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text-generation | transformers |
# OrpoLlama3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 1.5k steps of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Muhammad2003/OrpoLlama3-8B"
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"])
```
## 📈 Training curves
Wandb Report
## 🏆 Evaluation
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["orpo"], "datasets": ["mlabonne/orpo-dpo-mix-40k"], "base_model": ["meta-llama/Meta-Llama-3-8B"]} | Muhammad2003/OrpoLlama3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:35:57+00:00 |
image-classification | 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold3
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0008
- Accuracy: 0.6562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.495 | 1.0 | 923 | 1.4345 | 0.5207 |
| 1.3175 | 2.0 | 1846 | 1.2061 | 0.5953 |
| 1.2659 | 3.0 | 2769 | 1.1111 | 0.6154 |
| 1.0775 | 4.0 | 3692 | 1.0599 | 0.6343 |
| 0.9198 | 5.0 | 4615 | 1.0400 | 0.6443 |
| 0.9183 | 6.0 | 5538 | 1.0344 | 0.6440 |
| 0.869 | 7.0 | 6461 | 1.0165 | 0.6546 |
| 0.8871 | 8.0 | 7384 | 1.0264 | 0.6451 |
| 0.9047 | 9.0 | 8307 | 0.9953 | 0.6567 |
| 0.8868 | 10.0 | 9230 | 1.0008 | 0.6562 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-tiny-patch4-window16-256", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6562077360021639, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold3 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-tiny-patch4-window16-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T08:36:12+00:00 |
text-generation | transformers |
# llama-lexi-star-uncensored-8b-slerp
llama-lexi-star-uncensored-8b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1](https://huggingface.co/Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1)
* [liminerity/llama-3-8b-silent-star](https://huggingface.co/liminerity/llama-3-8b-silent-star)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1
layer_range: [0, 32]
- model: liminerity/llama-3-8b-silent-star
layer_range: [0, 32]
merge_method: slerp
base_model: Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/llama-lexi-star-uncensored-8b-slerp"
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", "Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1", "liminerity/llama-3-8b-silent-star"], "base_model": ["Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1", "liminerity/llama-3-8b-silent-star"]} | liminerity/llama-lexi-star-uncensored-8b-slerp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1",
"liminerity/llama-3-8b-silent-star",
"conversational",
"base_model:Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1",
"base_model:liminerity/llama-3-8b-silent-star",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:36:37+00:00 |
feature-extraction | transformers | {} | zzfive/ComfyChat-InternLM2-1-8b-v1 | null | [
"transformers",
"pytorch",
"internlm2",
"feature-extraction",
"custom_code",
"region:us"
]
| null | 2024-04-27T08:37:25+00:00 |
|
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Andro9669/flan-t5-ner | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:38:44+00:00 |
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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[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
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[More Information Needed]
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[More Information Needed]
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[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]
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| {"library_name": "transformers", "tags": []} | mjbuehler/Phi-3-mini-V200_NOINST_29040 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T08:39:23+00:00 |
null | null | {"license": "mit"} | th2w33knd/The-Weeknd-v1-RVC | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T08:41:13+00:00 |
|
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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[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
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#### Preprocessing [optional]
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[More Information Needed]
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[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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | swj0419/hp_all_STEP0000030 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:41:41+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Joy10/bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5055
- Validation Loss: 0.4208
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5055 | 0.4208 | 0 |
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "Joy10/bert-fine-tuned-cola", "results": []}]} | Joy10/bert-fine-tuned-cola | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T08:43:49+00:00 |
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. -->
# GUE_mouse_0-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5635
- F1 Score: 0.7205
- Accuracy: 0.7210
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6564 | 3.92 | 200 | 0.6224 | 0.6385 | 0.6432 |
| 0.6217 | 7.84 | 400 | 0.6094 | 0.6614 | 0.6642 |
| 0.6073 | 11.76 | 600 | 0.5943 | 0.6889 | 0.6889 |
| 0.5948 | 15.69 | 800 | 0.5874 | 0.6786 | 0.6790 |
| 0.5833 | 19.61 | 1000 | 0.5781 | 0.6951 | 0.6951 |
| 0.5724 | 23.53 | 1200 | 0.5694 | 0.6999 | 0.7 |
| 0.5626 | 27.45 | 1400 | 0.5663 | 0.7110 | 0.7111 |
| 0.5543 | 31.37 | 1600 | 0.5589 | 0.7146 | 0.7148 |
| 0.5518 | 35.29 | 1800 | 0.5574 | 0.7096 | 0.7099 |
| 0.5444 | 39.22 | 2000 | 0.5594 | 0.7062 | 0.7062 |
| 0.5396 | 43.14 | 2200 | 0.5516 | 0.7146 | 0.7148 |
| 0.5383 | 47.06 | 2400 | 0.5514 | 0.7175 | 0.7185 |
| 0.529 | 50.98 | 2600 | 0.5583 | 0.7196 | 0.7198 |
| 0.5288 | 54.9 | 2800 | 0.5527 | 0.7209 | 0.7222 |
| 0.5224 | 58.82 | 3000 | 0.5505 | 0.7300 | 0.7309 |
| 0.524 | 62.75 | 3200 | 0.5464 | 0.7146 | 0.7148 |
| 0.517 | 66.67 | 3400 | 0.5473 | 0.7144 | 0.7148 |
| 0.516 | 70.59 | 3600 | 0.5467 | 0.7099 | 0.7099 |
| 0.5154 | 74.51 | 3800 | 0.5446 | 0.7219 | 0.7222 |
| 0.5083 | 78.43 | 4000 | 0.5455 | 0.7254 | 0.7259 |
| 0.5097 | 82.35 | 4200 | 0.5412 | 0.7212 | 0.7222 |
| 0.5092 | 86.27 | 4400 | 0.5490 | 0.7148 | 0.7148 |
| 0.5063 | 90.2 | 4600 | 0.5409 | 0.7312 | 0.7321 |
| 0.5021 | 94.12 | 4800 | 0.5503 | 0.7247 | 0.7247 |
| 0.499 | 98.04 | 5000 | 0.5381 | 0.7235 | 0.7247 |
| 0.4951 | 101.96 | 5200 | 0.5424 | 0.7244 | 0.7247 |
| 0.4959 | 105.88 | 5400 | 0.5378 | 0.7266 | 0.7272 |
| 0.4952 | 109.8 | 5600 | 0.5411 | 0.7282 | 0.7284 |
| 0.4934 | 113.73 | 5800 | 0.5398 | 0.7261 | 0.7272 |
| 0.4878 | 117.65 | 6000 | 0.5439 | 0.7229 | 0.7235 |
| 0.49 | 121.57 | 6200 | 0.5387 | 0.7274 | 0.7284 |
| 0.4839 | 125.49 | 6400 | 0.5420 | 0.7257 | 0.7259 |
| 0.4836 | 129.41 | 6600 | 0.5420 | 0.7307 | 0.7309 |
| 0.4809 | 133.33 | 6800 | 0.5407 | 0.7264 | 0.7272 |
| 0.482 | 137.25 | 7000 | 0.5404 | 0.7367 | 0.7370 |
| 0.4776 | 141.18 | 7200 | 0.5408 | 0.7277 | 0.7284 |
| 0.4834 | 145.1 | 7400 | 0.5383 | 0.7292 | 0.7296 |
| 0.4763 | 149.02 | 7600 | 0.5390 | 0.7279 | 0.7284 |
| 0.4797 | 152.94 | 7800 | 0.5366 | 0.7241 | 0.7247 |
| 0.4728 | 156.86 | 8000 | 0.5408 | 0.7251 | 0.7259 |
| 0.4733 | 160.78 | 8200 | 0.5429 | 0.7331 | 0.7333 |
| 0.476 | 164.71 | 8400 | 0.5407 | 0.7305 | 0.7309 |
| 0.4738 | 168.63 | 8600 | 0.5379 | 0.7290 | 0.7296 |
| 0.474 | 172.55 | 8800 | 0.5381 | 0.7315 | 0.7321 |
| 0.4708 | 176.47 | 9000 | 0.5386 | 0.7304 | 0.7309 |
| 0.4704 | 180.39 | 9200 | 0.5386 | 0.7254 | 0.7259 |
| 0.4713 | 184.31 | 9400 | 0.5405 | 0.7357 | 0.7358 |
| 0.4721 | 188.24 | 9600 | 0.5387 | 0.7319 | 0.7321 |
| 0.4729 | 192.16 | 9800 | 0.5376 | 0.7317 | 0.7321 |
| 0.4677 | 196.08 | 10000 | 0.5380 | 0.7305 | 0.7309 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_0-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:44:02+00:00 |
null | transformers | {} | ikeno-ada/m2m100_418M_ct2_8bit | null | [
"transformers",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T08:44:23+00:00 |
|
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. -->
# GUE_mouse_0-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5997
- F1 Score: 0.7167
- Accuracy: 0.7173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6394 | 3.92 | 200 | 0.6016 | 0.6585 | 0.6593 |
| 0.5841 | 7.84 | 400 | 0.5833 | 0.6963 | 0.6963 |
| 0.5603 | 11.76 | 600 | 0.5582 | 0.6959 | 0.6963 |
| 0.5449 | 15.69 | 800 | 0.5542 | 0.7118 | 0.7136 |
| 0.5321 | 19.61 | 1000 | 0.5486 | 0.7220 | 0.7222 |
| 0.5229 | 23.53 | 1200 | 0.5510 | 0.7156 | 0.7185 |
| 0.5101 | 27.45 | 1400 | 0.5456 | 0.7161 | 0.7173 |
| 0.502 | 31.37 | 1600 | 0.5552 | 0.7308 | 0.7309 |
| 0.5001 | 35.29 | 1800 | 0.5464 | 0.7279 | 0.7284 |
