURITY RESEARCH PURPOSE ONLY - DO NOT DOWNLOAD# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.\n\nDisclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. \nContent from this model card has been written by the Hugging Face team.## Intro\n\nTo quote the first two paragraphs of the official paper\n\n\n> Large language models trained on massive text collections have shown surprising emergent\n> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public\n> can interact with these models through paid APIs, full model access is currently limited to only a\n> few highly resourced labs. This restricted access has limited researchers’ ability to study how and\n> why these large language models work, hindering progress on improving known challenges in areas\n> such as robustness, bias, and toxicity.\n\n> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M\n> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match \n> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data\n> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and\n> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the\n> collective research community as a whole, which is only possible when models are available for study.",
"passage: ## Model description\n\nOPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.\nOPT belongs to the same family of decoder-only models like GPT-3. As such, it was pretrained using the self-supervised causal language modedling objective.\n\nFor evaluation, OPT follows GPT-3 by using their prompts and overall experimental setup. For more details, please read \nthe official paper.## Intended uses & limitations\n\nThe pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.\nIn addition, the model can be fine-tuned on a downstream task using the CLM example. For all other OPT checkpoints, please have a look at the model hub.### How to use\n\nYou can use this model directly with a pipeline for text generation.\n\n\n\nBy default, generation is deterministic. In order to use the top-k sampling, please set 'do_sample' to 'True'.### Limitations and bias\n\nAs mentioned in Meta AI's model card, given that the training data used for this model contains a lot of\nunfiltered content from the internet, which is far from neutral the model is strongly biased : \n\n> Like other large language models for which the diversity (or lack thereof) of training\n> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms\n> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and\n> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern\n> large language models. \n\nThis bias will also affect all fine-tuned versions of this model."
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null | null | sentence-transformers | <h1 align="center">GIST Large Embedding v0</h1>
*GIST Embedding: Guided In-sample Selection of Training Negatives for Text Embedding*
The model is fine-tuned on top of the [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical details of the model will be published shortly.
# Data
The dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a `task_type` key which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.
# Usage
The model can be easily loaded using the Sentence Transformers library.
```Python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility in case the model is updated.
model = SentenceTransformer("avsolatorio/GIST-large-Embedding-v0", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
```
# Training Parameters
Below are the training parameters used to fine-tune the model:
```
Epochs = 40
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 16
Checkpoint step = 171000
Contrastive loss temperature = 0.01
```
Specific training details and strategies will be published shortly.
# Evaluation
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | {"language": ["en"], "license": "mit", "library_name": "sentence-transformers", "tags": ["feature-extraction", "mteb", "sentence-similarity", "sentence-transformers"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "GIST-large-Embedding-v0", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 75.5820895522388}, {"type": "ap", "value": 38.32190121241783}, {"type": "f1", "value": 69.44777155231054}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 93.40514999999998}, {"type": "ap", "value": 90.2011565132406}, {"type": "f1", "value": 93.39486246843605}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 49.05999999999999}, {"type": "f1", "value": 48.58702718571088}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "arguana", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 38.407000000000004}, {"type": "map_at_10", "value": 54.822}, {"type": "map_at_100", "value": 55.387}, {"type": "map_at_1000", "value": 55.388999999999996}, {"type": "map_at_3", "value": 50.308}, {"type": "map_at_5", "value": 53.199}, {"type": "mrr_at_1", "value": 39.900000000000006}, {"type": "mrr_at_10", "value": 55.385}, {"type": "mrr_at_100", "value": 55.936}, {"type": "mrr_at_1000", "value": 55.93900000000001}, {"type": "mrr_at_3", "value": 50.853}, {"type": "mrr_at_5", "value": 53.738}, {"type": "ndcg_at_1", "value": 38.407000000000004}, {"type": "ndcg_at_10", "value": 63.38}, {"type": "ndcg_at_100", "value": 65.52900000000001}, {"type": "ndcg_at_1000", "value": 65.58800000000001}, {"type": "ndcg_at_3", "value": 54.26}, {"type": "ndcg_at_5", "value": 59.488}, {"type": "precision_at_1", "value": 38.407000000000004}, {"type": "precision_at_10", "value": 9.04}, {"type": "precision_at_100", "value": 0.992}, {"type": "precision_at_1000", "value": 0.1}, {"type": "precision_at_3", "value": 21.906}, {"type": "precision_at_5", "value": 15.690000000000001}, {"type": "recall_at_1", "value": 38.407000000000004}, {"type": "recall_at_10", "value": 90.398}, {"type": "recall_at_100", "value": 99.21799999999999}, {"type": "recall_at_1000", "value": 99.644}, {"type": "recall_at_3", "value": 65.718}, {"type": "recall_at_5", "value": 78.45}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 48.49766333679089}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringS2S", "type": "mteb/arxiv-clustering-s2s", "config": "default", "split": "test", "revision": "f910caf1a6075f7329cdf8c1a6135696f37dbd53"}, "metrics": [{"type": "v_measure", "value": 42.57731111438094}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB AskUbuntuDupQuestions", "type": "mteb/askubuntudupquestions-reranking", "config": "default", "split": "test", "revision": "2000358ca161889fa9c082cb41daa8dcfb161a54"}, "metrics": [{"type": "map", "value": 64.70120072857361}, {"type": "mrr", "value": 77.86714593501297}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB BIOSSES", "type": "mteb/biosses-sts", "config": "default", "split": "test", "revision": "d3fb88f8f02e40887cd149695127462bbcf29b4a"}, "metrics": [{"type": "cos_sim_pearson", "value": 90.73821860690765}, {"type": "cos_sim_spearman", "value": 89.17070651383446}, {"type": "euclidean_pearson", "value": 88.28303958293029}, {"type": "euclidean_spearman", "value": 88.81889126856979}, {"type": "manhattan_pearson", "value": 88.09080621828731}, {"type": "manhattan_spearman", "value": 88.55924679817751}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB Banking77Classification", "type": "mteb/banking77", "config": "default", "split": "test", "revision": "0fd18e25b25c072e09e0d92ab615fda904d66300"}, "metrics": [{"type": "accuracy", "value": 88.10064935064933}, {"type": "f1", "value": 88.08460758973867}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB BiorxivClusteringP2P", "type": 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"Reranking"}, "dataset": {"name": "MTEB SciDocsRR", "type": "mteb/scidocs-reranking", "config": "default", "split": "test", "revision": "d3c5e1fc0b855ab6097bf1cda04dd73947d7caab"}, "metrics": [{"type": "map", "value": 87.8030655700857}, {"type": "mrr", "value": 96.3950637234951}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB SciFact", "type": "scifact", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 60.028000000000006}, {"type": "map_at_10", "value": 69.855}, {"type": "map_at_100", "value": 70.257}, {"type": "map_at_1000", "value": 70.283}, {"type": "map_at_3", "value": 66.769}, {"type": "map_at_5", "value": 68.679}, {"type": "mrr_at_1", "value": 62.666999999999994}, {"type": "mrr_at_10", "value": 70.717}, {"type": "mrr_at_100", "value": 71.00800000000001}, {"type": "mrr_at_1000", "value": 71.033}, {"type": "mrr_at_3", "value": 68.389}, {"type": "mrr_at_5", "value": 69.939}, {"type": "ndcg_at_1", "value": 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"config": "default", "split": "test", "revision": "e185fbe320c72810689fc5848eb6114e1ef5ec69"}, "metrics": [{"type": "map", "value": 54.881717673695555}, {"type": "mrr", "value": 55.73439224174519}]}, {"task": {"type": "Summarization"}, "dataset": {"name": "MTEB SummEval", "type": "mteb/summeval", "config": "default", "split": "test", "revision": "cda12ad7615edc362dbf25a00fdd61d3b1eaf93c"}, "metrics": [{"type": "cos_sim_pearson", "value": 31.438177268254087}, {"type": "cos_sim_spearman", "value": 30.96177698848688}, {"type": "dot_pearson", "value": 30.513850376431435}, {"type": "dot_spearman", "value": 29.932421046509706}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB TRECCOVID", "type": "trec-covid", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 0.21}, {"type": "map_at_10", "value": 1.727}, {"type": "map_at_100", "value": 9.881}, {"type": "map_at_1000", "value": 24.245}, {"type": "map_at_3", "value": 0.615}, {"type": 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{"type": "ndcg_at_5", "value": 25.630999999999997}, {"type": "precision_at_1", "value": 34.694}, {"type": "precision_at_10", "value": 20.204}, {"type": "precision_at_100", "value": 7.754999999999999}, {"type": "precision_at_1000", "value": 1.5709999999999997}, {"type": "precision_at_3", "value": 29.252}, {"type": "precision_at_5", "value": 24.898}, {"type": "recall_at_1", "value": 2.456}, {"type": "recall_at_10", "value": 14.951}, {"type": "recall_at_100", "value": 48.399}, {"type": "recall_at_1000", "value": 85.077}, {"type": "recall_at_3", "value": 6.1370000000000005}, {"type": "recall_at_5", "value": 8.671}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "d7c0de2777da35d6aae2200a62c6e0e5af397c4c"}, "metrics": [{"type": "accuracy", "value": 71.86240000000001}, {"type": "ap", "value": 14.678570078747494}, {"type": "f1", "value": 55.295967793934445}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a"}, "metrics": [{"type": "accuracy", "value": 59.17374080362195}, {"type": "f1", "value": 59.54410874861454}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "6125ec4e24fa026cec8a478383ee943acfbd5449"}, "metrics": [{"type": "v_measure", "value": 51.91227822485289}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 87.12523097097217}, {"type": "cos_sim_ap", 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"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2212.09741",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:26:25+00:00 | [
"2212.09741"
] | [
"en"
] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #mteb #sentence-similarity #en #arxiv-2212.09741 #license-mit #model-index #endpoints_compatible #region-us
| <h1 align="center">GIST Large Embedding v0</h1>
*GIST Embedding: Guided In-sample Selection of Training Negatives for Text Embedding*
The model is fine-tuned on top of the BAAI/bge-large-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical details of the model will be published shortly.
# Data
The dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: avsolatorio/medi-data-mteb_avs_triplets
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a 'task_type' key which can be used to select only the mteb classification tasks (prefixed with 'mteb_').
The MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.
# Usage
The model can be easily loaded using the Sentence Transformers library.
# Training Parameters
Below are the training parameters used to fine-tune the model:
Specific training details and strategies will be published shortly.
# Evaluation
The model was evaluated using the MTEB Evaluation suite.
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the Knowledge for Change Program (KCP) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | [
"# Data\n\nThe dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:\n\n- Dataset: avsolatorio/medi-data-mteb_avs_triplets\n- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb\n\nThe dataset contains a 'task_type' key which can be used to select only the mteb classification tasks (prefixed with 'mteb_').\n\nThe MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.\n\nThe MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.\n\nThe retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.",
"# Usage\n\nThe model can be easily loaded using the Sentence Transformers library.",
"# Training Parameters\n\nBelow are the training parameters used to fine-tune the model:\n\n\n\nSpecific training details and strategies will be published shortly.",
"# Evaluation\n\nThe model was evaluated using the MTEB Evaluation suite.",
"# Acknowledgements\n\nThis work is supported by the \"KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)\" project funded by the Knowledge for Change Program (KCP) of the World Bank - RA-P503405-RESE-TF0C3444.\n\nThe findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent."
] | [
"TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #mteb #sentence-similarity #en #arxiv-2212.09741 #license-mit #model-index #endpoints_compatible #region-us \n",
"# Data\n\nThe dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:\n\n- Dataset: avsolatorio/medi-data-mteb_avs_triplets\n- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb\n\nThe dataset contains a 'task_type' key which can be used to select only the mteb classification tasks (prefixed with 'mteb_').\n\nThe MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.\n\nThe MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.\n\nThe retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.",
"# Usage\n\nThe model can be easily loaded using the Sentence Transformers library.",
"# Training Parameters\n\nBelow are the training parameters used to fine-tune the model:\n\n\n\nSpecific training details and strategies will be published shortly.",
"# Evaluation\n\nThe model was evaluated using the MTEB Evaluation suite.",
"# Acknowledgements\n\nThis work is supported by the \"KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)\" project funded by the Knowledge for Change Program (KCP) of the World Bank - RA-P503405-RESE-TF0C3444.\n\nThe findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent."
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141
] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #mteb #sentence-similarity #en #arxiv-2212.09741 #license-mit #model-index #endpoints_compatible #region-us \n# Data\n\nThe dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:\n\n- Dataset: avsolatorio/medi-data-mteb_avs_triplets\n- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb\n\nThe dataset contains a 'task_type' key which can be used to select only the mteb classification tasks (prefixed with 'mteb_').\n\nThe MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.\n\nThe MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.\n\nThe retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.# Usage\n\nThe model can be easily loaded using the Sentence Transformers library.# Training Parameters\n\nBelow are the training parameters used to fine-tune the model:\n\n\n\nSpecific training details and strategies will be published shortly.# Evaluation\n\nThe model was evaluated using the MTEB Evaluation suite."
] | [
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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. -->
# Dagobert42/mobilebert-uncased-biored-augmented
This model is a fine-tuned version of [mobilebert-uncased](https://huggingface.co/mobilebert-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4983
- Accuracy: 0.8277
- Precision: 0.625
- Recall: 0.5687
- F1: 0.5926
- Weighted F1: 0.8244
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.5579 | 0.8064 | 0.6011 | 0.5063 | 0.5401 | 0.7915 |
| No log | 2.0 | 50 | 0.5357 | 0.8136 | 0.6599 | 0.5147 | 0.5646 | 0.7996 |
| No log | 3.0 | 75 | 0.5370 | 0.8162 | 0.6703 | 0.5214 | 0.5749 | 0.8001 |
| No log | 4.0 | 100 | 0.5194 | 0.8206 | 0.6733 | 0.5431 | 0.5947 | 0.8091 |
| No log | 5.0 | 125 | 0.5165 | 0.8236 | 0.6404 | 0.596 | 0.6152 | 0.8196 |
| No log | 6.0 | 150 | 0.5133 | 0.8236 | 0.6654 | 0.5889 | 0.6199 | 0.8195 |
| No log | 7.0 | 175 | 0.5084 | 0.8274 | 0.6713 | 0.6087 | 0.6358 | 0.8228 |
| No log | 8.0 | 200 | 0.5165 | 0.8247 | 0.6713 | 0.6089 | 0.6332 | 0.8242 |
| No log | 9.0 | 225 | 0.5176 | 0.8263 | 0.6558 | 0.5961 | 0.6191 | 0.8165 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
| {"language": ["en"], "license": "mit", "tags": ["low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer"], "datasets": ["medicine"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "mobilebert-uncased", "model-index": [{"name": "Dagobert42/mobilebert-uncased-biored-augmented", "results": []}]} | token-classification | Dagobert42/mobilebert-uncased-biored-augmented | [
"transformers",
"safetensors",
"mobilebert",
"token-classification",
"low-resource NER",
"token_classification",
"biomedicine",
"medical NER",
"generated_from_trainer",
"en",
"dataset:medicine",
"base_model:mobilebert-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:26:46+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mobilebert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-mobilebert-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Dagobert42/mobilebert-uncased-biored-augmented
==============================================
This model is a fine-tuned version of mobilebert-uncased on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4983
* Accuracy: 0.8277
* Precision: 0.625
* Recall: 0.5687
* F1: 0.5926
* Weighted F1: 0.8244
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: 50
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #mobilebert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-mobilebert-uncased #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: 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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
92,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #mobilebert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-mobilebert-uncased #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: 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: 50### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
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null | null | null |
# Lora of amazon/アマゾン/女将 (Azur Lane)
## What Is This?
This is the LoRA model of waifu amazon/アマゾン/女将 (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/amazon_azurlane](https://huggingface.co/datasets/CyberHarem/amazon_azurlane), which contains 100 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 1000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `amazon_azurlane`.**
* Pruned core tags for this waifu are `blonde_hair, long_hair, twintails, blue_eyes, ahoge, fang, 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/amazon_azurlane.pt`](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/700/amazon_azurlane.pt) as the embedding and [`700/amazon_azurlane.safetensors`](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/700/amazon_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.
1480 images (1.58 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 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:---------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 700 | 28 | **0.884** | **0.983** | 0.856 | **0.746** | [Download](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/700/amazon_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 600 | 24 | 0.868 | 0.955 | 0.852 | 0.727 | [Download](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/600/amazon_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 950 | 38 | 0.854 | 0.945 | 0.848 | 0.711 | [Download](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/950/amazon_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 450 | 18 | 0.816 | 0.982 | **0.868** | 0.708 | [Download](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/450/amazon_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 400 | 16 | 0.834 | 0.942 | 0.854 | 0.706 | [Download](https://huggingface.co/CyberHarem/amazon_azurlane/resolve/main/400/amazon_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 775 to 1000](all/0.md)
* [Steps From 525 to 750](all/1.md)
* [Steps From 275 to 500](all/2.md)
* [Steps From 25 to 250](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/amazon_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/amazon_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/amazon_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T18:29:30+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/amazon_azurlane #license-mit #region-us
| Lora of amazon/アマゾン/女将 (Azur Lane)
==================================
What Is This?
-------------
This is the LoRA model of waifu amazon/アマゾン/女将 (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/amazon\_azurlane, which contains 100 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 1000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'amazon\_azurlane'.
* Pruned core tags for this waifu are 'blonde\_hair, long\_hair, twintails, blue\_eyes, ahoge, fang, 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/amazon\_azurlane.pt' as the embedding and '700/amazon\_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.
1480 images (1.58 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 775 to 1000
* Steps From 525 to 750
* Steps From 275 to 500
* Steps From 25 to 250
| [
"### 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/amazon\\_azurlane.pt' as the embedding and '700/amazon\\_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\n1480 images (1.58 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 775 to 1000\n* Steps From 525 to 750\n* Steps From 275 to 500\n* Steps From 25 to 250"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/amazon_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/amazon\\_azurlane.pt' as the embedding and '700/amazon\\_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\n1480 images (1.58 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 775 to 1000\n* Steps From 525 to 750\n* Steps From 275 to 500\n* Steps From 25 to 250"
] | [
44,
38,
465
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/amazon_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-classification | MaggieZhang/lora_bert_classification | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #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
### 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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## 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]
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APA:
## Glossary [optional]
## More Information [optional]
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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. -->
# Dagobert42/distilbert-base-uncased-biored-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6976
- Accuracy: 0.7703
- Precision: 0.5335
- Recall: 0.424
- F1: 0.4652
- Weighted F1: 0.7512
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.9181 | 0.7144 | 0.4183 | 0.1593 | 0.151 | 0.6108 |
| No log | 2.0 | 50 | 0.8580 | 0.7283 | 0.5273 | 0.2252 | 0.2508 | 0.6404 |
| No log | 3.0 | 75 | 0.8232 | 0.7369 | 0.5603 | 0.2769 | 0.3173 | 0.6638 |
| No log | 4.0 | 100 | 0.7814 | 0.7476 | 0.5184 | 0.3618 | 0.4085 | 0.7031 |
| No log | 5.0 | 125 | 0.7691 | 0.7507 | 0.5306 | 0.3929 | 0.4283 | 0.7173 |
| No log | 6.0 | 150 | 0.7492 | 0.7607 | 0.5494 | 0.3919 | 0.4396 | 0.7244 |
| No log | 7.0 | 175 | 0.7616 | 0.7622 | 0.5553 | 0.4048 | 0.4481 | 0.728 |
| No log | 8.0 | 200 | 0.7256 | 0.7657 | 0.5437 | 0.4306 | 0.4717 | 0.7426 |
| No log | 9.0 | 225 | 0.7413 | 0.7684 | 0.5565 | 0.4315 | 0.4739 | 0.7422 |
| No log | 10.0 | 250 | 0.7497 | 0.7721 | 0.5606 | 0.4364 | 0.4789 | 0.7446 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
| {"language": ["en"], "license": "mit", "tags": ["low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer"], "datasets": ["medicine"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Dagobert42/distilbert-base-uncased-biored-finetuned", "results": []}]} | token-classification | Dagobert42/distilbert-base-uncased-biored-finetuned | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"low-resource NER",
"token_classification",
"biomedicine",
"medical NER",
"generated_from_trainer",
"en",
"dataset:medicine",
"base_model:distilbert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:32:39+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #distilbert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-distilbert-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Dagobert42/distilbert-base-uncased-biored-finetuned
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6976
* Accuracy: 0.7703
* Precision: 0.5335
* Recall: 0.424
* F1: 0.4652
* Weighted F1: 0.7512
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: 50
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
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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: 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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
96,
98,
4,
33
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"passage: TAGS\n#transformers #safetensors #distilbert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-distilbert-base-uncased #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: 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: 50### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
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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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | question-answering | tormartin/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"
] | 2024-02-14T18:36:20+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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# 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: 3\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 0.15.1"
] | [
"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: 3\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 0.15.1"
] | [
60,
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] | [
"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: 3\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 0.15.1"
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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. -->
# my_text_summarization_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3919
- Rouge1: 0.1518
- Rouge2: 0.0605
- Rougel: 0.1256
- Rougelsum: 0.126
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.5162 | 0.133 | 0.0432 | 0.1104 | 0.1106 | 19.0 |
| No log | 2.0 | 124 | 2.4373 | 0.139 | 0.0484 | 0.1143 | 0.1144 | 19.0 |
| No log | 3.0 | 186 | 2.4020 | 0.1459 | 0.0557 | 0.1212 | 0.1215 | 19.0 |
| No log | 4.0 | 248 | 2.3919 | 0.1518 | 0.0605 | 0.1256 | 0.126 | 19.0 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_text_summarization_model", "results": []}]} | text2text-generation | farfalla/my_text_summarization_model | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:37:22+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my\_text\_summarization\_model
==============================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3919
* Rouge1: 0.1518
* Rouge2: 0.0605
* Rougel: 0.1256
* Rougelsum: 0.126
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* 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
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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* num\\_epochs: 4\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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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* num\\_epochs: 4\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"
] | [
77,
113,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #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: 2e-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* num\\_epochs: 4\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 | 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": "263.16 +/- 14.24", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | hythyt/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T18:41:01+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 |
# 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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-2.4bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:42:16+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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。"
] | [
63,
35,
13,
298,
234
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 | 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 0.7.2.dev0 | {"library_name": "peft", "base_model": "bigcode/starcoder"} | null | totvs-ai/advpl-only | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-bigcode/starcoder #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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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).
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- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
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[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
# Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser
Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
layer_range: [0, 32]
- sources:
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mattshumer/Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser"
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"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "teknium/OpenHermes-2.5-Mistral-7B"], "base_model": ["cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser", "teknium/OpenHermes-2.5-Mistral-7B"]} | text-generation | mattshumer/Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser",
"teknium/OpenHermes-2.5-Mistral-7B",
"conversational",
"base_model:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:45:27+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #teknium/OpenHermes-2.5-Mistral-7B #conversational #base_model-cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser
Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser is a merge of the following models using LazyMergekit:
* cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
* teknium/OpenHermes-2.5-Mistral-7B
## Configuration
## Usage
| [
"# Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser\n\nCombined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser is a merge of the following models using LazyMergekit:\n* cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n* teknium/OpenHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #teknium/OpenHermes-2.5-Mistral-7B #conversational #base_model-cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser\n\nCombined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser is a merge of the following models using LazyMergekit:\n* cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n* teknium/OpenHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
154,
123,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #teknium/OpenHermes-2.5-Mistral-7B #conversational #base_model-cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Combined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser\n\nCombined-Test-14B-OpenHermes-2.5-Mistral-7B-and-dolphin-2.6-mistral-7b-dpo-laser is a merge of the following models using LazyMergekit:\n* cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser\n* teknium/OpenHermes-2.5-Mistral-7B## Configuration## Usage"
] | [
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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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-3.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:48:42+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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。"
] | [
63,
35,
13,
298,
234
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 | null |
# Lora of u_556/U-556 (Azur Lane)
## What Is This?
This is the LoRA model of waifu u_556/U-556 (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/u_556_azurlane](https://huggingface.co/datasets/CyberHarem/u_556_azurlane), which contains 90 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 920 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `u_556_azurlane`.**
* Pruned core tags for this waifu are `bangs, blue_hair, twintails, red_eyes, blunt_bangs, short_hair, breasts, short_twintails, multicolored_hair, long_hair, 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 575, you need to download [`575/u_556_azurlane.pt`](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/575/u_556_azurlane.pt) as the embedding and [`575/u_556_azurlane.safetensors`](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/575/u_556_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 575.
1560 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_0_2 | 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 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 575 | 26 | **0.884** | **0.977** | 0.845 | **0.799** | [Download](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/575/u_556_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 667 | 30 | 0.875 | 0.963 | 0.839 | 0.784 | [Download](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/667/u_556_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 759 | 34 | 0.877 | 0.951 | 0.834 | 0.772 | [Download](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/759/u_556_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 529 | 24 | 0.824 | 0.954 | 0.838 | 0.750 | [Download](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/529/u_556_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 644 | 29 | 0.709 | 0.963 | **0.847** | 0.696 | [Download](https://huggingface.co/CyberHarem/u_556_azurlane/resolve/main/644/u_556_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 713 to 920](all/0.md)
* [Steps From 483 to 690](all/1.md)
* [Steps From 253 to 460](all/2.md)
* [Steps From 23 to 230](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/u_556_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/u_556_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/u_556_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T18:48:44+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/u_556_azurlane #license-mit #region-us
| Lora of u\_556/U-556 (Azur Lane)
================================
What Is This?
-------------
This is the LoRA model of waifu u\_556/U-556 (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/u\_556\_azurlane, which contains 90 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 920 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'u\_556\_azurlane'.
* Pruned core tags for this waifu are 'bangs, blue\_hair, twintails, red\_eyes, blunt\_bangs, short\_hair, breasts, short\_twintails, multicolored\_hair, long\_hair, 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 575, you need to download '575/u\_556\_azurlane.pt' as the embedding and '575/u\_556\_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 575.