| 0.4876 | 39.22 | 2000 | 0.5472 | 0.7268 | 0.7272 |
| 0.4817 | 43.14 | 2200 | 0.5512 | 0.7293 | 0.7296 |
| 0.4792 | 47.06 | 2400 | 0.5515 | 0.7200 | 0.7210 |
| 0.4678 | 50.98 | 2600 | 0.5660 | 0.7355 | 0.7358 |
| 0.4624 | 54.9 | 2800 | 0.5581 | 0.7278 | 0.7284 |
| 0.4526 | 58.82 | 3000 | 0.5618 | 0.7355 | 0.7358 |
| 0.4517 | 62.75 | 3200 | 0.5550 | 0.7357 | 0.7358 |
| 0.4423 | 66.67 | 3400 | 0.5705 | 0.7354 | 0.7358 |
| 0.4367 | 70.59 | 3600 | 0.5654 | 0.7308 | 0.7309 |
| 0.4367 | 74.51 | 3800 | 0.5579 | 0.7374 | 0.7383 |
| 0.4271 | 78.43 | 4000 | 0.5731 | 0.7345 | 0.7346 |
| 0.424 | 82.35 | 4200 | 0.5756 | 0.7413 | 0.7432 |
| 0.4235 | 86.27 | 4400 | 0.5632 | 0.7367 | 0.7370 |
| 0.4196 | 90.2 | 4600 | 0.5668 | 0.7441 | 0.7444 |
| 0.4111 | 94.12 | 4800 | 0.5751 | 0.7407 | 0.7407 |
| 0.4096 | 98.04 | 5000 | 0.5687 | 0.7287 | 0.7296 |
| 0.4011 | 101.96 | 5200 | 0.5765 | 0.7432 | 0.7432 |
| 0.3974 | 105.88 | 5400 | 0.5788 | 0.7469 | 0.7469 |
| 0.3932 | 109.8 | 5600 | 0.5931 | 0.7370 | 0.7370 |
| 0.3941 | 113.73 | 5800 | 0.5861 | 0.7382 | 0.7383 |
| 0.3867 | 117.65 | 6000 | 0.5959 | 0.7427 | 0.7432 |
| 0.3884 | 121.57 | 6200 | 0.5960 | 0.7284 | 0.7284 |
| 0.3814 | 125.49 | 6400 | 0.5965 | 0.7308 | 0.7309 |
| 0.378 | 129.41 | 6600 | 0.6032 | 0.7371 | 0.7370 |
| 0.3754 | 133.33 | 6800 | 0.5998 | 0.7382 | 0.7383 |
| 0.3738 | 137.25 | 7000 | 0.6128 | 0.7404 | 0.7407 |
| 0.3691 | 141.18 | 7200 | 0.6121 | 0.7407 | 0.7407 |
| 0.3685 | 145.1 | 7400 | 0.6061 | 0.7391 | 0.7395 |
| 0.3679 | 149.02 | 7600 | 0.6080 | 0.7408 | 0.7407 |
| 0.3621 | 152.94 | 7800 | 0.6186 | 0.7327 | 0.7333 |
| 0.3606 | 156.86 | 8000 | 0.6166 | 0.7354 | 0.7358 |
| 0.3614 | 160.78 | 8200 | 0.6140 | 0.7341 | 0.7346 |
| 0.364 | 164.71 | 8400 | 0.6149 | 0.7407 | 0.7407 |
| 0.3583 | 168.63 | 8600 | 0.6186 | 0.7382 | 0.7383 |
| 0.3594 | 172.55 | 8800 | 0.6170 | 0.7418 | 0.7420 |
| 0.3545 | 176.47 | 9000 | 0.6191 | 0.7367 | 0.7370 |
| 0.3482 | 180.39 | 9200 | 0.6245 | 0.7403 | 0.7407 |
| 0.3565 | 184.31 | 9400 | 0.6209 | 0.7382 | 0.7383 |
| 0.3529 | 188.24 | 9600 | 0.6239 | 0.7407 | 0.7407 |
| 0.3537 | 192.16 | 9800 | 0.6203 | 0.7368 | 0.7370 |
| 0.3489 | 196.08 | 10000 | 0.6220 | 0.7344 | 0.7346 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_0-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:45:29+00:00 |
null | null | {} | NanyangTechnologicalUniversity/demo | null | [
"region:us"
]
| null | 2024-04-27T08:46:30+00:00 |
|
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. -->
# GUE_mouse_0-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0468
- F1 Score: 0.6972
- Accuracy: 0.6975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6263 | 3.92 | 200 | 0.5880 | 0.6701 | 0.6704 |
| 0.5607 | 7.84 | 400 | 0.5651 | 0.7075 | 0.7074 |
| 0.536 | 11.76 | 600 | 0.5482 | 0.7171 | 0.7185 |
| 0.5152 | 15.69 | 800 | 0.5594 | 0.7142 | 0.7185 |
| 0.4947 | 19.61 | 1000 | 0.5645 | 0.7309 | 0.7309 |
| 0.4717 | 23.53 | 1200 | 0.5769 | 0.7318 | 0.7321 |
| 0.443 | 27.45 | 1400 | 0.5844 | 0.7420 | 0.7420 |
| 0.4217 | 31.37 | 1600 | 0.6111 | 0.7307 | 0.7309 |
| 0.4108 | 35.29 | 1800 | 0.6075 | 0.7333 | 0.7333 |
| 0.3866 | 39.22 | 2000 | 0.6402 | 0.7198 | 0.7198 |
| 0.3733 | 43.14 | 2200 | 0.6276 | 0.7303 | 0.7309 |
| 0.359 | 47.06 | 2400 | 0.6436 | 0.7263 | 0.7272 |
| 0.3406 | 50.98 | 2600 | 0.6952 | 0.7270 | 0.7272 |
| 0.3281 | 54.9 | 2800 | 0.6855 | 0.7235 | 0.7247 |
| 0.3074 | 58.82 | 3000 | 0.7191 | 0.7254 | 0.7259 |
| 0.3002 | 62.75 | 3200 | 0.7193 | 0.7246 | 0.7247 |
| 0.2824 | 66.67 | 3400 | 0.7518 | 0.7242 | 0.7247 |
| 0.2723 | 70.59 | 3600 | 0.7467 | 0.7210 | 0.7210 |
| 0.2654 | 74.51 | 3800 | 0.7536 | 0.7109 | 0.7123 |
| 0.2514 | 78.43 | 4000 | 0.7737 | 0.7307 | 0.7309 |
| 0.2442 | 82.35 | 4200 | 0.8082 | 0.7271 | 0.7272 |
| 0.2345 | 86.27 | 4400 | 0.7781 | 0.7230 | 0.7235 |
| 0.2288 | 90.2 | 4600 | 0.8172 | 0.7233 | 0.7235 |
| 0.2184 | 94.12 | 4800 | 0.8705 | 0.7296 | 0.7296 |
| 0.2106 | 98.04 | 5000 | 0.8488 | 0.7295 | 0.7296 |
| 0.2046 | 101.96 | 5200 | 0.8438 | 0.7193 | 0.7198 |
| 0.198 | 105.88 | 5400 | 0.8727 | 0.7321 | 0.7321 |
| 0.1937 | 109.8 | 5600 | 0.9359 | 0.7223 | 0.7222 |
| 0.1874 | 113.73 | 5800 | 0.9022 | 0.7270 | 0.7272 |
| 0.1748 | 117.65 | 6000 | 0.9720 | 0.7275 | 0.7284 |
| 0.1851 | 121.57 | 6200 | 0.9062 | 0.7283 | 0.7284 |
| 0.1712 | 125.49 | 6400 | 0.9613 | 0.7290 | 0.7296 |
| 0.1672 | 129.41 | 6600 | 0.9403 | 0.7346 | 0.7346 |
| 0.1641 | 133.33 | 6800 | 0.9332 | 0.7258 | 0.7259 |
| 0.1602 | 137.25 | 7000 | 0.9433 | 0.7355 | 0.7358 |
| 0.1522 | 141.18 | 7200 | 1.0043 | 0.7342 | 0.7346 |
| 0.1536 | 145.1 | 7400 | 0.9667 | 0.7306 | 0.7309 |
| 0.1514 | 149.02 | 7600 | 0.9953 | 0.7321 | 0.7321 |
| 0.1477 | 152.94 | 7800 | 1.0024 | 0.7176 | 0.7185 |
| 0.1456 | 156.86 | 8000 | 1.0120 | 0.7330 | 0.7333 |
| 0.141 | 160.78 | 8200 | 1.0228 | 0.7431 | 0.7432 |
| 0.1441 | 164.71 | 8400 | 1.0232 | 0.7395 | 0.7395 |
| 0.1394 | 168.63 | 8600 | 1.0253 | 0.7367 | 0.7370 |
| 0.138 | 172.55 | 8800 | 1.0213 | 0.7308 | 0.7309 |
| 0.1402 | 176.47 | 9000 | 1.0117 | 0.7269 | 0.7272 |
| 0.1335 | 180.39 | 9200 | 1.0230 | 0.7240 | 0.7247 |
| 0.1363 | 184.31 | 9400 | 1.0253 | 0.7342 | 0.7346 |
| 0.1356 | 188.24 | 9600 | 1.0213 | 0.7369 | 0.7370 |
| 0.1351 | 192.16 | 9800 | 1.0196 | 0.7305 | 0.7309 |
| 0.1334 | 196.08 | 10000 | 1.0227 | 0.7317 | 0.7321 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_0-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:46:52+00:00 |
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. -->
# GUE_mouse_1-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2586
- F1 Score: 0.8835
- Accuracy: 0.8836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5207 | 0.47 | 200 | 0.4244 | 0.7974 | 0.7985 |
| 0.4191 | 0.95 | 400 | 0.3640 | 0.8336 | 0.8338 |
| 0.3748 | 1.42 | 600 | 0.3333 | 0.8479 | 0.8480 |
| 0.3603 | 1.9 | 800 | 0.3115 | 0.8617 | 0.8617 |
| 0.3365 | 2.37 | 1000 | 0.3054 | 0.8665 | 0.8666 |
| 0.3339 | 2.84 | 1200 | 0.2994 | 0.8685 | 0.8685 |
| 0.3174 | 3.32 | 1400 | 0.2950 | 0.8698 | 0.8698 |
| 0.326 | 3.79 | 1600 | 0.2910 | 0.8710 | 0.8710 |
| 0.322 | 4.27 | 1800 | 0.2886 | 0.8710 | 0.8712 |
| 0.3142 | 4.74 | 2000 | 0.2929 | 0.8715 | 0.8715 |
| 0.3177 | 5.21 | 2200 | 0.2829 | 0.8736 | 0.8737 |
| 0.3112 | 5.69 | 2400 | 0.2853 | 0.8752 | 0.8752 |
| 0.31 | 6.16 | 2600 | 0.2785 | 0.8782 | 0.8783 |
| 0.3046 | 6.64 | 2800 | 0.2764 | 0.8777 | 0.8778 |
| 0.3052 | 7.11 | 3000 | 0.2813 | 0.8795 | 0.8795 |
| 0.2977 | 7.58 | 3200 | 0.2804 | 0.8769 | 0.8769 |
| 0.2955 | 8.06 | 3400 | 0.2682 | 0.8829 | 0.8830 |
| 0.2918 | 8.53 | 3600 | 0.2724 | 0.8837 | 0.8838 |
| 0.2966 | 9.0 | 3800 | 0.2643 | 0.8853 | 0.8854 |
| 0.2891 | 9.48 | 4000 | 0.2637 | 0.8836 | 0.8838 |
| 0.2887 | 9.95 | 4200 | 0.2616 | 0.8852 | 0.8852 |
| 0.2876 | 10.43 | 4400 | 0.2599 | 0.8861 | 0.8861 |
| 0.2861 | 10.9 | 4600 | 0.2610 | 0.8854 | 0.8854 |
| 0.2805 | 11.37 | 4800 | 0.2586 | 0.8870 | 0.8870 |
| 0.2885 | 11.85 | 5000 | 0.2579 | 0.8871 | 0.8872 |
| 0.279 | 12.32 | 5200 | 0.2650 | 0.8818 | 0.8818 |
| 0.2872 | 12.8 | 5400 | 0.2591 | 0.8854 | 0.8854 |
| 0.2859 | 13.27 | 5600 | 0.2552 | 0.8900 | 0.8900 |
| 0.2788 | 13.74 | 5800 | 0.2563 | 0.8886 | 0.8887 |
| 0.2797 | 14.22 | 6000 | 0.2555 | 0.8901 | 0.8901 |
| 0.2761 | 14.69 | 6200 | 0.2562 | 0.8900 | 0.8900 |
| 0.2843 | 15.17 | 6400 | 0.2537 | 0.8911 | 0.8912 |
| 0.281 | 15.64 | 6600 | 0.2543 | 0.8901 | 0.8901 |
| 0.2766 | 16.11 | 6800 | 0.2530 | 0.8906 | 0.8906 |
| 0.2729 | 16.59 | 7000 | 0.2518 | 0.8928 | 0.8928 |
| 0.2761 | 17.06 | 7200 | 0.2532 | 0.8913 | 0.8913 |
| 0.2746 | 17.54 | 7400 | 0.2535 | 0.8897 | 0.8897 |
| 0.2766 | 18.01 | 7600 | 0.2522 | 0.8928 | 0.8928 |
| 0.2704 | 18.48 | 7800 | 0.2537 | 0.8882 | 0.8882 |
| 0.278 | 18.96 | 8000 | 0.2511 | 0.8913 | 0.8913 |
| 0.2738 | 19.43 | 8200 | 0.2520 | 0.8912 | 0.8912 |
| 0.2696 | 19.91 | 8400 | 0.2523 | 0.8926 | 0.8927 |
| 0.276 | 20.38 | 8600 | 0.2510 | 0.8920 | 0.8921 |
| 0.2732 | 20.85 | 8800 | 0.2513 | 0.8932 | 0.8933 |
| 0.2723 | 21.33 | 9000 | 0.2506 | 0.8920 | 0.8921 |
| 0.272 | 21.8 | 9200 | 0.2518 | 0.8913 | 0.8913 |
| 0.2753 | 22.27 | 9400 | 0.2511 | 0.8906 | 0.8906 |
| 0.27 | 22.75 | 9600 | 0.2511 | 0.8931 | 0.8931 |
| 0.2741 | 23.22 | 9800 | 0.2515 | 0.8913 | 0.8913 |
| 0.2747 | 23.7 | 10000 | 0.2511 | 0.8917 | 0.8918 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:46:52+00:00 |
image-text-to-text | xtuner |
# nullt3r/llava-llama-3-8b-v1_1-Q8_0-GGUF
This model was converted to GGUF format from [`xtuner/llava-llama-3-8b-v1_1`](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo nullt3r/llava-llama-3-8b-v1_1-Q8_0-GGUF --model llava-llama-3-8b-v1_1.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo nullt3r/llava-llama-3-8b-v1_1-Q8_0-GGUF --model llava-llama-3-8b-v1_1.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llava-llama-3-8b-v1_1.Q8_0.gguf -n 128