1560 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 713 to 920
* Steps From 483 to 690
* Steps From 253 to 460
* Steps From 23 to 230
| [
"### 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 575, you need to download '575/u\\_556\\_azurlane.pt' as the embedding and '575/u\\_556\\_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 575.\n\n\n1560 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 713 to 920\n* Steps From 483 to 690\n* Steps From 253 to 460\n* Steps From 23 to 230"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/u_556_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 575, you need to download '575/u\\_556\\_azurlane.pt' as the embedding and '575/u\\_556\\_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 575.\n\n\n1560 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 713 to 920\n* Steps From 483 to 690\n* Steps From 253 to 460\n* Steps From 23 to 230"
] | [
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477
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/u_556_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 |
# 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 | ace2105/mistral-coding-instruction | [
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:49:41+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #license-other #endpoints_compatible #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#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
33,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #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 |
<!-- 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. -->
# summarizer-billsum_dataset
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4835
- Rouge1: 0.1837
- Rouge2: 0.0818
- Rougel: 0.1536
- Rougelsum: 0.154
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 25 | 3.4284 | 0.1297 | 0.0383 | 0.109 | 0.1089 | 19.0 |
| No log | 2.0 | 50 | 3.0057 | 0.1222 | 0.0351 | 0.1031 | 0.1029 | 19.0 |
| No log | 3.0 | 75 | 2.8213 | 0.1242 | 0.0376 | 0.1042 | 0.1041 | 19.0 |
| No log | 4.0 | 100 | 2.7231 | 0.1283 | 0.0401 | 0.105 | 0.105 | 19.0 |
| No log | 5.0 | 125 | 2.6706 | 0.1371 | 0.049 | 0.1122 | 0.1122 | 19.0 |
| No log | 6.0 | 150 | 2.6307 | 0.1373 | 0.0473 | 0.1129 | 0.1128 | 19.0 |
| No log | 7.0 | 175 | 2.5988 | 0.1408 | 0.0496 | 0.1149 | 0.1148 | 19.0 |
| No log | 8.0 | 200 | 2.5731 | 0.1471 | 0.0509 | 0.1209 | 0.1212 | 19.0 |
| No log | 9.0 | 225 | 2.5557 | 0.156 | 0.0584 | 0.1293 | 0.1296 | 19.0 |
| No log | 10.0 | 250 | 2.5382 | 0.1642 | 0.0656 | 0.1357 | 0.1356 | 19.0 |
| No log | 11.0 | 275 | 2.5262 | 0.1695 | 0.0716 | 0.1402 | 0.1403 | 19.0 |
| No log | 12.0 | 300 | 2.5173 | 0.1773 | 0.0778 | 0.1475 | 0.1475 | 19.0 |
| No log | 13.0 | 325 | 2.5089 | 0.18 | 0.0801 | 0.1493 | 0.1496 | 19.0 |
| No log | 14.0 | 350 | 2.5013 | 0.1821 | 0.08 | 0.1515 | 0.1516 | 19.0 |
| No log | 15.0 | 375 | 2.4954 | 0.1823 | 0.0801 | 0.1527 | 0.1528 | 19.0 |
| No log | 16.0 | 400 | 2.4910 | 0.1832 | 0.0808 | 0.1532 | 0.1534 | 19.0 |
| No log | 17.0 | 425 | 2.4875 | 0.1842 | 0.082 | 0.154 | 0.1543 | 19.0 |
| No log | 18.0 | 450 | 2.4849 | 0.1841 | 0.0818 | 0.1539 | 0.1541 | 19.0 |
| No log | 19.0 | 475 | 2.4840 | 0.1837 | 0.0818 | 0.1536 | 0.154 | 19.0 |
| 2.7815 | 20.0 | 500 | 2.4835 | 0.1837 | 0.0818 | 0.1536 | 0.154 | 19.0 |
### 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": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "summarizer-billsum_dataset", "results": []}]} | text2text-generation | Surbhit/summarizer-billsum_dataset | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:54:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| summarizer-billsum\_dataset
===========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4835
* Rouge1: 0.1837
* Rouge2: 0.0818
* Rougel: 0.1536
* Rougelsum: 0.154
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### 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: 2e-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* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### 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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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #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: 2e-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* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### 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 |
# 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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-3.5bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
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"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T18:57:04+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 | # pipSQL-1.3b
[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
[colab_notebook](https://colab.research.google.com/drive/1insSxvc3jjAXe0zmdIjmbG3ttb5mpRgQ?usp=sharing)
## What have we built?
A 1.3 bn SQL model that outperforms most SQL expert models and chatgpt on popular benchmarks.
This is a distilled model built on the deepseek base model.
## How we built it?
We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
Loss behaviour in the set up mentioned above -
## Benchmarking :
For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with
Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley.
The benchmark contains 2200 test data points
Here is the link to run the evaluation:
[Test Suite SQL Eval](https://github.com/taoyds/test-suite-sql-eval)
|model|easy|medium|hard|extra|
|-----|----|------|----|-----|
|sqlcoder-7b-2|72.0|58.0|40.6|37.3|
|pipSQL-1.3b|71.1|49.9|31.5|24.1|
|pipSQL-7b|63.0|40.0|30.2|25.0|
|sqlcoder-7b|60.6|48.2|28.3|20.4|
|gpt-3.5|58.8|44.7|31.0|28.4|
We have also benchmarked it on defog eval.
It contains 200 test data points handpicked by defog team.
Here is the link to it:
[Defog SQL-Eval](https://github.com/defog-ai/sql-eval)
These are the results -
## License
The model is open source under apache 2.0. License
## Usage
### Installation
```bash
pip install transformers
```
### Prompt
```python
prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""
```
### PyTorch
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
```
### Flax
```python
from transfomers import FlaxAutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
```
### TensorFlow
```python
from transfomers import TFAutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = TFAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
```
## Examples
### Schema
```sql
CREATE TABLE Products (
product_id number,
parent_product_id number,
product_name text,
product_price number,
product_color text,
product_size text,
product_description text);
CREATE TABLE Customers (
customer_id number,
gender_code text,
customer_first_name text,
customer_middle_initial text,
customer_last_name text,
email_address text,
login_name text,
login_password text,
phone_number text,
address_line_1 text,
town_city text,
county text,
country text);
CREATE TABLE Customer_Payment_Methods (
customer_id number,
payment_method_code text);
CREATE TABLE Invoices (
invoice_number number,
invoice_status_code text,
invoice_date time);
CREATE TABLE Orders (
order_id number,
customer_id number,
order_status_code text,
date_order_placed time);
CREATE TABLE Order_Items (
order_item_id number,
product_id number,
order_id number,
order_item_status_code text);
CREATE TABLE Shipments (
shipment_id number,
order_id number,
invoice_number number,
shipment_tracking_number text,
shipment_date time);
CREATE TABLE Shipment_Items (
shipment_id number,
order_item_id number);
```
### Questions
What is the most popular payment method?
```sql
SELECT payment_method_code FROM Customer_Payment_Methods GROUP BY payment_method_code ORDER BY count(*) DESC LIMIT 1;
```
What are the product price and the product size of the products whose price is above average?
```sql
SELECT product_price , product_size FROM products WHERE product_price > (SELECT avg(product_price) FROM products)
```
What is the most uncommon order status?
```sql
SELECT order_status_code FROM orders GROUP BY order_status_code ORDER BY count(*) ASC LIMIT 1;
```
### Team
Avi Kothari,Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["sql", "code", "text2sql", "instruction_tuned", "basemodel", "jax", "pytorch", "tensorflow", "text-generation-inference"], "datasets": ["PipableAI/pip-txt-to-sql-spider-bird-dataset"], "metrics": ["accuracy"], "pipeline_tag": "text-generation", "widget": [{"text": "<schema>CREATE TABLE system(JobID: String,GID: String, UID: String, Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW: Time,NCPUS: Number,NNodes: Number, NodeList: List, State:String, Timelimit: Time);</schema><question>Get UID and job id for Jobs that started on Jan 20 , 2023 ended on feb 14 2023 and has job id 20</question><sql>", "example_title": "example"}]} | text-generation | PipableAI/pip-sql-1.3b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sql",
"code",
"text2sql",
"instruction_tuned",
"basemodel",
"jax",
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"autotrain_compatible",
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"region:us"
] | 2024-02-14T18:58:05+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #sql #code #text2sql #instruction_tuned #basemodel #jax #pytorch #tensorflow #text-generation-inference #conversational #en #dataset-PipableAI/pip-txt-to-sql-spider-bird-dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pipSQL-1.3b
===========
pipableAi
colab\_notebook
What have we built?
-------------------
A 1.3 bn SQL model that outperforms most SQL expert models and chatgpt on popular benchmarks.
This is a distilled model built on the deepseek base model.
How we built it?
----------------
We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
Loss behaviour in the set up mentioned above -
!image/png
Benchmarking :
--------------
For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with
Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley.
The benchmark contains 2200 test data points
Here is the link to run the evaluation:
Test Suite SQL Eval
We have also benchmarked it on defog eval.
It contains 200 test data points handpicked by defog team.
Here is the link to it:
Defog SQL-Eval
These are the results -
!image/png
License
-------
The model is open source under apache 2.0. License
Usage
-----
### Installation
### Prompt
### PyTorch
### Flax
### TensorFlow
Examples
--------
### Schema
### Questions
What is the most popular payment method?
What are the product price and the product size of the products whose price is above average?
What is the most uncommon order status?
### Team
Avi Kothari,Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya
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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. -->
# furina_seed42_eng_esp_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0227
- Spearman Corr: 0.7567
## 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 | 1.61 | 200 | 0.0390 | 0.5403 |
| 0.0806 | 3.23 | 400 | 0.0258 | 0.7313 |
| 0.0295 | 4.84 | 600 | 0.0231 | 0.7463 |
| 0.022 | 6.45 | 800 | 0.0216 | 0.7582 |
| 0.017 | 8.06 | 1000 | 0.0241 | 0.7626 |
| 0.017 | 9.68 | 1200 | 0.0214 | 0.7723 |
| 0.0142 | 11.29 | 1400 | 0.0212 | 0.7660 |
| 0.0113 | 12.9 | 1600 | 0.0221 | 0.7655 |
| 0.0096 | 14.52 | 1800 | 0.0214 | 0.7690 |
| 0.0083 | 16.13 | 2000 | 0.0222 | 0.7595 |
| 0.0083 | 17.74 | 2200 | 0.0218 | 0.7649 |
| 0.0073 | 19.35 | 2400 | 0.0221 | 0.7600 |
| 0.0065 | 20.97 | 2600 | 0.0225 | 0.7606 |
| 0.0059 | 22.58 | 2800 | 0.0222 | 0.7574 |
| 0.0055 | 24.19 | 3000 | 0.0227 | 0.7567 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_esp_hau_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_esp_hau_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:59:24+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_esp\_hau\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0227
* Spearman Corr: 0.7567
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
| [
"### 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"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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"
] | [
60,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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 | 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": "269.48 +/- 25.31", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | akhilshekkari/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T19:01:01+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 |
<!-- 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. -->
# Dagobert42/distilbert-base-uncased-biored-augmented
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5141
- Accuracy: 0.8189
- Precision: 0.6146
- Recall: 0.5864
- F1: 0.5983
- Weighted F1: 0.8169
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.5409 | 0.807 | 0.6881 | 0.5326 | 0.5615 | 0.7971 |
| No log | 2.0 | 50 | 0.5368 | 0.8108 | 0.7021 | 0.5447 | 0.5781 | 0.8012 |
| No log | 3.0 | 75 | 0.5383 | 0.8161 | 0.6921 | 0.5484 | 0.5835 | 0.8057 |
| No log | 4.0 | 100 | 0.5349 | 0.8131 | 0.6408 | 0.5885 | 0.6008 | 0.8103 |
| No log | 5.0 | 125 | 0.5436 | 0.8157 | 0.6275 | 0.606 | 0.6097 | 0.8136 |
| No log | 6.0 | 150 | 0.5488 | 0.8201 | 0.6805 | 0.5826 | 0.6043 | 0.8146 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
| {"language": ["en"], "license": "mit", "tags": ["low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer"], "datasets": ["medicine"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Dagobert42/distilbert-base-uncased-biored-augmented", "results": []}]} | token-classification | Dagobert42/distilbert-base-uncased-biored-augmented | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"low-resource NER",
"token_classification",
"biomedicine",
"medical NER",
"generated_from_trainer",
"en",
"dataset:medicine",
"base_model:distilbert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:01:08+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #distilbert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-distilbert-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Dagobert42/distilbert-base-uncased-biored-augmented
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5141
* Accuracy: 0.8189
* Precision: 0.6146
* Recall: 0.5864
* F1: 0.5983
* Weighted F1: 0.8169
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: 50
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #distilbert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-distilbert-base-uncased #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: 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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
96,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-distilbert-base-uncased #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: 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: 50### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
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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-tweet_eval-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Accuracy: 0.6529
- Precision: 0.8110
- Recall: 0.6529
- F1: 0.6507
- Auroc: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auroc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.7228 | 0.55 | 500 | 0.7232 | 0.6030 | 0.5625 | 0.6030 | 0.5760 | 0.8937 |
| 0.64 | 1.1 | 1000 | 0.6404 | 0.6529 | 0.8110 | 0.6529 | 0.6507 | 0.9184 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-tweet_eval-emotion", "results": []}]} | text-classification | max-gartz/distilbert-tweet_eval-emotion | [
"transformers",
"onnx",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:01:16+00:00 | [] | [] | TAGS
#transformers #onnx #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-tweet\_eval-emotion
==============================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6404
* Accuracy: 0.6529
* Precision: 0.8110
* Recall: 0.6529
* F1: 0.6507
* Auroc: 0.9184
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.2
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\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: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
72,
97,
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"passage: TAGS\n#transformers #onnx #safetensors #distilbert #text-classification #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: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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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).