```
| {"library_name": "xtuner", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-text-to-text"} | nullt3r/llava-llama-3-8b-v1_1-Q8_0-GGUF | null | [
"xtuner",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"dataset:Lin-Chen/ShareGPT4V",
"region:us"
]
| null | 2024-04-27T08:47:03+00:00 |
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. -->
# GUE_mouse_1-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2437
- F1 Score: 0.8923
- Accuracy: 0.8924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4767 | 0.47 | 200 | 0.3561 | 0.8367 | 0.8369 |
| 0.3515 | 0.95 | 400 | 0.3060 | 0.8652 | 0.8652 |
| 0.3248 | 1.42 | 600 | 0.2997 | 0.8689 | 0.8689 |
| 0.3262 | 1.9 | 800 | 0.2849 | 0.8739 | 0.8740 |
| 0.3086 | 2.37 | 1000 | 0.2851 | 0.8756 | 0.8756 |
| 0.3066 | 2.84 | 1200 | 0.2785 | 0.8781 | 0.8781 |
| 0.2922 | 3.32 | 1400 | 0.2750 | 0.8809 | 0.8809 |
| 0.3007 | 3.79 | 1600 | 0.2737 | 0.8796 | 0.8796 |
| 0.2944 | 4.27 | 1800 | 0.2640 | 0.8852 | 0.8852 |
| 0.2874 | 4.74 | 2000 | 0.2741 | 0.8803 | 0.8804 |
| 0.2901 | 5.21 | 2200 | 0.2590 | 0.8854 | 0.8855 |
| 0.2851 | 5.69 | 2400 | 0.2642 | 0.8823 | 0.8823 |
| 0.2826 | 6.16 | 2600 | 0.2554 | 0.8893 | 0.8894 |
| 0.2775 | 6.64 | 2800 | 0.2556 | 0.8875 | 0.8876 |
| 0.2792 | 7.11 | 3000 | 0.2627 | 0.8861 | 0.8861 |
| 0.2683 | 7.58 | 3200 | 0.2628 | 0.8854 | 0.8854 |
| 0.2684 | 8.06 | 3400 | 0.2478 | 0.8922 | 0.8922 |
| 0.2635 | 8.53 | 3600 | 0.2552 | 0.8900 | 0.8900 |
| 0.2697 | 9.0 | 3800 | 0.2481 | 0.8928 | 0.8928 |
| 0.2586 | 9.48 | 4000 | 0.2448 | 0.8924 | 0.8925 |
| 0.2628 | 9.95 | 4200 | 0.2417 | 0.8947 | 0.8947 |
| 0.2588 | 10.43 | 4400 | 0.2436 | 0.8946 | 0.8946 |
| 0.2597 | 10.9 | 4600 | 0.2454 | 0.8928 | 0.8928 |
| 0.2524 | 11.37 | 4800 | 0.2446 | 0.8930 | 0.8930 |
| 0.2614 | 11.85 | 5000 | 0.2406 | 0.8948 | 0.8949 |
| 0.253 | 12.32 | 5200 | 0.2517 | 0.8901 | 0.8901 |
| 0.2563 | 12.8 | 5400 | 0.2459 | 0.8940 | 0.8940 |
| 0.2594 | 13.27 | 5600 | 0.2411 | 0.8943 | 0.8943 |
| 0.2503 | 13.74 | 5800 | 0.2395 | 0.8948 | 0.8949 |
| 0.2517 | 14.22 | 6000 | 0.2416 | 0.8949 | 0.8949 |
| 0.2498 | 14.69 | 6200 | 0.2394 | 0.8966 | 0.8967 |
| 0.2543 | 15.17 | 6400 | 0.2400 | 0.8967 | 0.8967 |
| 0.2535 | 15.64 | 6600 | 0.2396 | 0.8961 | 0.8961 |
| 0.2471 | 16.11 | 6800 | 0.2388 | 0.8969 | 0.8970 |
| 0.2464 | 16.59 | 7000 | 0.2375 | 0.8964 | 0.8964 |
| 0.2471 | 17.06 | 7200 | 0.2391 | 0.8980 | 0.8980 |
| 0.2462 | 17.54 | 7400 | 0.2388 | 0.8968 | 0.8968 |
| 0.2464 | 18.01 | 7600 | 0.2365 | 0.8984 | 0.8984 |
| 0.2426 | 18.48 | 7800 | 0.2423 | 0.8958 | 0.8958 |
| 0.2494 | 18.96 | 8000 | 0.2364 | 0.8983 | 0.8983 |
| 0.2439 | 19.43 | 8200 | 0.2368 | 0.8974 | 0.8974 |
| 0.2412 | 19.91 | 8400 | 0.2376 | 0.8977 | 0.8977 |
| 0.2476 | 20.38 | 8600 | 0.2362 | 0.8987 | 0.8987 |
| 0.2432 | 20.85 | 8800 | 0.2373 | 0.8963 | 0.8964 |
| 0.2417 | 21.33 | 9000 | 0.2371 | 0.8983 | 0.8983 |
| 0.2422 | 21.8 | 9200 | 0.2384 | 0.8984 | 0.8984 |
| 0.2472 | 22.27 | 9400 | 0.2367 | 0.8976 | 0.8976 |
| 0.2393 | 22.75 | 9600 | 0.2363 | 0.8977 | 0.8977 |
| 0.2441 | 23.22 | 9800 | 0.2365 | 0.8983 | 0.8983 |
| 0.2437 | 23.7 | 10000 | 0.2365 | 0.8986 | 0.8986 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:47:33+00:00 |
text2text-generation | 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. -->
# CS505_COQE_viT5_total_Instruction0_SOPAL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_SOPAL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_SOPAL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:47:44+00:00 |
null | null | {"license": "apache-2.0"} | Defetya/qwen-14B-glue | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T08:48:33+00:00 |
|
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. -->
# GUE_mouse_1-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2345
- F1 Score: 0.8972
- Accuracy: 0.8973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4498 | 0.47 | 200 | 0.3274 | 0.8542 | 0.8543 |
| 0.3336 | 0.95 | 400 | 0.2971 | 0.8719 | 0.8719 |
| 0.312 | 1.42 | 600 | 0.2770 | 0.8788 | 0.8789 |
| 0.3121 | 1.9 | 800 | 0.2705 | 0.8816 | 0.8817 |
| 0.2937 | 2.37 | 1000 | 0.2703 | 0.8827 | 0.8827 |
| 0.2882 | 2.84 | 1200 | 0.2592 | 0.8882 | 0.8882 |
| 0.2719 | 3.32 | 1400 | 0.2574 | 0.8910 | 0.8910 |
| 0.2787 | 3.79 | 1600 | 0.2588 | 0.8884 | 0.8884 |
| 0.271 | 4.27 | 1800 | 0.2445 | 0.8925 | 0.8927 |
| 0.264 | 4.74 | 2000 | 0.2548 | 0.8910 | 0.8910 |
| 0.264 | 5.21 | 2200 | 0.2403 | 0.8947 | 0.8949 |
| 0.2588 | 5.69 | 2400 | 0.2511 | 0.8913 | 0.8913 |
| 0.2558 | 6.16 | 2600 | 0.2398 | 0.8958 | 0.8959 |
| 0.2536 | 6.64 | 2800 | 0.2412 | 0.8919 | 0.8921 |
| 0.2537 | 7.11 | 3000 | 0.2462 | 0.8953 | 0.8953 |
| 0.2422 | 7.58 | 3200 | 0.2513 | 0.8922 | 0.8922 |
| 0.2462 | 8.06 | 3400 | 0.2359 | 0.8978 | 0.8979 |
| 0.241 | 8.53 | 3600 | 0.2387 | 0.8993 | 0.8993 |
| 0.2473 | 9.0 | 3800 | 0.2374 | 0.8972 | 0.8973 |
| 0.239 | 9.48 | 4000 | 0.2336 | 0.8980 | 0.8981 |
| 0.2427 | 9.95 | 4200 | 0.2301 | 0.9011 | 0.9011 |
| 0.2367 | 10.43 | 4400 | 0.2347 | 0.8989 | 0.8989 |
| 0.2387 | 10.9 | 4600 | 0.2353 | 0.8989 | 0.8989 |
| 0.2315 | 11.37 | 4800 | 0.2392 | 0.8990 | 0.8990 |
| 0.2412 | 11.85 | 5000 | 0.2326 | 0.9014 | 0.9014 |
| 0.2322 | 12.32 | 5200 | 0.2400 | 0.8964 | 0.8964 |
| 0.2343 | 12.8 | 5400 | 0.2410 | 0.8986 | 0.8986 |
| 0.2376 | 13.27 | 5600 | 0.2354 | 0.8974 | 0.8974 |
| 0.2298 | 13.74 | 5800 | 0.2317 | 0.8995 | 0.8996 |
| 0.2307 | 14.22 | 6000 | 0.2321 | 0.9026 | 0.9026 |
| 0.2265 | 14.69 | 6200 | 0.2314 | 0.9033 | 0.9033 |
| 0.231 | 15.17 | 6400 | 0.2351 | 0.8974 | 0.8974 |
| 0.2314 | 15.64 | 6600 | 0.2340 | 0.9002 | 0.9002 |
| 0.2238 | 16.11 | 6800 | 0.2328 | 0.9011 | 0.9011 |
| 0.2234 | 16.59 | 7000 | 0.2332 | 0.8992 | 0.8992 |
| 0.2255 | 17.06 | 7200 | 0.2301 | 0.9001 | 0.9001 |
| 0.2232 | 17.54 | 7400 | 0.2314 | 0.9018 | 0.9019 |
| 0.2228 | 18.01 | 7600 | 0.2298 | 0.9027 | 0.9027 |
| 0.2196 | 18.48 | 7800 | 0.2374 | 0.8990 | 0.8990 |
| 0.2255 | 18.96 | 8000 | 0.2281 | 0.9035 | 0.9035 |
| 0.2188 | 19.43 | 8200 | 0.2303 | 0.9016 | 0.9016 |
| 0.2188 | 19.91 | 8400 | 0.2309 | 0.9020 | 0.9020 |
| 0.223 | 20.38 | 8600 | 0.2293 | 0.9012 | 0.9013 |
| 0.2183 | 20.85 | 8800 | 0.2316 | 0.9012 | 0.9013 |
| 0.2194 | 21.33 | 9000 | 0.2299 | 0.9017 | 0.9017 |
| 0.2175 | 21.8 | 9200 | 0.2308 | 0.9024 | 0.9024 |
| 0.2228 | 22.27 | 9400 | 0.2284 | 0.9027 | 0.9027 |
| 0.214 | 22.75 | 9600 | 0.2297 | 0.9018 | 0.9019 |
| 0.2182 | 23.22 | 9800 | 0.2298 | 0.9029 | 0.9029 |
| 0.2183 | 23.7 | 10000 | 0.2300 | 0.9027 | 0.9027 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:48:55+00:00 |
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## 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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| {"library_name": "transformers", "tags": []} | swj0419/hp_all_STEP0000040 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:49:57+00:00 |
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. -->
# GUE_mouse_4-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6011
- F1 Score: 0.6739
- Accuracy: 0.6745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6645 | 1.69 | 200 | 0.6442 | 0.6188 | 0.6251 |
| 0.6401 | 3.39 | 400 | 0.6242 | 0.6371 | 0.6389 |
| 0.6233 | 5.08 | 600 | 0.6153 | 0.6554 | 0.6553 |
| 0.6166 | 6.78 | 800 | 0.6090 | 0.6629 | 0.6628 |
| 0.6102 | 8.47 | 1000 | 0.6059 | 0.6633 | 0.6638 |
| 0.603 | 10.17 | 1200 | 0.6066 | 0.6606 | 0.6622 |
| 0.5969 | 11.86 | 1400 | 0.5961 | 0.6825 | 0.6824 |
| 0.5947 | 13.56 | 1600 | 0.5966 | 0.6744 | 0.6745 |
| 0.593 | 15.25 | 1800 | 0.5914 | 0.6809 | 0.6808 |
| 0.5878 | 16.95 | 2000 | 0.5894 | 0.6942 | 0.6941 |
| 0.5855 | 18.64 | 2200 | 0.5913 | 0.6795 | 0.6803 |
| 0.5855 | 20.34 | 2400 | 0.5864 | 0.6913 | 0.6914 |
| 0.5845 | 22.03 | 2600 | 0.5862 | 0.6898 | 0.6899 |
| 0.5797 | 23.73 | 2800 | 0.5839 | 0.6884 | 0.6883 |
| 0.578 | 25.42 | 3000 | 0.5847 | 0.6908 | 0.6909 |
| 0.5771 | 27.12 | 3200 | 0.6002 | 0.6552 | 0.6601 |
| 0.5788 | 28.81 | 3400 | 0.5861 | 0.6797 | 0.6803 |
| 0.5723 | 30.51 | 3600 | 0.5885 | 0.6805 | 0.6814 |
| 0.574 | 32.2 | 3800 | 0.5826 | 0.6889 | 0.6888 |
| 0.5729 | 33.9 | 4000 | 0.5884 | 0.6755 | 0.6766 |
| 0.5699 | 35.59 | 4200 | 0.5891 | 0.6749 | 0.6766 |
| 0.5733 | 37.29 | 4400 | 0.5829 | 0.6925 | 0.6925 |
| 0.5695 | 38.98 | 4600 | 0.5812 | 0.6936 | 0.6936 |
| 0.5646 | 40.68 | 4800 | 0.5845 | 0.6844 | 0.6851 |
| 0.5636 | 42.37 | 5000 | 0.5834 | 0.6871 | 0.6872 |
| 0.5662 | 44.07 | 5200 | 0.5818 | 0.6936 | 0.6936 |
| 0.5675 | 45.76 | 5400 | 0.5840 | 0.6862 | 0.6872 |
| 0.567 | 47.46 | 5600 | 0.5833 | 0.6920 | 0.6920 |
| 0.5701 | 49.15 | 5800 | 0.5860 | 0.6756 | 0.6776 |