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-large"} | null | HeydarS/flan-t5-large_peft_v26 | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-large #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Language(s) (NLP):
- License:
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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]
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APA:
## Glossary [optional]
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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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-3.75bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:06:06+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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。"
] | [
63,
35,
13,
298,
234
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 |
<!-- 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. -->
# Dagobert42/xlnet-base-cased-biored-finetuned
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7341
- Accuracy: 0.7714
- Precision: 0.5341
- Recall: 0.4169
- F1: 0.4594
- Weighted F1: 0.7495
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.8831 | 0.7377 | 0.4536 | 0.2481 | 0.2822 | 0.6719 |
| No log | 2.0 | 50 | 0.8309 | 0.7542 | 0.6035 | 0.3177 | 0.3598 | 0.6933 |
| No log | 3.0 | 75 | 0.7695 | 0.7624 | 0.568 | 0.3566 | 0.409 | 0.7189 |
| No log | 4.0 | 100 | 0.7562 | 0.7676 | 0.5536 | 0.3886 | 0.4398 | 0.7343 |
| No log | 5.0 | 125 | 0.7540 | 0.7673 | 0.5711 | 0.4013 | 0.4474 | 0.7368 |
| No log | 6.0 | 150 | 0.7425 | 0.7754 | 0.5867 | 0.4398 | 0.4873 | 0.7514 |
| No log | 7.0 | 175 | 0.7806 | 0.7788 | 0.606 | 0.4235 | 0.473 | 0.7475 |
| No log | 8.0 | 200 | 0.7638 | 0.7785 | 0.558 | 0.4547 | 0.4871 | 0.7549 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
| {"language": ["en"], "license": "mit", "tags": ["low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer"], "datasets": ["medicine"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "xlnet-base-cased", "model-index": [{"name": "Dagobert42/xlnet-base-cased-biored-finetuned", "results": []}]} | token-classification | Dagobert42/xlnet-base-cased-biored-finetuned | [
"transformers",
"safetensors",
"xlnet",
"token-classification",
"low-resource NER",
"token_classification",
"biomedicine",
"medical NER",
"generated_from_trainer",
"en",
"dataset:medicine",
"base_model:xlnet-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:09:45+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Dagobert42/xlnet-base-cased-biored-finetuned
============================================
This model is a fine-tuned version of xlnet-base-cased on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7341
* Accuracy: 0.7714
* Precision: 0.5341
* Recall: 0.4169
* F1: 0.4594
* Weighted F1: 0.7495
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: 50
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #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: 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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
93,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #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: 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: 50### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
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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-7B-v0.1-compliance-copilot-identity-moonlit-balloon-21
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- 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: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8616 | 0.4 | 500 | 1.3811 |
| 1.0483 | 0.8 | 1000 | 1.4380 |
| 1.0634 | 1.21 | 1500 | 0.8566 |
| 0.8777 | 1.61 | 2000 | 1.3069 |
| 0.8669 | 2.01 | 2500 | 0.9360 |
| 0.9865 | 2.41 | 3000 | 0.8838 |
| 1.0644 | 2.82 | 3500 | 0.8618 |
| 0.7973 | 3.22 | 4000 | 0.8659 |
| 0.6653 | 3.62 | 4500 | 1.3922 |
| 0.9981 | 4.02 | 5000 | 1.0783 |
| 0.9662 | 4.43 | 5500 | 0.9293 |
| 0.8306 | 4.83 | 6000 | 1.4177 |
| 0.6122 | 5.23 | 6500 | 0.9555 |
### Framework versions
- PEFT 0.8.1
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "Mistral-7B-v0.1-compliance-copilot-identity-moonlit-balloon-21", "results": []}]} | null | ripjar/Mistral-7B-v0.1-compliance-copilot-identity-moonlit-balloon-21 | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-14T19:15:30+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
| Mistral-7B-v0.1-compliance-copilot-identity-moonlit-balloon-21
==============================================================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9555
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* 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: constant
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.8.1
* Transformers 4.37.2
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
45,
158,
4,
39
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:15:51+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | travelgate/room-category-es-classifier | [
"transformers",
"safetensors",
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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
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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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 | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | null | ramirces/blindnessdataset | [
"fastai",
"region:us"
] | 2024-02-14T19:18:05+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the Fastai Discord!
Greetings fellow fastlearner ! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
9,
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"passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
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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. -->
# deed-summarization_version_5
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2110
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 72.0323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.5903 | 1.0 | 232 | 3.5371 | 0.0 | 0.0 | 0.0 | 0.0 | 35.1659 |
| 2.577 | 2.0 | 464 | 2.2487 | 0.0 | 0.0 | 0.0 | 0.0 | 49.875 |
| 1.6168 | 3.0 | 696 | 1.8681 | 0.0 | 0.0 | 0.0 | 0.0 | 58.2522 |
| 1.7148 | 4.0 | 928 | 1.6086 | 0.0 | 0.0 | 0.0 | 0.0 | 56.8362 |
| 1.9433 | 5.0 | 1160 | 1.3949 | 0.0 | 0.0 | 0.0 | 0.0 | 62.3427 |
| 1.6744 | 6.0 | 1392 | 1.2110 | 0.0 | 0.0 | 0.0 | 0.0 | 72.0323 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "csebuetnlp/mT5_multilingual_XLSum", "model-index": [{"name": "deed-summarization_version_5", "results": []}]} | text2text-generation | Hasanur525/deed-summarization_version_5 | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/mT5_multilingual_XLSum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:18:22+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-csebuetnlp/mT5_multilingual_XLSum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| deed-summarization\_version\_5
==============================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2110
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
* Gen Len: 72.0323
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5000
* num\_epochs: 6
### 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: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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: 5000\n* num\\_epochs: 6",
"### 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: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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: 5000\n* num\\_epochs: 6",
"### 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"
] | [
83,
116,
4,
33
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"passage: TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-csebuetnlp/mT5_multilingual_XLSum #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: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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: 5000\n* num\\_epochs: 6### 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 |
<!-- 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. -->
# wav2vec2-300m-england-0214-parallel-dim64-avatar
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2605
- Wer: 0.1585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1227
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5257 | 1.0 | 1227 | 0.2176 | 0.2211 |
| 0.2133 | 2.0 | 2454 | 0.1903 | 0.2003 |
| 0.1731 | 3.0 | 3681 | 0.1809 | 0.1776 |
| 0.1435 | 4.0 | 4908 | 0.1764 | 0.1738 |
| 0.1222 | 5.0 | 6135 | 0.1761 | 0.1662 |
| 0.1043 | 6.0 | 7362 | 0.1774 | 0.1638 |
| 0.0893 | 7.0 | 8589 | 0.1848 | 0.1644 |
| 0.0776 | 8.0 | 9816 | 0.1842 | 0.1617 |
| 0.0666 | 9.0 | 11043 | 0.1945 | 0.1614 |
| 0.0563 | 10.0 | 12270 | 0.2044 | 0.1589 |
| 0.0477 | 11.0 | 13497 | 0.2125 | 0.1585 |
| 0.0412 | 12.0 | 14724 | 0.2251 | 0.1586 |
| 0.0346 | 13.0 | 15951 | 0.2397 | 0.1597 |
| 0.0294 | 14.0 | 17178 | 0.2498 | 0.1597 |
| 0.0258 | 15.0 | 18405 | 0.2605 | 0.1585 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.14.7
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0214-parallel-dim64-avatar", "results": []}]} | automatic-speech-recognition | Lin25/wav2vec2-300m-england-0214-parallel-dim64-avatar | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:21:11+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-england-0214-parallel-dim64-avatar
================================================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2605
* Wer: 0.1585
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1227
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.14.7
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #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: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
80,
159,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #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: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
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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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:26:06+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 | null |
# Lora of gascogne/ガスコーニュ（μ兵装）/加斯科涅(μ兵装) (Azur Lane)
## What Is This?
This is the LoRA model of waifu gascogne/ガスコーニュ（μ兵装）/加斯科涅(μ兵装) (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/gascogne_azurlane](https://huggingface.co/datasets/CyberHarem/gascogne_azurlane), which contains 532 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 5, resolution is 720x720, clustering into 20 buckets.
* Trained for 5320 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `gascogne_azurlane`.**
* Pruned core tags for this waifu are `blue_hair, short_hair, yellow_eyes, headgear, breasts, bangs, medium_breasts, multicolored_hair, streaked_hair, mechanical_halo, halo`. 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 1330, you need to download [`1330/gascogne_azurlane.pt`](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/1330/gascogne_azurlane.pt) as the embedding and [`1330/gascogne_azurlane.safetensors`](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/1330/gascogne_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 1330.
1840 images (1.79 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_0 | pattern_1_1 | pattern_1_2 | pattern_2_0 | pattern_2_1 | pattern_2_2 | pattern_3_0 | pattern_3_1 | pattern_3_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 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 1330 | 10 | **0.993** | 0.949 | 0.847 | **0.813** | [Download](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/1330/gascogne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2793 | 21 | 0.991 | 0.954 | **0.848** | 0.805 | [Download](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/2793/gascogne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1463 | 11 | 0.993 | 0.957 | 0.842 | 0.802 | [Download](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/1463/gascogne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2660 | 20 | 0.993 | **0.967** | 0.840 | 0.798 | [Download](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/2660/gascogne_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 3458 | 26 | 0.992 | 0.942 | 0.840 | 0.796 | [Download](https://huggingface.co/CyberHarem/gascogne_azurlane/resolve/main/3458/gascogne_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 4123 to 5320](all/0.md)
* [Steps From 2793 to 3990](all/1.md)
* [Steps From 1463 to 2660](all/2.md)
* [Steps From 133 to 1330](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/gascogne_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/gascogne_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/gascogne_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T19:30:58+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gascogne_azurlane #license-mit #region-us
| Lora of gascogne/ガスコーニュ（μ兵装）/加斯科涅(μ兵装) (Azur Lane)
==================================================
What Is This?
-------------
This is the LoRA model of waifu gascogne/ガスコーニュ（μ兵装）/加斯科涅(μ兵装) (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/gascogne\_azurlane, which contains 532 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 5, resolution is 720x720, clustering into 20 buckets.
* Trained for 5320 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'gascogne\_azurlane'.
* Pruned core tags for this waifu are 'blue\_hair, short\_hair, yellow\_eyes, headgear, breasts, bangs, medium\_breasts, multicolored\_hair, streaked\_hair, mechanical\_halo, halo'. 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 1330, you need to download '1330/gascogne\_azurlane.pt' as the embedding and '1330/gascogne\_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 1330.
1840 images (1.79 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 4123 to 5320
* Steps From 2793 to 3990
* Steps From 1463 to 2660
* Steps From 133 to 1330
| [
"### 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 1330, you need to download '1330/gascogne\\_azurlane.pt' as the embedding and '1330/gascogne\\_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 1330.\n\n\n1840 images (1.79 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 4123 to 5320\n* Steps From 2793 to 3990\n* Steps From 1463 to 2660\n* Steps From 133 to 1330"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gascogne_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 1330, you need to download '1330/gascogne\\_azurlane.pt' as the embedding and '1330/gascogne\\_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 1330.\n\n\n1840 images (1.79 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 4123 to 5320\n* Steps From 2793 to 3990\n* Steps From 1463 to 2660\n* Steps From 133 to 1330"
] | [
45,
38,
476
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gascogne_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. -->
# 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: 3
- 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 | alexyuanj/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"
] | 2024-02-14T19:31:27+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: 3
- 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: 3\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: 3\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,
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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: 3\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 |
# Model Card for Model ID
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# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
### Downstream Use [optional]
### 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.
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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]
#### Training Hyperparameters
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## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text2text-generation | OmarHaroon01/flan_t5_imdb_accelerator | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:36:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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| [
"# 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]:",
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"## Uses",
"### Direct Use",
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"### 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"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #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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"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #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. -->
# furina_seed42_eng_kin_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0273
- Spearman Corr: 0.8040
## 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 | 1.77 | 200 | 0.0339 | 0.7391 |
| 0.0845 | 3.54 | 400 | 0.0337 | 0.7909 |
| 0.0273 | 5.31 | 600 | 0.0228 | 0.8081 |
| 0.0199 | 7.08 | 800 | 0.0229 | 0.8163 |
| 0.0152 | 8.85 | 1000 | 0.0241 | 0.8102 |
| 0.0123 | 10.62 | 1200 | 0.0278 | 0.8168 |
| 0.0102 | 12.39 | 1400 | 0.0282 | 0.8072 |
| 0.0087 | 14.16 | 1600 | 0.0210 | 0.8108 |
| 0.008 | 15.93 | 1800 | 0.0234 | 0.8106 |
| 0.008 | 17.7 | 2000 | 0.0278 | 0.8059 |
| 0.0069 | 19.47 | 2200 | 0.0308 | 0.8107 |
| 0.0063 | 21.24 | 2400 | 0.0257 | 0.8127 |
| 0.0057 | 23.01 | 2600 | 0.0257 | 0.8095 |
| 0.0055 | 24.78 | 2800 | 0.0250 | 0.8077 |
| 0.0051 | 26.55 | 3000 | 0.0252 | 0.8055 |
| 0.005 | 28.32 | 3200 | 0.0273 | 0.8040 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_kin_hau_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_kin_hau_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:39:32+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_kin\_hau\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0273
* Spearman Corr: 0.8040
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
| [
"### 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"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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"
] | [
60,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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 | 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 | LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T19:40:00+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 #safetensors #mixtral #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #autotrain_compatible #text-generation-inference #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 | null |
# Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.