| 0.5645 | 50.85 | 6000 | 0.5798 | 0.6869 | 0.6872 |
| 0.5639 | 52.54 | 6200 | 0.5845 | 0.6799 | 0.6808 |
| 0.5624 | 54.24 | 6400 | 0.5798 | 0.6941 | 0.6941 |
| 0.5656 | 55.93 | 6600 | 0.5785 | 0.6969 | 0.6968 |
| 0.5645 | 57.63 | 6800 | 0.5783 | 0.6952 | 0.6952 |
| 0.563 | 59.32 | 7000 | 0.5829 | 0.6842 | 0.6851 |
| 0.5612 | 61.02 | 7200 | 0.5776 | 0.6947 | 0.6946 |
| 0.5629 | 62.71 | 7400 | 0.5806 | 0.6933 | 0.6936 |
| 0.5614 | 64.41 | 7600 | 0.5800 | 0.6936 | 0.6936 |
| 0.5609 | 66.1 | 7800 | 0.5798 | 0.6950 | 0.6952 |
| 0.5601 | 67.8 | 8000 | 0.5780 | 0.6963 | 0.6962 |
| 0.5588 | 69.49 | 8200 | 0.5805 | 0.6924 | 0.6925 |
| 0.5574 | 71.19 | 8400 | 0.5796 | 0.6924 | 0.6925 |
| 0.5598 | 72.88 | 8600 | 0.5779 | 0.6969 | 0.6968 |
| 0.5559 | 74.58 | 8800 | 0.5798 | 0.6919 | 0.6920 |
| 0.5603 | 76.27 | 9000 | 0.5791 | 0.6925 | 0.6925 |
| 0.5579 | 77.97 | 9200 | 0.5790 | 0.6909 | 0.6909 |
| 0.556 | 79.66 | 9400 | 0.5787 | 0.6931 | 0.6930 |
| 0.5595 | 81.36 | 9600 | 0.5788 | 0.6920 | 0.6920 |
| 0.5581 | 83.05 | 9800 | 0.5787 | 0.6925 | 0.6925 |
| 0.5571 | 84.75 | 10000 | 0.5790 | 0.6925 | 0.6925 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:50:29+00:00 |
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. -->
# GUE_mouse_4-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6171
- F1 Score: 0.6829
- Accuracy: 0.6830
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6535 | 1.69 | 200 | 0.6284 | 0.6305 | 0.6330 |
| 0.6223 | 3.39 | 400 | 0.6105 | 0.6579 | 0.6591 |
| 0.6023 | 5.08 | 600 | 0.5991 | 0.6740 | 0.6745 |
| 0.5923 | 6.78 | 800 | 0.5908 | 0.6829 | 0.6835 |
| 0.5875 | 8.47 | 1000 | 0.5855 | 0.6836 | 0.6835 |
| 0.5815 | 10.17 | 1200 | 0.5897 | 0.6752 | 0.6766 |
| 0.576 | 11.86 | 1400 | 0.5817 | 0.6915 | 0.6920 |
| 0.574 | 13.56 | 1600 | 0.5799 | 0.6921 | 0.6920 |
| 0.5694 | 15.25 | 1800 | 0.5817 | 0.7023 | 0.7037 |
| 0.566 | 16.95 | 2000 | 0.5768 | 0.6915 | 0.6920 |
| 0.5617 | 18.64 | 2200 | 0.5776 | 0.6923 | 0.6925 |
| 0.56 | 20.34 | 2400 | 0.5748 | 0.7059 | 0.7058 |
| 0.5595 | 22.03 | 2600 | 0.5940 | 0.6750 | 0.6792 |
| 0.5535 | 23.73 | 2800 | 0.5723 | 0.7017 | 0.7015 |
| 0.5511 | 25.42 | 3000 | 0.5741 | 0.7008 | 0.7010 |
| 0.5486 | 27.12 | 3200 | 0.5916 | 0.6752 | 0.6792 |
| 0.5489 | 28.81 | 3400 | 0.5721 | 0.7043 | 0.7042 |
| 0.542 | 30.51 | 3600 | 0.5723 | 0.7032 | 0.7031 |
| 0.5438 | 32.2 | 3800 | 0.5729 | 0.7016 | 0.7015 |
| 0.5383 | 33.9 | 4000 | 0.5808 | 0.6904 | 0.6914 |
| 0.5344 | 35.59 | 4200 | 0.5862 | 0.6839 | 0.6867 |
| 0.5358 | 37.29 | 4400 | 0.5750 | 0.7047 | 0.7047 |
| 0.5334 | 38.98 | 4600 | 0.5685 | 0.7032 | 0.7031 |
| 0.5244 | 40.68 | 4800 | 0.5772 | 0.7050 | 0.7058 |
| 0.5227 | 42.37 | 5000 | 0.5742 | 0.7069 | 0.7069 |
| 0.5249 | 44.07 | 5200 | 0.5741 | 0.7070 | 0.7069 |
| 0.524 | 45.76 | 5400 | 0.5694 | 0.7117 | 0.7116 |
| 0.5217 | 47.46 | 5600 | 0.5760 | 0.7127 | 0.7127 |
| 0.5237 | 49.15 | 5800 | 0.5696 | 0.7050 | 0.7053 |
| 0.5171 | 50.85 | 6000 | 0.5713 | 0.7077 | 0.7079 |
| 0.5173 | 52.54 | 6200 | 0.5708 | 0.7111 | 0.7111 |
| 0.5126 | 54.24 | 6400 | 0.5731 | 0.7106 | 0.7106 |
| 0.5143 | 55.93 | 6600 | 0.5712 | 0.7112 | 0.7111 |
| 0.5146 | 57.63 | 6800 | 0.5705 | 0.7162 | 0.7164 |
| 0.5071 | 59.32 | 7000 | 0.5744 | 0.7137 | 0.7138 |
| 0.507 | 61.02 | 7200 | 0.5708 | 0.7133 | 0.7132 |
| 0.5057 | 62.71 | 7400 | 0.5675 | 0.7149 | 0.7148 |
| 0.5059 | 64.41 | 7600 | 0.5719 | 0.7112 | 0.7111 |
| 0.5017 | 66.1 | 7800 | 0.5708 | 0.7149 | 0.7148 |
| 0.5033 | 67.8 | 8000 | 0.5740 | 0.7164 | 0.7169 |
| 0.501 | 69.49 | 8200 | 0.5729 | 0.7168 | 0.7169 |
| 0.4989 | 71.19 | 8400 | 0.5722 | 0.7101 | 0.7100 |
| 0.501 | 72.88 | 8600 | 0.5726 | 0.7126 | 0.7127 |
| 0.4974 | 74.58 | 8800 | 0.5719 | 0.7111 | 0.7111 |
| 0.4996 | 76.27 | 9000 | 0.5721 | 0.7133 | 0.7132 |
| 0.4969 | 77.97 | 9200 | 0.5719 | 0.7106 | 0.7106 |
| 0.4957 | 79.66 | 9400 | 0.5733 | 0.7131 | 0.7132 |
| 0.4961 | 81.36 | 9600 | 0.5729 | 0.7106 | 0.7106 |
| 0.4951 | 83.05 | 9800 | 0.5729 | 0.7085 | 0.7084 |
| 0.4945 | 84.75 | 10000 | 0.5731 | 0.7090 | 0.7090 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:52:01+00:00 |
audio-classification | 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. -->
# distilhubert-finetuned-stutteringdetection
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the stuttering dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5717
- Accuracy: 0.9024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8357 | 1.0 | 92 | 0.7812 | 0.8659 |
| 0.2951 | 2.0 | 184 | 0.3680 | 0.8902 |
| 0.097 | 3.0 | 276 | 0.4000 | 0.8659 |
| 0.0872 | 4.0 | 368 | 0.3953 | 0.9024 |
| 0.4557 | 5.0 | 460 | 0.4904 | 0.9024 |
| 0.0368 | 6.0 | 552 | 0.4972 | 0.9024 |
| 0.0074 | 7.0 | 644 | 0.5408 | 0.9146 |
| 0.0039 | 8.0 | 736 | 0.5460 | 0.9024 |
| 0.0036 | 9.0 | 828 | 0.5684 | 0.9024 |
| 0.0035 | 10.0 | 920 | 0.5717 | 0.9024 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["HareemFatima/stutteringdetection"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-stutteringdetection", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "stuttering", "type": "HareemFatima/stutteringdetection"}, "metrics": [{"type": "accuracy", "value": 0.9024390243902439, "name": "Accuracy"}]}]}]} | HareemFatima/distilhubert-finetuned-stutterdetection | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:HareemFatima/stutteringdetection",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2024-04-27T08:52:48+00:00 |
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. -->
# GUE_mouse_4-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6255
- F1 Score: 0.6676
- Accuracy: 0.6681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6442 | 1.69 | 200 | 0.6142 | 0.6502 | 0.6506 |
| 0.6097 | 3.39 | 400 | 0.6025 | 0.6643 | 0.6660 |
| 0.5887 | 5.08 | 600 | 0.5891 | 0.6941 | 0.6941 |
| 0.5769 | 6.78 | 800 | 0.5898 | 0.6858 | 0.6888 |
| 0.5708 | 8.47 | 1000 | 0.5811 | 0.6832 | 0.6840 |
| 0.5602 | 10.17 | 1200 | 0.5839 | 0.6834 | 0.6845 |
| 0.5498 | 11.86 | 1400 | 0.5770 | 0.6938 | 0.6946 |
| 0.5427 | 13.56 | 1600 | 0.5732 | 0.6972 | 0.6973 |
| 0.532 | 15.25 | 1800 | 0.5853 | 0.7089 | 0.7100 |
| 0.5246 | 16.95 | 2000 | 0.5735 | 0.7015 | 0.7026 |
| 0.5147 | 18.64 | 2200 | 0.5812 | 0.6958 | 0.6968 |
| 0.5087 | 20.34 | 2400 | 0.5806 | 0.7043 | 0.7042 |
| 0.5004 | 22.03 | 2600 | 0.6028 | 0.6901 | 0.6920 |
| 0.4897 | 23.73 | 2800 | 0.5830 | 0.7053 | 0.7053 |
| 0.4826 | 25.42 | 3000 | 0.5912 | 0.7023 | 0.7037 |
| 0.4753 | 27.12 | 3200 | 0.6060 | 0.6832 | 0.6845 |
| 0.4712 | 28.81 | 3400 | 0.6045 | 0.6966 | 0.6973 |
| 0.4616 | 30.51 | 3600 | 0.6023 | 0.7047 | 0.7047 |
| 0.4613 | 32.2 | 3800 | 0.6148 | 0.7064 | 0.7063 |
| 0.452 | 33.9 | 4000 | 0.6174 | 0.7010 | 0.7010 |
| 0.4426 | 35.59 | 4200 | 0.6314 | 0.6923 | 0.6936 |
| 0.4417 | 37.29 | 4400 | 0.6398 | 0.7009 | 0.7010 |
| 0.4338 | 38.98 | 4600 | 0.6250 | 0.6990 | 0.6989 |
| 0.4208 | 40.68 | 4800 | 0.6326 | 0.6934 | 0.6936 |
| 0.4207 | 42.37 | 5000 | 0.6399 | 0.6974 | 0.6973 |
| 0.4196 | 44.07 | 5200 | 0.6558 | 0.6920 | 0.6920 |
| 0.4148 | 45.76 | 5400 | 0.6534 | 0.6995 | 0.6994 |
| 0.4105 | 47.46 | 5600 | 0.6697 | 0.6937 | 0.6936 |
| 0.4052 | 49.15 | 5800 | 0.6619 | 0.6894 | 0.6893 |
| 0.4024 | 50.85 | 6000 | 0.6680 | 0.6872 | 0.6872 |
| 0.3997 | 52.54 | 6200 | 0.6687 | 0.6945 | 0.6946 |
| 0.3922 | 54.24 | 6400 | 0.6793 | 0.6915 | 0.6920 |
| 0.3899 | 55.93 | 6600 | 0.6936 | 0.6889 | 0.6888 |
| 0.3878 | 57.63 | 6800 | 0.6648 | 0.6931 | 0.6930 |
| 0.3831 | 59.32 | 7000 | 0.6911 | 0.6930 | 0.6930 |
| 0.3774 | 61.02 | 7200 | 0.6868 | 0.6937 | 0.6936 |
| 0.3768 | 62.71 | 7400 | 0.6846 | 0.6883 | 0.6883 |
| 0.3747 | 64.41 | 7600 | 0.7037 | 0.6926 | 0.6925 |
| 0.3709 | 66.1 | 7800 | 0.7056 | 0.6868 | 0.6867 |
| 0.3676 | 67.8 | 8000 | 0.7123 | 0.6868 | 0.6872 |
| 0.3688 | 69.49 | 8200 | 0.7095 | 0.6904 | 0.6904 |
| 0.3665 | 71.19 | 8400 | 0.7124 | 0.6852 | 0.6851 |
| 0.3639 | 72.88 | 8600 | 0.7155 | 0.6867 | 0.6867 |
| 0.3599 | 74.58 | 8800 | 0.7138 | 0.6883 | 0.6883 |
| 0.3638 | 76.27 | 9000 | 0.7179 | 0.6888 | 0.6888 |
| 0.3555 | 77.97 | 9200 | 0.7185 | 0.6905 | 0.6904 |
| 0.3558 | 79.66 | 9400 | 0.7219 | 0.6910 | 0.6909 |
| 0.3562 | 81.36 | 9600 | 0.7225 | 0.6914 | 0.6914 |
| 0.3503 | 83.05 | 9800 | 0.7262 | 0.6899 | 0.6899 |
| 0.3538 | 84.75 | 10000 | 0.7271 | 0.6862 | 0.6861 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:53:29+00:00 |
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. -->
# GUE_mouse_3-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6449
- F1 Score: 0.8032
- Accuracy: 0.8033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6227 | 13.33 | 200 | 0.5259 | 0.7343 | 0.7364 |
| 0.5385 | 26.67 | 400 | 0.4620 | 0.7774 | 0.7782 |
| 0.4836 | 40.0 | 600 | 0.4452 | 0.8026 | 0.8033 |
| 0.436 | 53.33 | 800 | 0.4247 | 0.8030 | 0.8033 |
| 0.4031 | 66.67 | 1000 | 0.4354 | 0.8147 | 0.8159 |
| 0.3797 | 80.0 | 1200 | 0.4339 | 0.7988 | 0.7992 |
| 0.3566 | 93.33 | 1400 | 0.4335 | 0.8031 | 0.8033 |
| 0.334 | 106.67 | 1600 | 0.4571 | 0.8069 | 0.8075 |
| 0.3176 | 120.0 | 1800 | 0.4663 | 0.8026 | 0.8033 |
| 0.3078 | 133.33 | 2000 | 0.4810 | 0.8065 | 0.8075 |
| 0.2979 | 146.67 | 2200 | 0.4668 | 0.8149 | 0.8159 |
| 0.2921 | 160.0 | 2400 | 0.4999 | 0.7969 | 0.7992 |
| 0.2757 | 173.33 | 2600 | 0.4878 | 0.8108 | 0.8117 |
| 0.264 | 186.67 | 2800 | 0.4892 | 0.8112 | 0.8117 |
| 0.2574 | 200.0 | 3000 | 0.5113 | 0.8026 | 0.8033 |
| 0.2526 | 213.33 | 3200 | 0.4938 | 0.8114 | 0.8117 |
| 0.2447 | 226.67 | 3400 | 0.5226 | 0.8067 | 0.8075 |
| 0.2377 | 240.0 | 3600 | 0.5326 | 0.8071 | 0.8075 |
| 0.2274 | 253.33 | 3800 | 0.5275 | 0.8115 | 0.8117 |
| 0.2185 | 266.67 | 4000 | 0.5259 | 0.8115 | 0.8117 |
| 0.2171 | 280.0 | 4200 | 0.5551 | 0.8026 | 0.8033 |
| 0.2086 | 293.33 | 4400 | 0.5611 | 0.8067 | 0.8075 |
| 0.2075 | 306.67 | 4600 | 0.5746 | 0.8108 | 0.8117 |
| 0.203 | 320.0 | 4800 | 0.5510 | 0.8028 | 0.8033 |
| 0.1948 | 333.33 | 5000 | 0.5798 | 0.7942 | 0.7950 |
| 0.1945 | 346.67 | 5200 | 0.5596 | 0.8028 | 0.8033 |
| 0.1894 | 360.0 | 5400 | 0.5673 | 0.8030 | 0.8033 |
| 0.1858 | 373.33 | 5600 | 0.5887 | 0.7907 | 0.7908 |
| 0.1775 | 386.67 | 5800 | 0.6021 | 0.7944 | 0.7950 |
| 0.1806 | 400.0 | 6000 | 0.5913 | 0.7903 | 0.7908 |
| 0.1743 | 413.33 | 6200 | 0.5862 | 0.7988 | 0.7992 |
| 0.1738 | 426.67 | 6400 | 0.5910 | 0.7988 | 0.7992 |
| 0.1686 | 440.0 | 6600 | 0.5969 | 0.7988 | 0.7992 |
| 0.1692 | 453.33 | 6800 | 0.6030 | 0.8030 | 0.8033 |
| 0.1637 | 466.67 | 7000 | 0.6082 | 0.7991 | 0.7992 |
| 0.1599 | 480.0 | 7200 | 0.6129 | 0.7865 | 0.7866 |
| 0.1592 | 493.33 | 7400 | 0.6125 | 0.8033 | 0.8033 |
| 0.1579 | 506.67 | 7600 | 0.6296 | 0.8073 | 0.8075 |
| 0.158 | 520.0 | 7800 | 0.6281 | 0.8030 | 0.8033 |
| 0.1551 | 533.33 | 8000 | 0.6195 | 0.8116 | 0.8117 |
| 0.1567 | 546.67 | 8200 | 0.6295 | 0.8155 | 0.8159 |
| 0.154 | 560.0 | 8400 | 0.6250 | 0.8115 | 0.8117 |
| 0.1517 | 573.33 | 8600 | 0.6295 | 0.8073 | 0.8075 |
| 0.1529 | 586.67 | 8800 | 0.6249 | 0.8199 | 0.8201 |
| 0.1509 | 600.0 | 9000 | 0.6235 | 0.8033 | 0.8033 |
| 0.1486 | 613.33 | 9200 | 0.6299 | 0.8115 | 0.8117 |
| 0.1487 | 626.67 | 9400 | 0.6317 | 0.8032 | 0.8033 |
| 0.1502 | 640.0 | 9600 | 0.6266 | 0.8073 | 0.8075 |
| 0.1485 | 653.33 | 9800 | 0.6304 | 0.8115 | 0.8117 |
| 0.1456 | 666.67 | 10000 | 0.6292 | 0.8073 | 0.8075 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:53:30+00:00 |
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. -->
# GUE_mouse_3-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7940
- F1 Score: 0.8447
- Accuracy: 0.8452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5818 | 13.33 | 200 | 0.4453 | 0.7948 | 0.7950 |
| 0.4301 | 26.67 | 400 | 0.4297 | 0.8106 | 0.8117 |
| 0.3549 | 40.0 | 600 | 0.4200 | 0.8200 | 0.8201 |
| 0.2973 | 53.33 | 800 | 0.4637 | 0.8272 | 0.8285 |
| 0.2662 | 66.67 | 1000 | 0.5280 | 0.8046 | 0.8075 |
| 0.2336 | 80.0 | 1200 | 0.5160 | 0.8153 | 0.8159 |
| 0.2145 | 93.33 | 1400 | 0.5109 | 0.8281 | 0.8285 |
| 0.1911 | 106.67 | 1600 | 0.5495 | 0.8197 | 0.8201 |
| 0.1777 | 120.0 | 1800 | 0.5476 | 0.7987 | 0.7992 |
| 0.1646 | 133.33 | 2000 | 0.6371 | 0.7880 | 0.7908 |
| 0.1505 | 146.67 | 2200 | 0.5463 | 0.8108 | 0.8117 |
| 0.141 | 160.0 | 2400 | 0.5784 | 0.8149 | 0.8159 |
| 0.1268 | 173.33 | 2600 | 0.5710 | 0.8408 | 0.8410 |
| 0.1155 | 186.67 | 2800 | 0.5945 | 0.8367 | 0.8368 |
| 0.1109 | 200.0 | 3000 | 0.5784 | 0.8410 | 0.8410 |
| 0.1061 | 213.33 | 3200 | 0.5774 | 0.8534 | 0.8536 |
| 0.0977 | 226.67 | 3400 | 0.6396 | 0.8449 | 0.8452 |
| 0.097 | 240.0 | 3600 | 0.6888 | 0.8284 | 0.8285 |
| 0.0888 | 253.33 | 3800 | 0.6826 | 0.8450 | 0.8452 |
| 0.0771 | 266.67 | 4000 | 0.6878 | 0.8492 | 0.8494 |
| 0.0806 | 280.0 | 4200 | 0.6557 | 0.8408 | 0.8410 |
| 0.0765 | 293.33 | 4400 | 0.6868 | 0.8405 | 0.8410 |
| 0.0699 | 306.67 | 4600 | 0.7310 | 0.8493 | 0.8494 |
| 0.0679 | 320.0 | 4800 | 0.6940 | 0.8410 | 0.8410 |
| 0.0653 | 333.33 | 5000 | 0.7225 | 0.8491 | 0.8494 |
| 0.0615 | 346.67 | 5200 | 0.7379 | 0.8491 | 0.8494 |
| 0.0607 | 360.0 | 5400 | 0.7678 | 0.8322 | 0.8326 |
| 0.0558 | 373.33 | 5600 | 0.7789 | 0.8367 | 0.8368 |
| 0.0536 | 386.67 | 5800 | 0.8346 | 0.8446 | 0.8452 |
| 0.0549 | 400.0 | 6000 | 0.7734 | 0.8408 | 0.8410 |
| 0.0502 | 413.33 | 6200 | 0.7813 | 0.8493 | 0.8494 |
| 0.0557 | 426.67 | 6400 | 0.7285 | 0.8575 | 0.8577 |
| 0.0453 | 440.0 | 6600 | 0.8123 | 0.8450 | 0.8452 |
| 0.0515 | 453.33 | 6800 | 0.7763 | 0.8575 | 0.8577 |
| 0.0446 | 466.67 | 7000 | 0.7729 | 0.8493 | 0.8494 |
| 0.0458 | 480.0 | 7200 | 0.7779 | 0.8450 | 0.8452 |
| 0.0439 | 493.33 | 7400 | 0.7898 | 0.8368 | 0.8368 |
| 0.0461 | 506.67 | 7600 | 0.8091 | 0.8450 | 0.8452 |
| 0.0449 | 520.0 | 7800 | 0.8044 | 0.8491 | 0.8494 |
| 0.0444 | 533.33 | 8000 | 0.7947 | 0.8408 | 0.8410 |
| 0.0403 | 546.67 | 8200 | 0.8372 | 0.8449 | 0.8452 |
| 0.0429 | 560.0 | 8400 | 0.8311 | 0.8449 | 0.8452 |
| 0.0423 | 573.33 | 8600 | 0.8076 | 0.8451 | 0.8452 |
| 0.0406 | 586.67 | 8800 | 0.8227 | 0.8533 | 0.8536 |
| 0.0397 | 600.0 | 9000 | 0.8339 | 0.8491 | 0.8494 |
| 0.0374 | 613.33 | 9200 | 0.8464 | 0.8449 | 0.8452 |
| 0.04 | 626.67 | 9400 | 0.8335 | 0.8491 | 0.8494 |
| 0.0407 | 640.0 | 9600 | 0.8229 | 0.8450 | 0.8452 |
| 0.0377 | 653.33 | 9800 | 0.8410 | 0.8449 | 0.8452 |
| 0.0386 | 666.67 | 10000 | 0.8276 | 0.8449 | 0.8452 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:53:59+00:00 |
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. -->
# GUE_mouse_3-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8993
- F1 Score: 0.8409
- Accuracy: 0.8410
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5266 | 13.33 | 200 | 0.3982 | 0.8242 | 0.8243 |
| 0.35 | 26.67 | 400 | 0.4361 | 0.8192 | 0.8201 |
| 0.2628 | 40.0 | 600 | 0.4681 | 0.8451 | 0.8452 |
| 0.2005 | 53.33 | 800 | 0.5248 | 0.8278 | 0.8285 |
| 0.1675 | 66.67 | 1000 | 0.5468 | 0.8322 | 0.8326 |
| 0.1296 | 80.0 | 1200 | 0.5736 | 0.8200 | 0.8201 |
| 0.1139 | 93.33 | 1400 | 0.6281 | 0.8242 | 0.8243 |
| 0.0923 | 106.67 | 1600 | 0.7864 | 0.8226 | 0.8243 |
| 0.0825 | 120.0 | 1800 | 0.6495 | 0.8282 | 0.8285 |
| 0.0699 | 133.33 | 2000 | 0.7029 | 0.8278 | 0.8285 |
| 0.0566 | 146.67 | 2200 | 0.7612 | 0.8240 | 0.8243 |
| 0.055 | 160.0 | 2400 | 0.7407 | 0.8274 | 0.8285 |
| 0.0488 | 173.33 | 2600 | 0.7233 | 0.8241 | 0.8243 |
| 0.042 | 186.67 | 2800 | 0.7600 | 0.8493 | 0.8494 |
| 0.0406 | 200.0 | 3000 | 0.7998 | 0.8159 | 0.8159 |
| 0.0363 | 213.33 | 3200 | 0.7662 | 0.8368 | 0.8368 |
| 0.0335 | 226.67 | 3400 | 0.8254 | 0.8243 | 0.8243 |
| 0.0354 | 240.0 | 3600 | 0.8436 | 0.8241 | 0.8243 |
| 0.0307 | 253.33 | 3800 | 0.8122 | 0.8368 | 0.8368 |
| 0.023 | 266.67 | 4000 | 0.8677 | 0.8326 | 0.8326 |
| 0.0255 | 280.0 | 4200 | 0.9085 | 0.8159 | 0.8159 |
| 0.0259 | 293.33 | 4400 | 0.8586 | 0.8199 | 0.8201 |
| 0.0259 | 306.67 | 4600 | 0.8769 | 0.8159 | 0.8159 |
| 0.0223 | 320.0 | 4800 | 0.8891 | 0.8367 | 0.8368 |
| 0.0205 | 333.33 | 5000 | 0.9187 | 0.8324 | 0.8326 |
| 0.0173 | 346.67 | 5200 | 0.9073 | 0.8325 | 0.8326 |
| 0.0193 | 360.0 | 5400 | 0.8803 | 0.8365 | 0.8368 |
| 0.0168 | 373.33 | 5600 | 0.8920 | 0.8410 | 0.8410 |
| 0.0153 | 386.67 | 5800 | 0.9922 | 0.8282 | 0.8285 |
| 0.016 | 400.0 | 6000 | 0.9730 | 0.8116 | 0.8117 |
| 0.0156 | 413.33 | 6200 | 1.0085 | 0.8156 | 0.8159 |
| 0.0155 | 426.67 | 6400 | 0.9479 | 0.8114 | 0.8117 |
| 0.0147 | 440.0 | 6600 | 0.9176 | 0.8114 | 0.8117 |
| 0.016 | 453.33 | 6800 | 0.8997 | 0.8200 | 0.8201 |
| 0.013 | 466.67 | 7000 | 0.9789 | 0.8326 | 0.8326 |
| 0.0125 | 480.0 | 7200 | 0.9769 | 0.8326 | 0.8326 |
| 0.0126 | 493.33 | 7400 | 0.9434 | 0.8326 | 0.8326 |
| 0.0108 | 506.67 | 7600 | 1.0108 | 0.8285 | 0.8285 |
| 0.0128 | 520.0 | 7800 | 0.9395 | 0.8283 | 0.8285 |
| 0.0118 | 533.33 | 8000 | 0.9746 | 0.8240 | 0.8243 |
| 0.0092 | 546.67 | 8200 | 1.0324 | 0.8241 | 0.8243 |
| 0.0123 | 560.0 | 8400 | 0.9384 | 0.8367 | 0.8368 |
| 0.01 | 573.33 | 8600 | 0.9883 | 0.8325 | 0.8326 |
| 0.0106 | 586.67 | 8800 | 1.0023 | 0.8325 | 0.8326 |
| 0.0107 | 600.0 | 9000 | 0.9692 | 0.8240 | 0.8243 |
| 0.0083 | 613.33 | 9200 | 0.9892 | 0.8325 | 0.8326 |
| 0.0083 | 626.67 | 9400 | 0.9902 | 0.8367 | 0.8368 |
| 0.011 | 640.0 | 9600 | 0.9960 | 0.8368 | 0.8368 |
| 0.0082 | 653.33 | 9800 | 0.9793 | 0.8409 | 0.8410 |
| 0.0105 | 666.67 | 10000 | 0.9793 | 0.8409 | 0.8410 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:54:45+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Andro9669/t5-ner | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:55:52+00:00 |
null | transformers |
# Model Card for Model ID
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## 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.