# NeuralTrix-bf16
NeuralTrix-bf16 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [bardsai/jaskier-7b-dpo-v3.3](https://huggingface.co/bardsai/jaskier-7b-dpo-v3.3)
* [CultriX/NeuralTrix-v4-bf16](https://huggingface.co/CultriX/NeuralTrix-v4-bf16)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
## 🧩 Configuration
```yaml
models:
- model: eren23/dpo-binarized-NeuralTrix-7B
# no parameters necessary for base model
- model: bardsai/jaskier-7b-dpo-v3.3
parameters:
density: 0.65
weight: 0.4
- model: CultriX/NeuralTrix-v4-bf16
parameters:
density: 0.6
weight: 0.35
- model: CultriX/NeuralTrix-7B-dpo
parameters:
density: 0.6
weight: 0.35
merge_method: dare_ties
base_model: eren23/dpo-binarized-NeuralTrix-7B
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/"
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", "bardsai/jaskier-7b-dpo-v3.3", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-7B-dpo"], "base_model": ["bardsai/jaskier-7b-dpo-v3.3", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-7B-dpo"]} | null | CultriX/NeuralTrix-bf16-GGUF | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"bardsai/jaskier-7b-dpo-v3.3",
"CultriX/NeuralTrix-v4-bf16",
"CultriX/NeuralTrix-7B-dpo",
"base_model:bardsai/jaskier-7b-dpo-v3.3",
"base_model:CultriX/NeuralTrix-v4-bf16",
"base_model:CultriX/NeuralTrix-7B-dpo",
"region:us"
] | 2024-02-14T19:42:43+00:00 | [] | [] | TAGS
#gguf #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #region-us
|
# Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.
# NeuralTrix-bf16
NeuralTrix-bf16 is a merge of the following models using LazyMergekit:
* bardsai/jaskier-7b-dpo-v3.3
* CultriX/NeuralTrix-v4-bf16
* CultriX/NeuralTrix-7B-dpo
## Configuration
## Usage
| [
"# Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.",
"# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo",
"## Configuration",
"## Usage"
] | [
"TAGS\n#gguf #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #region-us \n",
"# Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.",
"# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo",
"## Configuration",
"## Usage"
] | [
126,
37,
73,
4,
3
] | [
"passage: TAGS\n#gguf #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #region-us \n# Note: This is a test to check if it fixed the INSTINSTINST error in the output! Please let me know if you still get errors using this model.# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo## Configuration## Usage"
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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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5863
- Accuracy: 0.7966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 204 | 0.6400 | 0.7804 |
| No log | 2.0 | 408 | 0.5863 | 0.7966 |
### Framework versions
- Transformers 4.37.2
- 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": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | text-classification | gracegsy/my_awesome_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:46:08+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_awesome\_model
==================
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: 0.5863
* Accuracy: 0.7966
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### 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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"### 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"
] | [
72,
98,
4,
33
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #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: 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* num\\_epochs: 2### 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 | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | braunagn/test | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# 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.
## 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
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#### Factors
#### 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. -->
# distilBert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Precision: 0.5530
- Recall: 0.3818
- F1: 0.4518
- Accuracy: 0.9462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 107 | 0.2886 | 0.5599 | 0.3077 | 0.3971 | 0.9439 |
| No log | 2.0 | 214 | 0.2800 | 0.5530 | 0.3818 | 0.4518 | 0.9462 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilBert", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "config": "wnut_17", "split": "test", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.553020134228188, "name": "Precision"}, {"type": "recall", "value": 0.3818350324374421, "name": "Recall"}, {"type": "f1", "value": 0.4517543859649123, "name": "F1"}, {"type": "accuracy", "value": 0.9461758796118165, "name": "Accuracy"}]}]}]} | token-classification | codeSlang/distilBert | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:54:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilBert
==========
This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2800
* Precision: 0.5530
* Recall: 0.3818
* F1: 0.4518
* Accuracy: 0.9462
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\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: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
85,
98,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
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null | null | null |
# Stable Cascade - one ZIP-file | {"license": "mit", "tags": ["safetensors", "stable-cascade", "by-StabilityAI", "zip"], "pipeline_tag": "text-to-image", "inference": false} | text-to-image | ehristoforu/stable-cascade-zip | [
"safetensors",
"stable-cascade",
"by-StabilityAI",
"zip",
"text-to-image",
"license:mit",
"region:us"
] | 2024-02-14T19:54:36+00:00 | [] | [] | TAGS
#safetensors #stable-cascade #by-StabilityAI #zip #text-to-image #license-mit #region-us
|
# Stable Cascade - one ZIP-file | [
"# Stable Cascade - one ZIP-file"
] | [
"TAGS\n#safetensors #stable-cascade #by-StabilityAI #zip #text-to-image #license-mit #region-us \n",
"# Stable Cascade - one ZIP-file"
] | [
36,
11
] | [
"passage: TAGS\n#safetensors #stable-cascade #by-StabilityAI #zip #text-to-image #license-mit #region-us \n# Stable Cascade - one ZIP-file"
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | null | cnrcastroli/drpairForm2Checkboxes10kList | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T19:55:20+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us
|
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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]
## 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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"# Model Card for Model ID",
"## Model Details",
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"## Training Details",
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"### Training Procedure",
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"### Compute Infrastructure",
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"#### 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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text2text-generation | OmarHaroon01/flan_t5_imdb_accelerator_1 | [
"transformers",
"safetensors",
"t5",
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"arxiv:1910.09700",
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#transformers #safetensors #t5 #text2text-generation #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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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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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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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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### Compute Infrastructure
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## Glossary [optional]
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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. -->
# he
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3168
- Precision: 0.1010
- Recall: 0.1098
- F1: 0.1051
- Precision Median: 0.0
- Recall Median: 0.0
- F1 Median: 0.0
- Precision Max: 0.5
- Recall Max: 0.5333
- F1 Max: 0.5161
- Precision Min: 0.0
- Recall Min: 0.0
- F1 Min: 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-06
- train_batch_size: 32
- 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: 200
- training_steps: 800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Precision Median | Recall Median | F1 Median | Precision Max | Recall Max | F1 Max | Precision Min | Recall Min | F1 Min |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|
| 3.5705 | 0.32 | 200 | 2.7306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2.0183 | 0.63 | 400 | 1.6209 | 0.0341 | 0.0373 | 0.0356 | 0.0 | 0.0 | 0.0 | 0.4375 | 0.4667 | 0.4516 | 0.0 | 0.0 | 0.0 |
| 1.6646 | 0.95 | 600 | 1.3710 | 0.1062 | 0.1151 | 0.1103 | 0.0 | 0.0 | 0.0 | 0.5 | 0.5333 | 0.5161 | 0.0 | 0.0 | 0.0 |
| 1.6004 | 1.27 | 800 | 1.3168 | 0.1010 | 0.1098 | 0.1051 | 0.0 | 0.0 | 0.0 | 0.5 | 0.5333 | 0.5161 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"language": ["he"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "base_model": "openai/whisper-medium", "model-index": [{"name": "he", "results": []}]} | automatic-speech-recognition | cantillation/whisper-medium-he-teamim-aviv-bavly-without-nikud-test | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"he",
"base_model:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:04:21+00:00 | [] | [
"he"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #he #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #region-us
| he
==
This model is a fine-tuned version of openai/whisper-medium on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3168
* Precision: 0.1010
* Recall: 0.1098
* F1: 0.1051
* Precision Median: 0.0
* Recall Median: 0.0
* F1 Median: 0.0
* Precision Max: 0.5
* Recall Max: 0.5333
* F1 Max: 0.5161
* Precision Min: 0.0
* Recall Min: 0.0
* F1 Min: 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-06
* train\_batch\_size: 32
* 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: 200
* training\_steps: 800
* 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
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\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: 200\n* training\\_steps: 800\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\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 #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #he #base_model-openai/whisper-medium #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-06\n* train\\_batch\\_size: 32\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: 200\n* training\\_steps: 800\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
81,
130,
4,
33
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #he #base_model-openai/whisper-medium #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-06\n* train\\_batch\\_size: 32\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: 200\n* training\\_steps: 800\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\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 | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hythyt/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | reinforcement-learning | hythyt/ppo-Huggy | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2024-02-14T20:05:55+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
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: hythyt/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\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: hythyt/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\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: hythyt/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
44,
199
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\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: hythyt/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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] |
null | null | diffusers | # Sadayo Kawakami (Persona5)
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Arczisan/skp5-guy-v2/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "masterpiece, best quality, outdoors, light particles, lens flare, depth of field, 1girl, solo, looking at viewer, large breasts, mature female, collarbone, <lora:skp5-guy-v2:1>, skp5, bangs, striped shirt, long sleeves, skirt, denim skirt, hands on hips,", "parameters": {"negative_prompt": "(worst quality, low quality:1.4), fcNeg-neg, text, watermark, artist name, signature"}, "output": {"url": "images/00006-1768999734.jpeg"}}], "base_model": "stabilityai/stable-cascade"} | text-to-image | Arczisan/skp5-guy-v2 | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-cascade",
"region:us"
] | 2024-02-14T20:07:44+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-cascade #region-us
| # Sadayo Kawakami (Persona5)
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Sadayo Kawakami (Persona5)\n\n<Gallery />",
"## 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-stabilityai/stable-cascade #region-us \n",
"# Sadayo Kawakami (Persona5)\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
49,
14,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-cascade #region-us \n# Sadayo Kawakami (Persona5)\n\n<Gallery />## 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 | # gpt2_ties_merge_ab_with_classic_e2_d5
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [rirv938/gpt2_sequence_classification_base](https://huggingface.co/rirv938/gpt2_sequence_classification_base) as a base.
### Models Merged
The following models were included in the merge:
* [ChaiML/reward_models_100_170000000_cp_332032](https://huggingface.co/ChaiML/reward_models_100_170000000_cp_332032)
* [rirv938/reward_gpt2_preference_24m_e2](https://huggingface.co/rirv938/reward_gpt2_preference_24m_e2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: rirv938/gpt2_sequence_classification_base
# no parameters necessary for base model
- model: ChaiML/reward_models_100_170000000_cp_332032
parameters:
density: 0.5
weight: 0.5
- model: rirv938/reward_gpt2_preference_24m_e2
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: rirv938/gpt2_sequence_classification_base
parameters:
normalize: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ChaiML/reward_models_100_170000000_cp_332032", "rirv938/gpt2_sequence_classification_base", "rirv938/reward_gpt2_preference_24m_e2"]} | text-classification | rirv938/gpt2_ties_merge_preference_plus_classic_e2_density_5 | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:ChaiML/reward_models_100_170000000_cp_332032",
"base_model:rirv938/gpt2_sequence_classification_base",
"base_model:rirv938/reward_gpt2_preference_24m_e2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T20:09:23+00:00 | [
"2306.01708"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_ties_merge_ab_with_classic_e2_d5
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.
### Models Merged
The following models were included in the merge:
* ChaiML/reward_models_100_170000000_cp_332032
* rirv938/reward_gpt2_preference_24m_e2
### Configuration
The following YAML configuration was used to produce this model:
| [
"# gpt2_ties_merge_ab_with_classic_e2_d5\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_ties_merge_ab_with_classic_e2_d5\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
133,
38,
4,
37,
56,
17
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2_ties_merge_ab_with_classic_e2_d5\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | transformers | # gpt2_ties_merge_ab_with_classic_e2_d9
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [rirv938/gpt2_sequence_classification_base](https://huggingface.co/rirv938/gpt2_sequence_classification_base) as a base.
### Models Merged
The following models were included in the merge:
* [ChaiML/reward_models_100_170000000_cp_332032](https://huggingface.co/ChaiML/reward_models_100_170000000_cp_332032)
* [rirv938/reward_gpt2_preference_24m_e2](https://huggingface.co/rirv938/reward_gpt2_preference_24m_e2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: rirv938/gpt2_sequence_classification_base
# no parameters necessary for base model
- model: ChaiML/reward_models_100_170000000_cp_332032
parameters:
density: 0.9
weight: 0.5
- model: rirv938/reward_gpt2_preference_24m_e2
parameters:
density: 0.9
weight: 0.5
merge_method: ties
base_model: rirv938/gpt2_sequence_classification_base
parameters:
normalize: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ChaiML/reward_models_100_170000000_cp_332032", "rirv938/reward_gpt2_preference_24m_e2", "rirv938/gpt2_sequence_classification_base"]} | text-classification | rirv938/gpt2_ties_merge_preference_plus_classic_e2_density_9 | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:ChaiML/reward_models_100_170000000_cp_332032",
"base_model:rirv938/reward_gpt2_preference_24m_e2",
"base_model:rirv938/gpt2_sequence_classification_base",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T20:09:31+00:00 | [
"2306.01708"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-rirv938/gpt2_sequence_classification_base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_ties_merge_ab_with_classic_e2_d9
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.