- **Developed by:** [More Information Needed]
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### Recommendations
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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
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[More Information Needed]
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## Model Examination [optional]
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[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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | siacus/Llama-3-8B-tweets-10 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T08:56:06+00:00 |
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. -->
# GUE_mouse_2-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4210
- F1 Score: 0.8567
- Accuracy: 0.8567
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4428 | 9.52 | 200 | 0.3414 | 0.8475 | 0.8476 |
| 0.3343 | 19.05 | 400 | 0.3365 | 0.8407 | 0.8415 |
| 0.2965 | 28.57 | 600 | 0.3178 | 0.8688 | 0.8689 |
| 0.2711 | 38.1 | 800 | 0.3228 | 0.8597 | 0.8598 |
| 0.2544 | 47.62 | 1000 | 0.3130 | 0.8506 | 0.8506 |
| 0.2381 | 57.14 | 1200 | 0.3218 | 0.8596 | 0.8598 |
| 0.2263 | 66.67 | 1400 | 0.3147 | 0.8567 | 0.8567 |
| 0.2167 | 76.19 | 1600 | 0.3256 | 0.8564 | 0.8567 |
| 0.2062 | 85.71 | 1800 | 0.3272 | 0.8564 | 0.8567 |
| 0.197 | 95.24 | 2000 | 0.3177 | 0.8628 | 0.8628 |
| 0.1882 | 104.76 | 2200 | 0.3257 | 0.8658 | 0.8659 |
| 0.1802 | 114.29 | 2400 | 0.3357 | 0.8659 | 0.8659 |
| 0.1786 | 123.81 | 2600 | 0.3291 | 0.8658 | 0.8659 |
| 0.1733 | 133.33 | 2800 | 0.3512 | 0.8474 | 0.8476 |
| 0.1663 | 142.86 | 3000 | 0.3306 | 0.8628 | 0.8628 |
| 0.1619 | 152.38 | 3200 | 0.3402 | 0.8536 | 0.8537 |
| 0.1572 | 161.9 | 3400 | 0.3509 | 0.8534 | 0.8537 |
| 0.1508 | 171.43 | 3600 | 0.3477 | 0.8627 | 0.8628 |
| 0.1502 | 180.95 | 3800 | 0.3449 | 0.8658 | 0.8659 |
| 0.1432 | 190.48 | 4000 | 0.3475 | 0.8597 | 0.8598 |
| 0.1382 | 200.0 | 4200 | 0.3617 | 0.8657 | 0.8659 |
| 0.1379 | 209.52 | 4400 | 0.3404 | 0.8719 | 0.8720 |
| 0.1335 | 219.05 | 4600 | 0.3454 | 0.8719 | 0.8720 |
| 0.131 | 228.57 | 4800 | 0.3593 | 0.8688 | 0.8689 |
| 0.1305 | 238.1 | 5000 | 0.3712 | 0.8718 | 0.8720 |
| 0.1264 | 247.62 | 5200 | 0.3574 | 0.8658 | 0.8659 |
| 0.1236 | 257.14 | 5400 | 0.3616 | 0.8658 | 0.8659 |
| 0.1228 | 266.67 | 5600 | 0.3769 | 0.8626 | 0.8628 |
| 0.1209 | 276.19 | 5800 | 0.3600 | 0.8658 | 0.8659 |
| 0.1164 | 285.71 | 6000 | 0.3763 | 0.8626 | 0.8628 |
| 0.1153 | 295.24 | 6200 | 0.3627 | 0.8627 | 0.8628 |
| 0.1143 | 304.76 | 6400 | 0.3730 | 0.8627 | 0.8628 |
| 0.1104 | 314.29 | 6600 | 0.3826 | 0.8719 | 0.8720 |
| 0.113 | 323.81 | 6800 | 0.3738 | 0.8627 | 0.8628 |
| 0.107 | 333.33 | 7000 | 0.3782 | 0.8718 | 0.8720 |
| 0.1102 | 342.86 | 7200 | 0.3708 | 0.8749 | 0.875 |
| 0.1038 | 352.38 | 7400 | 0.3719 | 0.8749 | 0.875 |
| 0.1064 | 361.9 | 7600 | 0.3751 | 0.8688 | 0.8689 |
| 0.1061 | 371.43 | 7800 | 0.3803 | 0.8718 | 0.8720 |
| 0.1051 | 380.95 | 8000 | 0.3799 | 0.8718 | 0.8720 |
| 0.1035 | 390.48 | 8200 | 0.3796 | 0.8718 | 0.8720 |
| 0.1017 | 400.0 | 8400 | 0.3828 | 0.8718 | 0.8720 |
| 0.1003 | 409.52 | 8600 | 0.3778 | 0.8749 | 0.875 |
| 0.0996 | 419.05 | 8800 | 0.3786 | 0.8750 | 0.875 |
| 0.1005 | 428.57 | 9000 | 0.3830 | 0.8718 | 0.8720 |
| 0.101 | 438.1 | 9200 | 0.3836 | 0.8718 | 0.8720 |
| 0.0961 | 447.62 | 9400 | 0.3840 | 0.8718 | 0.8720 |
| 0.0971 | 457.14 | 9600 | 0.3813 | 0.8749 | 0.875 |
| 0.0968 | 466.67 | 9800 | 0.3824 | 0.8749 | 0.875 |
| 0.0975 | 476.19 | 10000 | 0.3843 | 0.8718 | 0.8720 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:56:10+00:00 |
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. -->
# GUE_mouse_2-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4164
- F1 Score: 0.8719
- Accuracy: 0.8720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3997 | 9.52 | 200 | 0.3155 | 0.8594 | 0.8598 |
| 0.2795 | 19.05 | 400 | 0.2910 | 0.8779 | 0.8780 |
| 0.2392 | 28.57 | 600 | 0.2782 | 0.8841 | 0.8841 |
| 0.2094 | 38.1 | 800 | 0.2874 | 0.8750 | 0.875 |
| 0.1877 | 47.62 | 1000 | 0.2986 | 0.8749 | 0.875 |
| 0.1683 | 57.14 | 1200 | 0.3442 | 0.8713 | 0.8720 |
| 0.1554 | 66.67 | 1400 | 0.3778 | 0.8744 | 0.875 |
| 0.1464 | 76.19 | 1600 | 0.3928 | 0.8683 | 0.8689 |
| 0.1344 | 85.71 | 1800 | 0.3672 | 0.8930 | 0.8933 |
| 0.125 | 95.24 | 2000 | 0.3655 | 0.8838 | 0.8841 |
| 0.1126 | 104.76 | 2200 | 0.3630 | 0.8931 | 0.8933 |
| 0.1014 | 114.29 | 2400 | 0.4538 | 0.8930 | 0.8933 |
| 0.1011 | 123.81 | 2600 | 0.3930 | 0.8931 | 0.8933 |
| 0.0924 | 133.33 | 2800 | 0.4071 | 0.8899 | 0.8902 |
| 0.0862 | 142.86 | 3000 | 0.4078 | 0.8962 | 0.8963 |
| 0.08 | 152.38 | 3200 | 0.4487 | 0.8868 | 0.8872 |
| 0.0767 | 161.9 | 3400 | 0.5149 | 0.8806 | 0.8811 |
| 0.0709 | 171.43 | 3600 | 0.4059 | 0.8962 | 0.8963 |
| 0.069 | 180.95 | 3800 | 0.4635 | 0.8808 | 0.8811 |
| 0.0625 | 190.48 | 4000 | 0.4600 | 0.8901 | 0.8902 |
| 0.0573 | 200.0 | 4200 | 0.5042 | 0.8807 | 0.8811 |
| 0.0581 | 209.52 | 4400 | 0.4973 | 0.8776 | 0.8780 |
| 0.0581 | 219.05 | 4600 | 0.4788 | 0.8777 | 0.8780 |
| 0.054 | 228.57 | 4800 | 0.5444 | 0.8744 | 0.875 |
| 0.0545 | 238.1 | 5000 | 0.4845 | 0.8900 | 0.8902 |
| 0.0509 | 247.62 | 5200 | 0.4610 | 0.8932 | 0.8933 |
| 0.046 | 257.14 | 5400 | 0.5035 | 0.8962 | 0.8963 |
| 0.0447 | 266.67 | 5600 | 0.5513 | 0.8807 | 0.8811 |
| 0.0425 | 276.19 | 5800 | 0.5364 | 0.8870 | 0.8872 |
| 0.0425 | 285.71 | 6000 | 0.5192 | 0.8870 | 0.8872 |
| 0.0384 | 295.24 | 6200 | 0.5858 | 0.8869 | 0.8872 |
| 0.0401 | 304.76 | 6400 | 0.5853 | 0.8838 | 0.8841 |
| 0.0395 | 314.29 | 6600 | 0.5985 | 0.8807 | 0.8811 |
| 0.0392 | 323.81 | 6800 | 0.5633 | 0.8807 | 0.8811 |
| 0.0371 | 333.33 | 7000 | 0.5896 | 0.8745 | 0.875 |
| 0.0368 | 342.86 | 7200 | 0.5585 | 0.8838 | 0.8841 |
| 0.0358 | 352.38 | 7400 | 0.5254 | 0.8931 | 0.8933 |
| 0.0373 | 361.9 | 7600 | 0.5235 | 0.8870 | 0.8872 |
| 0.0346 | 371.43 | 7800 | 0.5312 | 0.8900 | 0.8902 |
| 0.0331 | 380.95 | 8000 | 0.6006 | 0.8807 | 0.8811 |
| 0.0304 | 390.48 | 8200 | 0.5686 | 0.8870 | 0.8872 |
| 0.0318 | 400.0 | 8400 | 0.5678 | 0.8869 | 0.8872 |
| 0.0308 | 409.52 | 8600 | 0.5983 | 0.8807 | 0.8811 |
| 0.0298 | 419.05 | 8800 | 0.5908 | 0.8807 | 0.8811 |
| 0.0315 | 428.57 | 9000 | 0.5852 | 0.8777 | 0.8780 |
| 0.0307 | 438.1 | 9200 | 0.5532 | 0.8808 | 0.8811 |
| 0.0288 | 447.62 | 9400 | 0.5655 | 0.8870 | 0.8872 |
| 0.0285 | 457.14 | 9600 | 0.5867 | 0.8808 | 0.8811 |
| 0.0294 | 466.67 | 9800 | 0.5711 | 0.8808 | 0.8811 |
| 0.0272 | 476.19 | 10000 | 0.5798 | 0.8808 | 0.8811 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:56:23+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | HusseinEid/Reinforce-Cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-27T08:57:20+00:00 |
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. -->
# GUE_mouse_2-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3136
- F1 Score: 0.8628
- Accuracy: 0.8628
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3675 | 9.52 | 200 | 0.2939 | 0.8719 | 0.8720 |
| 0.2394 | 19.05 | 400 | 0.2925 | 0.8809 | 0.8811 |
| 0.1887 | 28.57 | 600 | 0.2900 | 0.8902 | 0.8902 |
| 0.1513 | 38.1 | 800 | 0.3103 | 0.8750 | 0.875 |
| 0.1232 | 47.62 | 1000 | 0.4024 | 0.8748 | 0.875 |
| 0.1012 | 57.14 | 1200 | 0.4141 | 0.8686 | 0.8689 |
| 0.0864 | 66.67 | 1400 | 0.5371 | 0.8683 | 0.8689 |
| 0.0737 | 76.19 | 1600 | 0.5424 | 0.8745 | 0.875 |
| 0.0604 | 85.71 | 1800 | 0.6019 | 0.8776 | 0.8780 |
| 0.0523 | 95.24 | 2000 | 0.5280 | 0.8749 | 0.875 |
| 0.0464 | 104.76 | 2200 | 0.5558 | 0.8780 | 0.8780 |
| 0.0381 | 114.29 | 2400 | 0.6441 | 0.8839 | 0.8841 |
| 0.036 | 123.81 | 2600 | 0.6390 | 0.8718 | 0.8720 |
| 0.0323 | 133.33 | 2800 | 0.5986 | 0.8719 | 0.8720 |
| 0.0271 | 142.86 | 3000 | 0.7002 | 0.8809 | 0.8811 |
| 0.0271 | 152.38 | 3200 | 0.6997 | 0.8748 | 0.875 |
| 0.0257 | 161.9 | 3400 | 0.7475 | 0.8838 | 0.8841 |
| 0.0215 | 171.43 | 3600 | 0.7352 | 0.8779 | 0.8780 |
| 0.0227 | 180.95 | 3800 | 0.7311 | 0.8778 | 0.8780 |
| 0.0178 | 190.48 | 4000 | 0.7539 | 0.8749 | 0.875 |
| 0.0169 | 200.0 | 4200 | 0.7203 | 0.8718 | 0.8720 |
| 0.0174 | 209.52 | 4400 | 0.7283 | 0.8779 | 0.8780 |
| 0.0154 | 219.05 | 4600 | 0.7179 | 0.8810 | 0.8811 |
| 0.015 | 228.57 | 4800 | 0.7887 | 0.8656 | 0.8659 |
| 0.0155 | 238.1 | 5000 | 0.7739 | 0.8718 | 0.8720 |
| 0.0131 | 247.62 | 5200 | 0.7593 | 0.8719 | 0.8720 |
| 0.0132 | 257.14 | 5400 | 0.7224 | 0.8779 | 0.8780 |
| 0.0111 | 266.67 | 5600 | 0.7515 | 0.8749 | 0.875 |
| 0.0126 | 276.19 | 5800 | 0.7008 | 0.8811 | 0.8811 |
| 0.0093 | 285.71 | 6000 | 0.7463 | 0.8719 | 0.8720 |
| 0.0082 | 295.24 | 6200 | 0.7215 | 0.8811 | 0.8811 |
| 0.0102 | 304.76 | 6400 | 0.7556 | 0.8687 | 0.8689 |
| 0.0081 | 314.29 | 6600 | 0.7973 | 0.8779 | 0.8780 |
| 0.0101 | 323.81 | 6800 | 0.7145 | 0.8688 | 0.8689 |
| 0.0078 | 333.33 | 7000 | 0.7828 | 0.8657 | 0.8659 |
| 0.009 | 342.86 | 7200 | 0.7628 | 0.8749 | 0.875 |
| 0.0092 | 352.38 | 7400 | 0.7076 | 0.8750 | 0.875 |
| 0.0077 | 361.9 | 7600 | 0.7738 | 0.8658 | 0.8659 |
| 0.0086 | 371.43 | 7800 | 0.7825 | 0.8658 | 0.8659 |
| 0.0074 | 380.95 | 8000 | 0.7797 | 0.8749 | 0.875 |
| 0.0056 | 390.48 | 8200 | 0.8023 | 0.8689 | 0.8689 |
| 0.0057 | 400.0 | 8400 | 0.8784 | 0.8778 | 0.8780 |
| 0.0067 | 409.52 | 8600 | 0.8161 | 0.8719 | 0.8720 |
| 0.006 | 419.05 | 8800 | 0.7977 | 0.8689 | 0.8689 |
| 0.0066 | 428.57 | 9000 | 0.8332 | 0.8657 | 0.8659 |
| 0.0069 | 438.1 | 9200 | 0.7704 | 0.8658 | 0.8659 |
| 0.006 | 447.62 | 9400 | 0.7767 | 0.8748 | 0.875 |
| 0.0063 | 457.14 | 9600 | 0.7809 | 0.8748 | 0.875 |
| 0.0061 | 466.67 | 9800 | 0.7697 | 0.8687 | 0.8689 |
| 0.0045 | 476.19 | 10000 | 0.7637 | 0.8718 | 0.8720 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:58:12+00:00 |
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | swj0419/hp_all_STEP0000050 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T08:58:23+00:00 |
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. -->
# GUE_splice_reconstructed-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3949
- F1 Score: 0.8432
- Accuracy: 0.8426
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.9544 | 0.7 | 200 | 0.8989 | 0.5038 | 0.5783 |
| 0.7744 | 1.4 | 400 | 0.6254 | 0.7144 | 0.7135 |
| 0.5998 | 2.1 | 600 | 0.5328 | 0.7683 | 0.7674 |
| 0.5445 | 2.8 | 800 | 0.5231 | 0.7736 | 0.7725 |
| 0.5252 | 3.5 | 1000 | 0.4931 | 0.7867 | 0.7858 |
| 0.5101 | 4.2 | 1200 | 0.4983 | 0.7889 | 0.7876 |
| 0.5005 | 4.9 | 1400 | 0.4710 | 0.7992 | 0.7986 |
| 0.4848 | 5.59 | 1600 | 0.4855 | 0.7969 | 0.7966 |
| 0.4811 | 6.29 | 1800 | 0.4811 | 0.7985 | 0.7975 |
| 0.4696 | 6.99 | 2000 | 0.4671 | 0.8037 | 0.8027 |
| 0.458 | 7.69 | 2200 | 0.4568 | 0.8088 | 0.8086 |
| 0.461 | 8.39 | 2400 | 0.4699 | 0.8054 | 0.8045 |
| 0.4517 | 9.09 | 2600 | 0.4603 | 0.8085 | 0.8073 |
| 0.4486 | 9.79 | 2800 | 0.4553 | 0.8105 | 0.8095 |
| 0.4517 | 10.49 | 3000 | 0.4565 | 0.8116 | 0.8106 |
| 0.4375 | 11.19 | 3200 | 0.4634 | 0.8093 | 0.8082 |
| 0.4389 | 11.89 | 3400 | 0.4407 | 0.8187 | 0.8178 |
| 0.4255 | 12.59 | 3600 | 0.4567 | 0.8153 | 0.8143 |
| 0.4311 | 13.29 | 3800 | 0.4412 | 0.8176 | 0.8165 |
| 0.423 | 13.99 | 4000 | 0.4540 | 0.8140 | 0.8130 |
| 0.4143 | 14.69 | 4200 | 0.4364 | 0.8247 | 0.8238 |
| 0.4198 | 15.38 | 4400 | 0.4477 | 0.8160 | 0.8150 |
| 0.4129 | 16.08 | 4600 | 0.4591 | 0.8153 | 0.8143 |
| 0.4129 | 16.78 | 4800 | 0.4306 | 0.8236 | 0.8227 |
| 0.4016 | 17.48 | 5000 | 0.4344 | 0.8259 | 0.8249 |
| 0.409 | 18.18 | 5200 | 0.4196 | 0.8317 | 0.8310 |
| 0.4078 | 18.88 | 5400 | 0.4377 | 0.8225 | 0.8216 |
| 0.4027 | 19.58 | 5600 | 0.4198 | 0.8321 | 0.8314 |
| 0.4055 | 20.28 | 5800 | 0.4184 | 0.8316 | 0.8308 |
| 0.4002 | 20.98 | 6000 | 0.4179 | 0.8330 | 0.8323 |
| 0.3981 | 21.68 | 6200 | 0.4257 | 0.8287 | 0.8277 |
| 0.3926 | 22.38 | 6400 | 0.4217 | 0.8273 | 0.8264 |
| 0.3889 | 23.08 | 6600 | 0.4189 | 0.8341 | 0.8332 |
| 0.3922 | 23.78 | 6800 | 0.4210 | 0.8339 | 0.8330 |
| 0.3937 | 24.48 | 7000 | 0.4294 | 0.8270 | 0.8260 |
| 0.3883 | 25.17 | 7200 | 0.4114 | 0.8380 | 0.8371 |
| 0.3881 | 25.87 | 7400 | 0.4151 | 0.8363 | 0.8354 |
| 0.3883 | 26.57 | 7600 | 0.4206 | 0.8316 | 0.8306 |
| 0.3833 | 27.27 | 7800 | 0.4180 | 0.8331 | 0.8323 |
| 0.3858 | 27.97 | 8000 | 0.4170 | 0.8341 | 0.8332 |
| 0.3889 | 28.67 | 8200 | 0.4172 | 0.8330 | 0.8321 |
| 0.3823 | 29.37 | 8400 | 0.4112 | 0.8361 | 0.8352 |
| 0.3786 | 30.07 | 8600 | 0.4042 | 0.8401 | 0.8395 |
| 0.3833 | 30.77 | 8800 | 0.4171 | 0.8329 | 0.8319 |
| 0.3838 | 31.47 | 9000 | 0.4116 | 0.8366 | 0.8356 |
| 0.3828 | 32.17 | 9200 | 0.4096 | 0.8371 | 0.8363 |
| 0.3802 | 32.87 | 9400 | 0.4140 | 0.8343 | 0.8334 |
| 0.3798 | 33.57 | 9600 | 0.4123 | 0.8356 | 0.8347 |
| 0.3786 | 34.27 | 9800 | 0.4098 | 0.8376 | 0.8367 |
| 0.3752 | 34.97 | 10000 | 0.4115 | 0.8367 | 0.8358 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:59:33+00:00 |
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. -->
# GUE_splice_reconstructed-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4633
- F1 Score: 0.8080
- Accuracy: 0.8069
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.9669 | 0.7 | 200 | 0.9307 | 0.4578 | 0.5642 |
| 0.9215 | 1.4 | 400 | 0.8928 | 0.5146 | 0.5826 |
| 0.8296 | 2.1 | 600 | 0.6980 | 0.6810 | 0.6817 |
| 0.6659 | 2.8 | 800 | 0.6269 | 0.7130 | 0.7115 |
| 0.6235 | 3.5 | 1000 | 0.5873 | 0.7356 | 0.7341 |
| 0.5916 | 4.2 | 1200 | 0.5581 | 0.7545 | 0.7534 |
| 0.5763 | 4.9 | 1400 | 0.5311 | 0.7658 | 0.7652 |
| 0.5597 | 5.59 | 1600 | 0.5250 | 0.7694 | 0.7685 |
| 0.5487 | 6.29 | 1800 | 0.5269 | 0.7708 | 0.7696 |
| 0.5368 | 6.99 | 2000 | 0.5178 | 0.7764 | 0.7751 |
| 0.5276 | 7.69 | 2200 | 0.4951 | 0.7846 | 0.7845 |
| 0.5308 | 8.39 | 2400 | 0.5020 | 0.7842 | 0.7832 |
| 0.5217 | 9.09 | 2600 | 0.5034 | 0.7851 | 0.7836 |
| 0.5178 | 9.79 | 2800 | 0.4998 | 0.7857 | 0.7845 |
| 0.5195 | 10.49 | 3000 | 0.4928 | 0.7874 | 0.7863 |
| 0.5109 | 11.19 | 3200 | 0.5012 | 0.7853 | 0.7839 |
| 0.511 | 11.89 | 3400 | 0.4799 | 0.7967 | 0.7959 |
| 0.5008 | 12.59 | 3600 | 0.4822 | 0.7905 | 0.7893 |
| 0.5045 | 13.29 | 3800 | 0.4846 | 0.7904 | 0.7893 |
| 0.4989 | 13.99 | 4000 | 0.4913 | 0.7921 | 0.7909 |
| 0.4914 | 14.69 | 4200 | 0.4756 | 0.7962 | 0.7950 |
| 0.4948 | 15.38 | 4400 | 0.4816 | 0.7958 | 0.7946 |
| 0.4929 | 16.08 | 4600 | 0.4987 | 0.7845 | 0.7832 |
| 0.4889 | 16.78 | 4800 | 0.4732 | 0.7990 | 0.7979 |
| 0.4844 | 17.48 | 5000 | 0.4836 | 0.7920 | 0.7907 |
| 0.4888 | 18.18 | 5200 | 0.4635 | 0.8029 | 0.8021 |
| 0.4888 | 18.88 | 5400 | 0.4827 | 0.7954 | 0.7942 |
| 0.4872 | 19.58 | 5600 | 0.4649 | 0.8009 | 0.8003 |
| 0.4842 | 20.28 | 5800 | 0.4679 | 0.8009 | 0.7999 |
| 0.4821 | 20.98 | 6000 | 0.4622 | 0.8030 | 0.8021 |
| 0.4852 | 21.68 | 6200 | 0.4721 | 0.7999 | 0.7988 |
| 0.4758 | 22.38 | 6400 | 0.4666 | 0.8013 | 0.8003 |
| 0.4758 | 23.08 | 6600 | 0.4623 | 0.8033 | 0.8023 |
| 0.4788 | 23.78 | 6800 | 0.4649 | 0.8020 | 0.8010 |
| 0.4802 | 24.48 | 7000 | 0.4727 | 0.8007 | 0.7994 |
| 0.4758 | 25.17 | 7200 | 0.4621 | 0.8036 | 0.8025 |
| 0.476 | 25.87 | 7400 | 0.4602 | 0.8082 | 0.8071 |
| 0.4769 | 26.57 | 7600 | 0.4698 | 0.8000 | 0.7988 |
| 0.4727 | 27.27 | 7800 | 0.4618 | 0.8042 | 0.8032 |
| 0.471 | 27.97 | 8000 | 0.4617 | 0.8074 | 0.8062 |
| 0.4771 | 28.67 | 8200 | 0.4702 | 0.8006 | 0.7994 |
| 0.4708 | 29.37 | 8400 | 0.4620 | 0.8041 | 0.8029 |
| 0.4687 | 30.07 | 8600 | 0.4528 | 0.8066 | 0.8058 |
| 0.4769 | 30.77 | 8800 | 0.4618 | 0.8049 | 0.8038 |
| 0.474 | 31.47 | 9000 | 0.4601 | 0.8052 | 0.8040 |
| 0.4703 | 32.17 | 9200 | 0.4615 | 0.8052 | 0.8040 |
| 0.4726 | 32.87 | 9400 | 0.4630 | 0.8028 | 0.8016 |
| 0.473 | 33.57 | 9600 | 0.4614 | 0.8034 | 0.8023 |
| 0.4645 | 34.27 | 9800 | 0.4594 | 0.8051 | 0.8040 |
| 0.4681 | 34.97 | 10000 | 0.4601 | 0.8047 | 0.8036 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T08:59:38+00:00 |
null | null |
# gate369/llama-lexi-star-uncensored-8b-slerp-Q4_K_M-GGUF
This model was converted to GGUF format from [`liminerity/llama-lexi-star-uncensored-8b-slerp`](https://huggingface.co/liminerity/llama-lexi-star-uncensored-8b-slerp) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/liminerity/llama-lexi-star-uncensored-8b-slerp) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo gate369/llama-lexi-star-uncensored-8b-slerp-Q4_K_M-GGUF --model llama-lexi-star-uncensored-8b-slerp.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo gate369/llama-lexi-star-uncensored-8b-slerp-Q4_K_M-GGUF --model llama-lexi-star-uncensored-8b-slerp.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-lexi-star-uncensored-8b-slerp.Q4_K_M.gguf -n 128
```
| {"tags": ["merge", "mergekit", "lazymergekit", "Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1", "liminerity/llama-3-8b-silent-star", "llama-cpp", "gguf-my-repo"], "base_model": ["Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1", "liminerity/llama-3-8b-silent-star"]} | gate369/llama-lexi-star-uncensored-8b-slerp-Q4_K_M-GGUF | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1",
"liminerity/llama-3-8b-silent-star",
"llama-cpp",
"gguf-my-repo",
"base_model:Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1",
"base_model:liminerity/llama-3-8b-silent-star",
"region:us"
]
| null | 2024-04-27T09:00:01+00:00 |
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. -->
# GUE_splice_reconstructed-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3483
- F1 Score: 0.8692
- Accuracy: 0.8687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.9287 | 0.7 | 200 | 0.7118 | 0.6762 | 0.6767 |