### Models Merged
The following models were included in the merge:
* ChaiML/reward_models_100_170000000_cp_332032
* rirv938/reward_gpt2_preference_24m_e2
### Configuration
The following YAML configuration was used to produce this model:
| [
"# gpt2_ties_merge_ab_with_classic_e2_d9\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-rirv938/gpt2_sequence_classification_base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_ties_merge_ab_with_classic_e2_d9\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
133,
38,
4,
37,
56,
17
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-rirv938/gpt2_sequence_classification_base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2_ties_merge_ab_with_classic_e2_d9\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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null | null | transformers | # gpt2_ties_merge_ab_with_classic_e2_d99
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [rirv938/gpt2_sequence_classification_base](https://huggingface.co/rirv938/gpt2_sequence_classification_base) as a base.
### Models Merged
The following models were included in the merge:
* [rirv938/reward_gpt2_preference_24m_e2](https://huggingface.co/rirv938/reward_gpt2_preference_24m_e2)
* [ChaiML/reward_models_100_170000000_cp_332032](https://huggingface.co/ChaiML/reward_models_100_170000000_cp_332032)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: rirv938/gpt2_sequence_classification_base
# no parameters necessary for base model
- model: ChaiML/reward_models_100_170000000_cp_332032
parameters:
density: 0.9
weight: 0.5
- model: rirv938/reward_gpt2_preference_24m_e2
parameters:
density: 0.9
weight: 0.5
merge_method: ties
base_model: rirv938/gpt2_sequence_classification_base
parameters:
normalize: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["rirv938/gpt2_sequence_classification_base", "rirv938/reward_gpt2_preference_24m_e2", "ChaiML/reward_models_100_170000000_cp_332032"]} | text-classification | rirv938/gpt2_ties_merge_preference_plus_classic_e2_density_99 | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:rirv938/gpt2_sequence_classification_base",
"base_model:rirv938/reward_gpt2_preference_24m_e2",
"base_model:ChaiML/reward_models_100_170000000_cp_332032",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T20:09:40+00:00 | [
"2306.01708"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_ties_merge_ab_with_classic_e2_d99
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.
### Models Merged
The following models were included in the merge:
* rirv938/reward_gpt2_preference_24m_e2
* ChaiML/reward_models_100_170000000_cp_332032
### Configuration
The following YAML configuration was used to produce this model:
| [
"# gpt2_ties_merge_ab_with_classic_e2_d99\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* rirv938/reward_gpt2_preference_24m_e2\n* ChaiML/reward_models_100_170000000_cp_332032",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_ties_merge_ab_with_classic_e2_d99\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* rirv938/reward_gpt2_preference_24m_e2\n* ChaiML/reward_models_100_170000000_cp_332032",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
133,
38,
4,
37,
56,
17
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #arxiv-2306.01708 #base_model-rirv938/gpt2_sequence_classification_base #base_model-rirv938/reward_gpt2_preference_24m_e2 #base_model-ChaiML/reward_models_100_170000000_cp_332032 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2_ties_merge_ab_with_classic_e2_d99\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the TIES merge method using rirv938/gpt2_sequence_classification_base as a base.### Models Merged\n\nThe following models were included in the merge:\n* rirv938/reward_gpt2_preference_24m_e2\n* ChaiML/reward_models_100_170000000_cp_332032### Configuration\n\nThe following YAML configuration was used to produce this model:"
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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。
***
Quantization of Model [OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]
| {"language": ["zh", "en", "fr", "de", "ja", "ko", "it", "ru"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": false} | text-generation | nold/openbuddy-mixtral-7bx8-v18.1-32k-GGUF | [
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"region:us"
] | 2024-02-14T20:10:36+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #gguf #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #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。
*
Quantization of Model OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]
| [
"# 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。\n\n*\n\nQuantization of Model OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]"
] | [
"TAGS\n#transformers #gguf #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #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。\n\n*\n\nQuantization of Model OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]"
] | [
41,
35,
13,
298,
296
] | [
"passage: TAGS\n#transformers #gguf #text-generation #zh #en #fr #de #ja #ko #it #ru #license-apache-2.0 #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 |
<!-- 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. -->
# Dagobert42/xlnet-base-cased-biored-augmented
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2844
- Accuracy: 0.9054
- Precision: 0.7748
- Recall: 0.7812
- F1: 0.7773
- Weighted F1: 0.9055
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log | 1.0 | 25 | 0.3325 | 0.8915 | 0.8442 | 0.7017 | 0.7598 | 0.8863 |
| No log | 2.0 | 50 | 0.3047 | 0.9035 | 0.8101 | 0.7947 | 0.8003 | 0.9025 |
| No log | 3.0 | 75 | 0.3012 | 0.9067 | 0.806 | 0.8046 | 0.8042 | 0.9062 |
| No log | 4.0 | 100 | 0.3098 | 0.9046 | 0.7849 | 0.8235 | 0.8022 | 0.9052 |
| No log | 5.0 | 125 | 0.3181 | 0.9087 | 0.7964 | 0.8165 | 0.8027 | 0.9083 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0
| {"language": ["en"], "license": "mit", "tags": ["low-resource NER", "token_classification", "biomedicine", "medical NER", "generated_from_trainer"], "datasets": ["medicine"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "xlnet-base-cased", "model-index": [{"name": "Dagobert42/xlnet-base-cased-biored-augmented", "results": []}]} | token-classification | Dagobert42/xlnet-base-cased-biored-augmented | [
"transformers",
"safetensors",
"xlnet",
"token-classification",
"low-resource NER",
"token_classification",
"biomedicine",
"medical NER",
"generated_from_trainer",
"en",
"dataset:medicine",
"base_model:xlnet-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:12:23+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Dagobert42/xlnet-base-cased-biored-augmented
============================================
This model is a fine-tuned version of xlnet-base-cased on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2844
* Accuracy: 0.9054
* Precision: 0.7748
* Recall: 0.7812
* F1: 0.7773
* Weighted F1: 0.9055
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: 50
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #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: 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: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
] | [
93,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #xlnet #token-classification #low-resource NER #token_classification #biomedicine #medical NER #generated_from_trainer #en #dataset-medicine #base_model-xlnet-base-cased #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: 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: 50### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.15.0"
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] |
null | null | transformers |
# Multi-species Foundation Model for Universal RNA and DNA Downstream Tasks
# Notes
We are keep updating the checkpoints, the current checkpoint is trained for 0.85 epoch.
## Training Examples
Refer to GitHub [https://github.com/yangheng95/MP-RNA](https://github.com/yangheng95/MP-RNA)
## Usage
This model is available for replacing genomic foundation models such as CDSBERT, Nucleotide Transformers, DNABERT2, etc.
```
from transformers import AutoModel
model = AutoModel.from_pretrained("yangheng/MPRNA-52M-v1", trust_remote_code=True)
```
## Subtasks
- Secondary structure prediction
- Genome Sequence Classification
- Genome Sequence Regression
- Single Nucleotide Repair
- Genome Masked Language Modeling
- etc.
Part of the codes are adapted from ESM2.
| {"language": ["rna", "dna"], "license": "mit", "tags": ["Genomic-Language-Modeling", "RNA Genomic Foundation Model"]} | fill-mask | yangheng/MPRNA-52M-v1 | [
"transformers",
"safetensors",
"mprna",
"fill-mask",
"Genomic-Language-Modeling",
"RNA Genomic Foundation Model",
"custom_code",
"rna",
"dna",
"license:mit",
"autotrain_compatible",
"region:us"
] | 2024-02-14T20:15:02+00:00 | [] | [
"rna",
"dna"
] | TAGS
#transformers #safetensors #mprna #fill-mask #Genomic-Language-Modeling #RNA Genomic Foundation Model #custom_code #rna #dna #license-mit #autotrain_compatible #region-us
|
# Multi-species Foundation Model for Universal RNA and DNA Downstream Tasks
# Notes
We are keep updating the checkpoints, the current checkpoint is trained for 0.85 epoch.
## Training Examples
Refer to GitHub URL
## Usage
This model is available for replacing genomic foundation models such as CDSBERT, Nucleotide Transformers, DNABERT2, etc.
## Subtasks
- Secondary structure prediction
- Genome Sequence Classification
- Genome Sequence Regression
- Single Nucleotide Repair
- Genome Masked Language Modeling
- etc.
Part of the codes are adapted from ESM2.
| [
"# Multi-species Foundation Model for Universal RNA and DNA Downstream Tasks",
"# Notes\nWe are keep updating the checkpoints, the current checkpoint is trained for 0.85 epoch.",
"## Training Examples\nRefer to GitHub URL",
"## Usage\nThis model is available for replacing genomic foundation models such as CDSBERT, Nucleotide Transformers, DNABERT2, etc.",
"## Subtasks\n- Secondary structure prediction\n- Genome Sequence Classification\n- Genome Sequence Regression\n- Single Nucleotide Repair\n- Genome Masked Language Modeling\n- etc.\n\nPart of the codes are adapted from ESM2."
] | [
"TAGS\n#transformers #safetensors #mprna #fill-mask #Genomic-Language-Modeling #RNA Genomic Foundation Model #custom_code #rna #dna #license-mit #autotrain_compatible #region-us \n",
"# Multi-species Foundation Model for Universal RNA and DNA Downstream Tasks",
"# Notes\nWe are keep updating the checkpoints, the current checkpoint is trained for 0.85 epoch.",
"## Training Examples\nRefer to GitHub URL",
"## Usage\nThis model is available for replacing genomic foundation models such as CDSBERT, Nucleotide Transformers, DNABERT2, etc.",
"## Subtasks\n- Secondary structure prediction\n- Genome Sequence Classification\n- Genome Sequence Regression\n- Single Nucleotide Repair\n- Genome Masked Language Modeling\n- etc.\n\nPart of the codes are adapted from ESM2."
] | [
61,
17,
28,
10,
36,
57
] | [
"passage: TAGS\n#transformers #safetensors #mprna #fill-mask #Genomic-Language-Modeling #RNA Genomic Foundation Model #custom_code #rna #dna #license-mit #autotrain_compatible #region-us \n# Multi-species Foundation Model for Universal RNA and DNA Downstream Tasks# Notes\nWe are keep updating the checkpoints, the current checkpoint is trained for 0.85 epoch.## Training Examples\nRefer to GitHub URL## Usage\nThis model is available for replacing genomic foundation models such as CDSBERT, Nucleotide Transformers, DNABERT2, etc.## Subtasks\n- Secondary structure prediction\n- Genome Sequence Classification\n- Genome Sequence Regression\n- Single Nucleotide Repair\n- Genome Masked Language Modeling\n- etc.\n\nPart of the codes are adapted from ESM2."
] | [
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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": "249.88 +/- 21.35", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AMOUR3099/LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T20:15:30+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 | 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 SyntaxTheRed -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 SyntaxTheRed -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 SyntaxTheRed
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"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": "495.50 +/- 139.35", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | SyntaxTheRed/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T20:19:32+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 | sentence-transformers |
# turkish-tiny-bert-uncased-mean-nli-stsb-tr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was adapted from [ytu-ce-cosmos/turkish-tiny-bert-uncased](https://huggingface.co/ytu-ce-cosmos/turkish-tiny-bert-uncased) and fine-tuned on these datasets:
- [nli_tr](https://huggingface.co/datasets/nli_tr)
- [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Bu örnek bir cümle", "Her cümle dönüştürülür"]
model = SentenceTransformer('atasoglu/turkish-tiny-bert-uncased-mean-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["Bu örnek bir cümle", "Her cümle dönüştürülür"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('atasoglu/turkish-tiny-bert-uncased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('atasoglu/turkish-tiny-bert-uncased-mean-nli-stsb-tr')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below:
```txt
Cosine-Similarity : Pearson: 0.6587 Spearman: 0.6370
Manhattan-Distance: Pearson: 0.6293 Spearman: 0.6151
Euclidean-Distance: Pearson: 0.6335 Spearman: 0.6186
Dot-Product-Similarity: Pearson: 0.5972 Spearman: 0.5756
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 45 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 22,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 45,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 128, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"language": ["tr"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["nli_tr", "emrecan/stsb-mt-turkish"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | atasoglu/turkish-tiny-bert-uncased-mean-nli-stsb-tr | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"tr",
"dataset:nli_tr",
"dataset:emrecan/stsb-mt-turkish",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:22:41+00:00 | [] | [
"tr"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #tr #dataset-nli_tr #dataset-emrecan/stsb-mt-turkish #license-mit #endpoints_compatible #region-us
|
# turkish-tiny-bert-uncased-mean-nli-stsb-tr
This is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was adapted from ytu-ce-cosmos/turkish-tiny-bert-uncased and fine-tuned on these datasets:
- nli_tr
- emrecan/stsb-mt-turkish
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
Achieved results on the STS-b test split are given below:
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 45 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# turkish-tiny-bert-uncased-mean-nli-stsb-tr\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis model was adapted from ytu-ce-cosmos/turkish-tiny-bert-uncased and fine-tuned on these datasets:\n- nli_tr\n- emrecan/stsb-mt-turkish",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\nAchieved results on the STS-b test split are given below:",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 45 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #tr #dataset-nli_tr #dataset-emrecan/stsb-mt-turkish #license-mit #endpoints_compatible #region-us \n",
"# turkish-tiny-bert-uncased-mean-nli-stsb-tr\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis model was adapted from ytu-ce-cosmos/turkish-tiny-bert-uncased and fine-tuned on these datasets:\n- nli_tr\n- emrecan/stsb-mt-turkish",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\nAchieved results on the STS-b test split are given below:",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 45 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
74,
119,
38,
64,
20,
77,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #tr #dataset-nli_tr #dataset-emrecan/stsb-mt-turkish #license-mit #endpoints_compatible #region-us \n# turkish-tiny-bert-uncased-mean-nli-stsb-tr\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis model was adapted from ytu-ce-cosmos/turkish-tiny-bert-uncased and fine-tuned on these datasets:\n- nli_tr\n- emrecan/stsb-mt-turkish## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\nAchieved results on the STS-b test split are given below:## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 45 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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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 tr Beta - tgrhn
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2430
- Wer: 87.8151
## 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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0686 | 3.05 | 500 | 0.2430 | 87.8151 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"language": ["tr"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small tr Beta - tgrhn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "tr", "split": "test", "args": "tr"}, "metrics": [{"type": "wer", "value": 87.8150622532645, "name": "Wer"}]}]}]} | automatic-speech-recognition | tgrhn/whisper-tr-small-cv11 | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"tr",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:22:59+00:00 | [] | [
"tr"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Small tr Beta - tgrhn
=============================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2430
* Wer: 87.8151
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: 64
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* training\_steps: 500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* 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: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 500\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 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #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: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 500\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 2.17.0\n* Tokenizers 0.15.2"
] | [
100,
158,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #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: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 500\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 2.17.0\n* Tokenizers 0.15.2"
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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": "-295.75 +/- 74.93", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Bassilik/ppo-LunarLander-v2-TEST | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T20:28:44+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 |
# Uploaded model
- **Developed by:** arendgb
- **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", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | text-generation | arendgb/restaurant_demo_mistral_ft_16bit | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:32:02+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: arendgb
- 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: arendgb\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 #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: arendgb\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\"/>"
] | [
92,
85
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: arendgb\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 | 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 OsherElhadad -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 OsherElhadad -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 OsherElhadad
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"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": "734.50 +/- 189.74", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | OsherElhadad/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T20:32:16+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"
] | [
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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 | 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": "NousResearch/Llama-2-7b-chat-hf"} | null | Nadeemag/ustaadnow_qa | [
"peft",
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#peft #safetensors #arxiv-1910.09700 #base_model-NousResearch/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- 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
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APA:
## Glossary [optional]
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null | null | peft |
# Model Card for Model ID
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<!-- 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": "microsoft/CodeGPT-small-py"} | null | adalib/sqlmodel-cond-gen-CodeGPT-small-py-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]
### 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 | transformers |
# DistilBERT Cross Segment Document Chunking
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) for classifying if two subsequent sentences are from the same Wikipedia article section. Intended usage is **text segmantation/document chunking**.
It is based on the article *Text Segmentation by Cross Segment Attention by Michal Lukasik, Boris Dadachev, Gonc¸alo Simoes and Kishore Papineni*.
## How to use it
One way to use this model is via the HuggingFace transformers TextClassificationPipeline class.
```python
from transformers import (
AutoModelForSequenceClassification,
DistilBertTokenizer,
TextClassificationPipeline
)
model_name = "BlueOrangeDigital/distilbert-cross-segment-document-chunking"
id2label = {0: "SAME", 1: "DIFFERENT"}
label2id = {"SAME": 0, "DIFFERENT": 1}
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=2,
id2label=id2label,
label2id=label2id
)
pairs = [
"Left context. [SEP] Right context.",
"he also earned five mvp stars with the martian men's tenis team in 2149. [SEP] mart jhones spent the 2166 and 2167 seasons with the all stars intergalactic in the interstelar soccer league ( isl ).",
]
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
pipe(pairs)
[[{'label': 'SAME', 'score': 0.9845659136772156},
{'label': 'DIFFERENT', 'score': 0.015434039756655693}],
[{'label': 'SAME', 'score': 0.44031277298927307},
{'label': 'DIFFERENT', 'score': 0.5596872568130493}]]
```
## Training Data
Sentences pairs from 40,000 (train) + 4,000 (validation) Wikipedia articles.
**Label 1:** Two subsequent sentences that are not from the same article section;
**Label 0:** Every other pair of subsequent sentences.
Label 0 pairs were undersampled, resulting in a total of 408,753 and 45,417 training and validation pairs, respectively.
The input of the model are of the form
```
[CLS] Right context [SEP] Left context [SEP]
```
Given DistilBERT 512 token limit, both right and left context are limited to 255 token length. When exceeding this limit, the sentence was truncated (either the beggining or the end of the sentence, for right and left context, respectively).
## Trainig Procedure
The model was trained for 2 epochs with a learning rate of 1e-5 and cross-entropy loss on a P100 GPU for 8 hours.
## Validation Metrics
| Loss | Accuracy | Recall | Precision | F1 |
|:----:|:----:|:----:|:-----:|:----:|
| 0.398 | 0.815 | 0.815 | 0.817 | 0.815 | | {"language": "en", "license": "apache-2.0", "tags": ["text segmentation", "document chunking"], "datasets": ["wikipedia"], "pipeline_tag": "text-classification", "base_model": "distilbert/distilbert-base-uncased", "widget": [{"text": "Left context. [SEP] Right context."}]} | text-classification | BlueOrangeDigital/distilbert-cross-segment-document-chunking | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"text segmentation",
"document chunking",
"en",
"dataset:wikipedia",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:36:39+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #distilbert #text-classification #text segmentation #document chunking #en #dataset-wikipedia #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT Cross Segment Document Chunking
==========================================
This model is a fine-tuned version of distilbert-base-uncased for classifying if two subsequent sentences are from the same Wikipedia article section. Intended usage is text segmantation/document chunking.
It is based on the article *Text Segmentation by Cross Segment Attention by Michal Lukasik, Boris Dadachev, Gonc¸alo Simoes and Kishore Papineni*.
How to use it
-------------
One way to use this model is via the HuggingFace transformers TextClassificationPipeline class.
Training Data
-------------
Sentences pairs from 40,000 (train) + 4,000 (validation) Wikipedia articles.
Label 1: Two subsequent sentences that are not from the same article section;
Label 0: Every other pair of subsequent sentences.
Label 0 pairs were undersampled, resulting in a total of 408,753 and 45,417 training and validation pairs, respectively.
The input of the model are of the form
Given DistilBERT 512 token limit, both right and left context are limited to 255 token length. When exceeding this limit, the sentence was truncated (either the beggining or the end of the sentence, for right and left context, respectively).
Trainig Procedure
-----------------
The model was trained for 2 epochs with a learning rate of 1e-5 and cross-entropy loss on a P100 GPU for 8 hours.
Validation Metrics
------------------
| [] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #text segmentation #document chunking #en #dataset-wikipedia #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
80
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #text segmentation #document chunking #en #dataset-wikipedia #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #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. -->
# FakeNewsDetection_Cross-Sean
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- F1: 0.9882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0786 | 1.0 | 1100 | 0.0654 | 0.9845 |
| 0.0386 | 2.0 | 2200 | 0.0574 | 0.9852 |
| 0.0222 | 3.0 | 3300 | 0.0689 | 0.9864 |
| 0.0098 | 4.0 | 4400 | 0.0924 | 0.9848 |
| 0.0059 | 5.0 | 5500 | 0.0824 | 0.9882 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "digitalepidemiologylab/covid-twitter-bert-v2", "model-index": [{"name": "FakeNewsDetection_Cross-Sean", "results": []}]} | text-classification | sgonzalezsilot/FakeNewsDetection_Cross-Sean | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:digitalepidemiologylab/covid-twitter-bert-v2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:38:22+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-digitalepidemiologylab/covid-twitter-bert-v2 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| FakeNewsDetection\_Cross-Sean
=============================
This model is a fine-tuned version of digitalepidemiologylab/covid-twitter-bert-v2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0824
* F1: 0.9882
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* 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: 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* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-digitalepidemiologylab/covid-twitter-bert-v2 #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: 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* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
70,
98,
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33
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-digitalepidemiologylab/covid-twitter-bert-v2 #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: 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* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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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 0.7.1 | {"library_name": "peft", "base_model": "microsoft/CodeGPT-small-py"} | null | adalib/sfepy-cond-gen-CodeGPT-small-py-prefix | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/CodeGPT-small-py #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
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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]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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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-v0.1-3.0bpw-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"
] | 2024-02-14T20:42:39+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 |
# Uploaded model
- **Developed by:** arendgb
- **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 | arendgb/restaurant_demo_mistral_ft_GGUF_q8 | [
"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"
] | 2024-02-14T20:44:53+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: arendgb
- 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: arendgb\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: arendgb\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,
85
] | [
"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: arendgb\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 | 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. -->
# marian-finetuned-kde4-en-to-it
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8490
- Bleu: 51.1019
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "base_model": "Helsinki-NLP/opus-mt-en-it", "model-index": [{"name": "marian-finetuned-kde4-en-to-it", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "config": "en-it", "split": "train", "args": "en-it"}, "metrics": [{"type": "bleu", "value": 51.10193545551006, "name": "Bleu"}]}]}]} | translation | callmyname/marian-finetuned-kde4-en-to-it | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-it",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:46:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-it #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-it
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-it on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8490
- Bleu: 51.1019
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# marian-finetuned-kde4-en-to-it\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-it on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8490\n- Bleu: 51.1019",
"## 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- gradient_accumulation_steps: 4\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: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-it #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-it\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-it on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8490\n- Bleu: 51.1019",
"## 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- gradient_accumulation_steps: 4\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: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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"passage: TAGS\n#transformers #tensorboard #safetensors #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-it #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# marian-finetuned-kde4-en-to-it\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-it on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8490\n- Bleu: 51.1019## 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- gradient_accumulation_steps: 4\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: 5### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_ner_reddit_cooking` |
| **Version** | `3.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `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 (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `DISH`, `EQUIPMENT`, `INGREDIENT` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 62.48 |
| `ENTS_P` | 62.26 |
| `ENTS_R` | 62.70 |
| `TOK2VEC_LOSS` | 76363.02 |
| `NER_LOSS` | 153362.98 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | wesslen/en_ner_reddit_cooking | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | 2024-02-14T20:46:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (3 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] | [
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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-v0.1-4.0bpw-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"
] | 2024-02-14T20:49:01+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 | 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": "286.93 +/- 13.89", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | OsnNos/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T20:49:02+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 | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: juan9/ppo-Pyramis-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | juan9/ppo-Pyramis-Training | [
"ml-agents",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-14T20:52:44+00:00 | [] | [] | TAGS
#ml-agents #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
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: juan9/ppo-Pyramis-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: juan9/ppo-Pyramis-Training\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: juan9/ppo-Pyramis-Training\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
40,
204
] | [
"passage: TAGS\n#ml-agents #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\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: juan9/ppo-Pyramis-Training\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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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. -->
# furina_seed42_eng_amh_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Spearman Corr: 0.7965
## 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 | 1.72 | 200 | 0.0245 | 0.7642 |
| 0.0761 | 3.45 | 400 | 0.0205 | 0.8068 |
| 0.0252 | 5.17 | 600 | 0.0201 | 0.8002 |
| 0.0185 | 6.9 | 800 | 0.0193 | 0.8106 |
| 0.0147 | 8.62 | 1000 | 0.0203 | 0.8017 |
| 0.0118 | 10.34 | 1200 | 0.0198 | 0.8021 |
| 0.0099 | 12.07 | 1400 | 0.0200 | 0.8029 |
| 0.0099 | 13.79 | 1600 | 0.0228 | 0.7997 |
| 0.0086 | 15.52 | 1800 | 0.0202 | 0.7982 |
| 0.0077 | 17.24 | 2000 | 0.0217 | 0.7921 |
| 0.0067 | 18.97 | 2200 | 0.0208 | 0.7985 |
| 0.0062 | 20.69 | 2400 | 0.0202 | 0.7965 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_amh_hau_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_amh_hau_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T20:53:47+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_amh\_hau\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0202
* Spearman Corr: 0.7965
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
| [
"### 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"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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"
] | [
60,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #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 | 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-v0.1-4.65bpw-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"
] | 2024-02-14T20:56:52+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 | null |
# Lora of chen_hai/鎮海/镇海 (Azur Lane)
## What Is This?
This is the LoRA model of waifu chen_hai/鎮海/镇海 (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/chen_hai_azurlane](https://huggingface.co/datasets/CyberHarem/chen_hai_azurlane), which contains 331 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 3320 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `chen_hai_azurlane`.**
* Pruned core tags for this waifu are `black_hair, breasts, large_breasts, hair_ornament, long_hair, bangs, purple_eyes, red_eyes, hair_flower`. 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 3071, you need to download [`3071/chen_hai_azurlane.pt`](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/3071/chen_hai_azurlane.pt) as the embedding and [`3071/chen_hai_azurlane.safetensors`](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/3071/chen_hai_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 3071.
1440 images (1.53 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 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 3071 | 38 | **0.997** | 0.939 | 0.815 | **0.823** | [Download](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/3071/chen_hai_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1577 | 20 | 0.968 | **0.981** | **0.830** | 0.782 | [Download](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/1577/chen_hai_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 2490 | 31 | 0.960 | 0.931 | 0.828 | 0.758 | [Download](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/2490/chen_hai_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 3237 | 40 | 0.970 | 0.906 | 0.811 | 0.755 | [Download](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/3237/chen_hai_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1328 | 17 | 0.957 | 0.959 | 0.822 | 0.746 | [Download](https://huggingface.co/CyberHarem/chen_hai_azurlane/resolve/main/1328/chen_hai_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 2573 to 3320](all/0.md)
* [Steps From 1743 to 2490](all/1.md)
* [Steps From 913 to 1660](all/2.md)
* [Steps From 83 to 830](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/chen_hai_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/chen_hai_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/chen_hai_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T20:56:58+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/chen_hai_azurlane #license-mit #region-us
| Lora of chen\_hai/鎮海/镇海 (Azur Lane)
===================================
What Is This?
-------------
This is the LoRA model of waifu chen\_hai/鎮海/镇海 (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/chen\_hai\_azurlane, which contains 331 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 3320 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'chen\_hai\_azurlane'.
* Pruned core tags for this waifu are 'black\_hair, breasts, large\_breasts, hair\_ornament, long\_hair, bangs, purple\_eyes, red\_eyes, hair\_flower'. 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 3071, you need to download '3071/chen\_hai\_azurlane.pt' as the embedding and '3071/chen\_hai\_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 3071.
1440 images (1.53 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 2573 to 3320
* Steps From 1743 to 2490
* Steps From 913 to 1660
* Steps From 83 to 830
| [
"### 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 3071, you need to download '3071/chen\\_hai\\_azurlane.pt' as the embedding and '3071/chen\\_hai\\_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 3071.\n\n\n1440 images (1.53 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 2573 to 3320\n* Steps From 1743 to 2490\n* Steps From 913 to 1660\n* Steps From 83 to 830"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/chen_hai_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 3071, you need to download '3071/chen\\_hai\\_azurlane.pt' as the embedding and '3071/chen\\_hai\\_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 3071.\n\n\n1440 images (1.53 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 2573 to 3320\n* Steps From 1743 to 2490\n* Steps From 913 to 1660\n* Steps From 83 to 830"
] | [
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] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/chen_hai_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 | 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 0.7.1 | {"library_name": "peft", "base_model": "microsoft/CodeGPT-small-py"} | null | adalib/megengine-cond-gen-CodeGPT-small-py-prefix | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/CodeGPT-small-py #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- 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
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APA:
## Glossary [optional]
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null | null | null |
# Lora of mccall/マッコール/麦考尔 (Azur Lane)
## What Is This?
This is the LoRA model of waifu mccall/マッコール/麦考尔 (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/mccall_azurlane](https://huggingface.co/datasets/CyberHarem/mccall_azurlane), which contains 25 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 `mccall_azurlane`.**
* Pruned core tags for this waifu are `blue_eyes, hair_ornament, long_hair, ahoge, pink_hair, star_hair_ornament, twintails, low_twintails, hairclip, bangs, 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 320, you need to download [`320/mccall_azurlane.pt`](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/320/mccall_azurlane.pt) as the embedding and [`320/mccall_azurlane.safetensors`](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/320/mccall_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 320.
1480 images (1.65 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 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:---------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 320 | 52 | **1.000** | 0.981 | **0.849** | **0.711** | [Download](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/320/mccall_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 540 | 87 | **1.000** | 0.964 | 0.849 | 0.711 | [Download](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/540/mccall_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 680 | 109 | **1.000** | **0.984** | 0.846 | 0.706 | [Download](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/680/mccall_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 480 | 77 | **1.000** | 0.972 | 0.845 | 0.704 | [Download](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/480/mccall_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 620 | 100 | **1.000** | 0.979 | 0.843 | 0.701 | [Download](https://huggingface.co/CyberHarem/mccall_azurlane/resolve/main/620/mccall_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/mccall_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/mccall_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/mccall_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T21:02:40+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/mccall_azurlane #license-mit #region-us
| Lora of mccall/マッコール/麦考尔 (Azur Lane)
====================================
What Is This?
-------------
This is the LoRA model of waifu mccall/マッコール/麦考尔 (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/mccall\_azurlane, which contains 25 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 'mccall\_azurlane'.
* Pruned core tags for this waifu are 'blue\_eyes, hair\_ornament, long\_hair, ahoge, pink\_hair, star\_hair\_ornament, twintails, low\_twintails, hairclip, bangs, 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 320, you need to download '320/mccall\_azurlane.pt' as the embedding and '320/mccall\_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 320.
1480 images (1.65 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 320, you need to download '320/mccall\\_azurlane.pt' as the embedding and '320/mccall\\_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 320.\n\n\n1480 images (1.65 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/mccall_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 320, you need to download '320/mccall\\_azurlane.pt' as the embedding and '320/mccall\\_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 320.\n\n\n1480 images (1.65 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/mccall_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-v0.1-5.0bpw-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"
] | 2024-02-14T21:05:55+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 | # Overview
This is the the Pythia-160m model developed by EleutherAI fine-tuned using Cell2Sentence on *full* scRNA-seq cells.
Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics.
We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences".
For more details, we refer to the paper linked below.
This model was trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197)
using 8 A100 40GB GPUs for approximately 20 hours on the following tasks:
1. conditional cell generation
2. unconditional cell generation
3. cell type prediction
## Cell2Sentence Links:
GitHub: <https://github.com/vandijklab/cell2sentence-ft>
Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3>
## Pythia Links:
GitHub: <https://github.com/EleutherAI/pythia>
Paper: <https://arxiv.org/abs/2304.01373>
Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m>
# Evaluation
This model was evaluated on KNN classification and Gromov-Wasserstein (GW) distance.
The label for a generated cell is the corresponding cell type used in its corresponding prompt for generation.
Ground truth cells were sampled with replacement from a held out test dataset.
The generated cells are converted to expression vectors using the method described in the paper.
For complete details on the experiments, we refer to the paper.
| Model | k=3 NN (↑) | k=5 NN (↑) | k=10 NN (↑) | k=25 NN (↑) | GW (↓) |
| :---- | :---: | :---: | :---: | :---: | :----: |
| scGEN | 0.2376 | 0.2330 | 0.2377 | 0.2335 | 315.9505 |
| scVI | 0.2436 | 0.2400 | 0.2425 | 0.2348 | 302.1285 |
| scDiffusion | 0.2335 | 0.2288 | 0.2368 | 0.2306 | 72.0208 |
| scGPT | 0.1838 | 0.1788 | 0.1811 | 0.1882 | 2989.8066 |
| **C2S (Pythia-160m)** | **0.2588** | **0.2565** | **0.2746** | **0.2715** | **54.3040** |
# Sample Code
We provide an example of how to use the model to conditionally generate a cell equipped with a post-processing function to remove duplicate and invalid genes.
In order to generate full cells, the `max_length` generation parameter should be changed to 9200.
However, we recommend using an A100 GPU for inference speed and memory capacity if full cell generation is required.
Unconditional cell generation and cell type prediction prompts are included as well, but we do not include an example cell sentence to format the prompt.
We refer to the paper and GitHub repository for instructions on how to transform expression vectors into cell sentences.
```
import json
import re
from collections import Counter
from typing import List
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def post_process_generated_cell_sentences(
cell_sentence: str,
gene_dictionary: List
):
"""
Post-processing function for generated cell sentences.
Invalid genes are removed and ranks of duplicated genes are averaged.
Arguments:
cell_sentence: generated cell sentence string
gene_dictionary: list of gene vocabulary (all uppercase)
Returns:
post_processed_sentence: generated cell sentence after post processing steps
"""
generated_gene_names = cell_sentence.split(" ")
generated_gene_names = [generated_gene.upper() for generated_gene in generated_gene_names]
#--- Remove nonsense genes ---#
generated_gene_names = [gene_name for gene_name in generated_gene_names if gene_name in gene_dictionary]
#--- Average ranks ---#
gene_name_to_occurrences = Counter(generated_gene_names) # get mapping of gene name --> number of occurrences
post_processed_sentence = generated_gene_names.copy() # copy of generated gene list
for gene_name in gene_name_to_occurrences:
if gene_name_to_occurrences[gene_name] > 1 and gene_name != replace_nonsense_string:
# Find positions of all occurrences of duplicated generated gene in list
# Note: using post_processed_sentence here; since duplicates are being removed, list will be
# getting shorter. Getting indices in original list will no longer be accurate positions
occurrence_positions = [idx for idx, elem in enumerate(post_processed_sentence) if elem == gene_name]
average_position = int(sum(occurrence_positions) / len(occurrence_positions))
# Remove occurrences
post_processed_sentence = [elem for elem in post_processed_sentence if elem != gene_name]
# Reinsert gene_name at average position
post_processed_sentence.insert(average_position, gene_name)
return post_processed_sentence
genes_path = "pbmc_vocab.json"
with open(vocab_path, "r") as f:
gene_dictionary = json.load(f)
model_name = "vandijklab/pythia-160m-c2s"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
attn_implementation="flash_attention_2"
).to(torch.device("cuda"))
tokenizer = AutoTokenizer.from_pretrained(model_name)
cell_type = "T Cell"
ccg = f"Enumerate the genes in a {cell_type} cell with nonzero expression, from highest to lowest."
# Prompts for other forms a generation.
# ucg = "Display a cell's genes by expression level, in descending order."
# cellsentence = "CELL_SENTENCE"
# ctp = "Identify the cell type most likely associated with these highly expressed genes listed in descending order. "
# + cellsentence +
# "Name the cell type connected to these genes, ranked from highest to lowest expression."
tokens = tokenizer(ccg, return_tensors='pt')
input_ids = tokens['input_ids'].to(torch.device("cuda"))
attention_mask = tokens['attention_mask'].to(torch.device("cuda"))
with torch.no_grad():
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
do_sample=True,
max_length=1024,
top_k=50,
top_p=0.95,
)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
cell_sentence = "".join(re.split(r"\?|\.|:", output_text)[1:]).strip()
processed_genes = post_process_generated_cell_sentences(cell_sentence, gene_dictionary)
``` | {"language": ["en"], "license": "cc-by-nc-nd-4.0", "tags": ["pytorch", "causal-lm", "scRNA-seq"], "datasets": ["vandijklab/immune-c2s"]} | text-generation | vandijklab/pythia-160m-c2s | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"pytorch",
"causal-lm",
"scRNA-seq",
"en",
"dataset:vandijklab/immune-c2s",
"arxiv:2304.01373",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:06:44+00:00 | [
"2304.01373"
] | [
"en"
] | TAGS
#transformers #safetensors #gpt_neox #text-generation #pytorch #causal-lm #scRNA-seq #en #dataset-vandijklab/immune-c2s #arxiv-2304.01373 #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Overview
========
This is the the Pythia-160m model developed by EleutherAI fine-tuned using Cell2Sentence on *full* scRNA-seq cells.
Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics.
We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences".
For more details, we refer to the paper linked below.
This model was trained on the immune tissue dataset from Domínguez et al.
using 8 A100 40GB GPUs for approximately 20 hours on the following tasks:
1. conditional cell generation
2. unconditional cell generation
3. cell type prediction
Cell2Sentence Links:
--------------------
GitHub: <URL
Paper: <URL
Pythia Links:
-------------
GitHub: <URL
Paper: <URL
Hugging Face: <URL
Evaluation
==========
This model was evaluated on KNN classification and Gromov-Wasserstein (GW) distance.
The label for a generated cell is the corresponding cell type used in its corresponding prompt for generation.
Ground truth cells were sampled with replacement from a held out test dataset.
The generated cells are converted to expression vectors using the method described in the paper.
For complete details on the experiments, we refer to the paper.
Sample Code
===========
We provide an example of how to use the model to conditionally generate a cell equipped with a post-processing function to remove duplicate and invalid genes.
In order to generate full cells, the 'max\_length' generation parameter should be changed to 9200.
However, we recommend using an A100 GPU for inference speed and memory capacity if full cell generation is required.
Unconditional cell generation and cell type prediction prompts are included as well, but we do not include an example cell sentence to format the prompt.
We refer to the paper and GitHub repository for instructions on how to transform expression vectors into cell sentences.
| [] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #pytorch #causal-lm #scRNA-seq #en #dataset-vandijklab/immune-c2s #arxiv-2304.01373 #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
105
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #pytorch #causal-lm #scRNA-seq #en #dataset-vandijklab/immune-c2s #arxiv-2304.01373 #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | null | git lfs install
git clone https://huggingface.co/spaces/coqui/xtts | {} | null | Tigerstar/coqui-tts | [
"region:us"
] | 2024-02-14T21:10:52+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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Subsets and Splits