| 0.6007 | 1.4 | 400 | 0.5402 | 0.7593 | 0.7582 |
| 0.539 | 2.1 | 600 | 0.4914 | 0.7874 | 0.7865 |
| 0.4949 | 2.8 | 800 | 0.5057 | 0.7827 | 0.7817 |
| 0.4775 | 3.5 | 1000 | 0.4581 | 0.8093 | 0.8084 |
| 0.4643 | 4.2 | 1200 | 0.4679 | 0.8048 | 0.8036 |
| 0.4463 | 4.9 | 1400 | 0.4395 | 0.8233 | 0.8227 |
| 0.4288 | 5.59 | 1600 | 0.4535 | 0.8165 | 0.8159 |
| 0.4233 | 6.29 | 1800 | 0.4458 | 0.8136 | 0.8124 |
| 0.4096 | 6.99 | 2000 | 0.4190 | 0.8347 | 0.8338 |
| 0.3915 | 7.69 | 2200 | 0.4193 | 0.8331 | 0.8323 |
| 0.3961 | 8.39 | 2400 | 0.4223 | 0.8305 | 0.8297 |
| 0.3818 | 9.09 | 2600 | 0.4250 | 0.8342 | 0.8327 |
| 0.378 | 9.79 | 2800 | 0.4182 | 0.8340 | 0.8327 |
| 0.376 | 10.49 | 3000 | 0.3991 | 0.8433 | 0.8426 |
| 0.3631 | 11.19 | 3200 | 0.4295 | 0.8289 | 0.8279 |
| 0.3627 | 11.89 | 3400 | 0.3879 | 0.8464 | 0.8457 |
| 0.3515 | 12.59 | 3600 | 0.3995 | 0.8453 | 0.8446 |
| 0.3509 | 13.29 | 3800 | 0.3893 | 0.8478 | 0.8470 |
| 0.3448 | 13.99 | 4000 | 0.4024 | 0.8401 | 0.8391 |
| 0.3376 | 14.69 | 4200 | 0.3902 | 0.8450 | 0.8441 |
| 0.3369 | 15.38 | 4400 | 0.3926 | 0.8489 | 0.8481 |
| 0.327 | 16.08 | 4600 | 0.4101 | 0.8415 | 0.8406 |
| 0.3292 | 16.78 | 4800 | 0.3866 | 0.8483 | 0.8474 |
| 0.3156 | 17.48 | 5000 | 0.3948 | 0.8470 | 0.8461 |
| 0.3257 | 18.18 | 5200 | 0.3735 | 0.8591 | 0.8586 |
| 0.3199 | 18.88 | 5400 | 0.3695 | 0.8586 | 0.8580 |
| 0.3124 | 19.58 | 5600 | 0.3653 | 0.8617 | 0.8612 |
| 0.3178 | 20.28 | 5800 | 0.3772 | 0.8556 | 0.8549 |
| 0.31 | 20.98 | 6000 | 0.3696 | 0.8578 | 0.8571 |
| 0.3081 | 21.68 | 6200 | 0.3708 | 0.8585 | 0.8577 |
| 0.3044 | 22.38 | 6400 | 0.3623 | 0.8623 | 0.8617 |
| 0.2997 | 23.08 | 6600 | 0.3701 | 0.8571 | 0.8564 |
| 0.3016 | 23.78 | 6800 | 0.3665 | 0.8601 | 0.8595 |
| 0.2976 | 24.48 | 7000 | 0.3792 | 0.8559 | 0.8551 |
| 0.2929 | 25.17 | 7200 | 0.3724 | 0.8578 | 0.8571 |
| 0.2971 | 25.87 | 7400 | 0.3675 | 0.8630 | 0.8623 |
| 0.2943 | 26.57 | 7600 | 0.3692 | 0.8589 | 0.8582 |
| 0.2913 | 27.27 | 7800 | 0.3768 | 0.8550 | 0.8542 |
| 0.2928 | 27.97 | 8000 | 0.3667 | 0.8608 | 0.8601 |
| 0.2929 | 28.67 | 8200 | 0.3738 | 0.8563 | 0.8555 |
| 0.2857 | 29.37 | 8400 | 0.3676 | 0.8598 | 0.8591 |
| 0.2802 | 30.07 | 8600 | 0.3617 | 0.8661 | 0.8656 |
| 0.2816 | 30.77 | 8800 | 0.3699 | 0.8589 | 0.8582 |
| 0.2877 | 31.47 | 9000 | 0.3685 | 0.8585 | 0.8577 |
| 0.2871 | 32.17 | 9200 | 0.3618 | 0.8638 | 0.8632 |
| 0.281 | 32.87 | 9400 | 0.3681 | 0.8617 | 0.8610 |
| 0.2825 | 33.57 | 9600 | 0.3675 | 0.8628 | 0.8621 |
| 0.2797 | 34.27 | 9800 | 0.3658 | 0.8639 | 0.8632 |
| 0.2767 | 34.97 | 10000 | 0.3676 | 0.8628 | 0.8621 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:00:14+00:00 |
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. -->
# GUE_tf_0-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3882
- F1 Score: 0.8304
- Accuracy: 0.831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.581 | 0.79 | 200 | 0.5158 | 0.7465 | 0.75 |
| 0.5192 | 1.58 | 400 | 0.4922 | 0.7533 | 0.757 |
| 0.504 | 2.37 | 600 | 0.4914 | 0.7442 | 0.75 |
| 0.4901 | 3.16 | 800 | 0.4684 | 0.7651 | 0.766 |
| 0.4852 | 3.95 | 1000 | 0.4719 | 0.7669 | 0.769 |
| 0.4844 | 4.74 | 1200 | 0.4714 | 0.7690 | 0.77 |
| 0.4794 | 5.53 | 1400 | 0.4694 | 0.7681 | 0.77 |
| 0.4739 | 6.32 | 1600 | 0.4638 | 0.7755 | 0.776 |
| 0.4742 | 7.11 | 1800 | 0.4628 | 0.7779 | 0.778 |
| 0.4762 | 7.91 | 2000 | 0.4631 | 0.7835 | 0.784 |
| 0.469 | 8.7 | 2200 | 0.4635 | 0.7805 | 0.781 |
| 0.4687 | 9.49 | 2400 | 0.4669 | 0.7683 | 0.77 |
| 0.4688 | 10.28 | 2600 | 0.4625 | 0.7820 | 0.782 |
| 0.4646 | 11.07 | 2800 | 0.4614 | 0.7809 | 0.781 |
| 0.4668 | 11.86 | 3000 | 0.4612 | 0.7869 | 0.787 |
| 0.465 | 12.65 | 3200 | 0.4622 | 0.7810 | 0.781 |
| 0.4625 | 13.44 | 3400 | 0.4656 | 0.7758 | 0.777 |
| 0.462 | 14.23 | 3600 | 0.4624 | 0.7761 | 0.777 |
| 0.4616 | 15.02 | 3800 | 0.4624 | 0.7860 | 0.786 |
| 0.4649 | 15.81 | 4000 | 0.4620 | 0.7859 | 0.786 |
| 0.4591 | 16.6 | 4200 | 0.4608 | 0.7795 | 0.78 |
| 0.4602 | 17.39 | 4400 | 0.4621 | 0.7870 | 0.787 |
| 0.4612 | 18.18 | 4600 | 0.4597 | 0.7869 | 0.787 |
| 0.4587 | 18.97 | 4800 | 0.4631 | 0.7871 | 0.787 |
| 0.4622 | 19.76 | 5000 | 0.4583 | 0.7858 | 0.786 |
| 0.4581 | 20.55 | 5200 | 0.4591 | 0.7832 | 0.784 |
| 0.4551 | 21.34 | 5400 | 0.4587 | 0.7818 | 0.782 |
| 0.4551 | 22.13 | 5600 | 0.4595 | 0.7858 | 0.786 |
| 0.4587 | 22.92 | 5800 | 0.4585 | 0.7867 | 0.787 |
| 0.453 | 23.72 | 6000 | 0.4595 | 0.7940 | 0.794 |
| 0.4538 | 24.51 | 6200 | 0.4579 | 0.7865 | 0.787 |
| 0.4593 | 25.3 | 6400 | 0.4583 | 0.7899 | 0.79 |
| 0.4524 | 26.09 | 6600 | 0.4580 | 0.7868 | 0.787 |
| 0.4529 | 26.88 | 6800 | 0.4594 | 0.7899 | 0.79 |
| 0.4517 | 27.67 | 7000 | 0.4574 | 0.7876 | 0.788 |
| 0.453 | 28.46 | 7200 | 0.4577 | 0.7854 | 0.786 |
| 0.4541 | 29.25 | 7400 | 0.4574 | 0.7856 | 0.786 |
| 0.4543 | 30.04 | 7600 | 0.4573 | 0.7856 | 0.786 |
| 0.4524 | 30.83 | 7800 | 0.4567 | 0.7878 | 0.788 |
| 0.4533 | 31.62 | 8000 | 0.4563 | 0.7855 | 0.786 |
| 0.4521 | 32.41 | 8200 | 0.4567 | 0.7939 | 0.794 |
| 0.4469 | 33.2 | 8400 | 0.4574 | 0.7888 | 0.789 |
| 0.4553 | 33.99 | 8600 | 0.4566 | 0.7857 | 0.786 |
| 0.4521 | 34.78 | 8800 | 0.4566 | 0.7885 | 0.789 |
| 0.4477 | 35.57 | 9000 | 0.4574 | 0.7898 | 0.79 |
| 0.4513 | 36.36 | 9200 | 0.4578 | 0.7919 | 0.792 |
| 0.4526 | 37.15 | 9400 | 0.4572 | 0.7929 | 0.793 |
| 0.4503 | 37.94 | 9600 | 0.4568 | 0.7877 | 0.788 |
| 0.4516 | 38.74 | 9800 | 0.4568 | 0.7888 | 0.789 |
| 0.4544 | 39.53 | 10000 | 0.4568 | 0.7888 | 0.789 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:01:00+00:00 |
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. -->
# GUE_tf_0-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3855
- F1 Score: 0.8285
- Accuracy: 0.829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5543 | 0.79 | 200 | 0.4845 | 0.7528 | 0.755 |
| 0.4904 | 1.58 | 400 | 0.4700 | 0.7666 | 0.768 |
| 0.4829 | 2.37 | 600 | 0.4719 | 0.7645 | 0.767 |
| 0.4735 | 3.16 | 800 | 0.4582 | 0.7778 | 0.778 |
| 0.47 | 3.95 | 1000 | 0.4613 | 0.7723 | 0.774 |
| 0.4684 | 4.74 | 1200 | 0.4630 | 0.7848 | 0.785 |
| 0.4625 | 5.53 | 1400 | 0.4630 | 0.7811 | 0.781 |
| 0.4578 | 6.32 | 1600 | 0.4571 | 0.7919 | 0.792 |
| 0.4591 | 7.11 | 1800 | 0.4637 | 0.7710 | 0.771 |
| 0.4594 | 7.91 | 2000 | 0.4610 | 0.7791 | 0.779 |
| 0.4518 | 8.7 | 2200 | 0.4609 | 0.7748 | 0.775 |
| 0.4494 | 9.49 | 2400 | 0.4597 | 0.7822 | 0.783 |
| 0.4519 | 10.28 | 2600 | 0.4616 | 0.7831 | 0.783 |
| 0.4464 | 11.07 | 2800 | 0.4611 | 0.7771 | 0.777 |
| 0.447 | 11.86 | 3000 | 0.4625 | 0.7801 | 0.78 |
| 0.4442 | 12.65 | 3200 | 0.4616 | 0.7760 | 0.776 |
| 0.4433 | 13.44 | 3400 | 0.4603 | 0.7810 | 0.782 |
| 0.4423 | 14.23 | 3600 | 0.4578 | 0.7888 | 0.789 |
| 0.4409 | 15.02 | 3800 | 0.4647 | 0.7871 | 0.787 |
| 0.4444 | 15.81 | 4000 | 0.4582 | 0.7810 | 0.781 |
| 0.4372 | 16.6 | 4200 | 0.4597 | 0.7848 | 0.785 |
| 0.4389 | 17.39 | 4400 | 0.4671 | 0.782 | 0.782 |
| 0.4388 | 18.18 | 4600 | 0.4616 | 0.7840 | 0.784 |
| 0.4371 | 18.97 | 4800 | 0.4603 | 0.7880 | 0.788 |
| 0.4394 | 19.76 | 5000 | 0.4561 | 0.7829 | 0.783 |
| 0.4361 | 20.55 | 5200 | 0.4565 | 0.7834 | 0.784 |
| 0.432 | 21.34 | 5400 | 0.4577 | 0.7870 | 0.787 |
| 0.4332 | 22.13 | 5600 | 0.4578 | 0.782 | 0.782 |
| 0.4347 | 22.92 | 5800 | 0.4562 | 0.7849 | 0.785 |
| 0.4297 | 23.72 | 6000 | 0.4588 | 0.7911 | 0.791 |
| 0.4305 | 24.51 | 6200 | 0.4549 | 0.7828 | 0.783 |
| 0.4361 | 25.3 | 6400 | 0.4553 | 0.7830 | 0.783 |
| 0.4289 | 26.09 | 6600 | 0.4544 | 0.7848 | 0.785 |
| 0.4281 | 26.88 | 6800 | 0.4584 | 0.788 | 0.788 |
| 0.4261 | 27.67 | 7000 | 0.4555 | 0.7857 | 0.786 |
| 0.4283 | 28.46 | 7200 | 0.4556 | 0.7846 | 0.785 |
| 0.4297 | 29.25 | 7400 | 0.4559 | 0.7839 | 0.784 |
| 0.4303 | 30.04 | 7600 | 0.4550 | 0.7829 | 0.783 |
| 0.4258 | 30.83 | 7800 | 0.4558 | 0.7890 | 0.789 |
| 0.4269 | 31.62 | 8000 | 0.4534 | 0.7863 | 0.787 |
| 0.429 | 32.41 | 8200 | 0.4557 | 0.7870 | 0.787 |
| 0.4201 | 33.2 | 8400 | 0.4553 | 0.7859 | 0.786 |
| 0.4287 | 33.99 | 8600 | 0.4544 | 0.7848 | 0.785 |
| 0.4267 | 34.78 | 8800 | 0.4538 | 0.7885 | 0.789 |
| 0.4215 | 35.57 | 9000 | 0.4554 | 0.7868 | 0.787 |
| 0.4246 | 36.36 | 9200 | 0.4567 | 0.7890 | 0.789 |
| 0.4255 | 37.15 | 9400 | 0.4554 | 0.784 | 0.784 |
| 0.4246 | 37.94 | 9600 | 0.4544 | 0.7829 | 0.783 |
| 0.4255 | 38.74 | 9800 | 0.4547 | 0.7840 | 0.784 |
| 0.4269 | 39.53 | 10000 | 0.4546 | 0.7839 | 0.784 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:02:01+00:00 |
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. -->
# GUE_tf_0-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3866
- F1 Score: 0.8232
- Accuracy: 0.824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5373 | 0.79 | 200 | 0.4807 | 0.7629 | 0.766 |
| 0.4837 | 1.58 | 400 | 0.4626 | 0.7732 | 0.774 |
| 0.477 | 2.37 | 600 | 0.4670 | 0.7752 | 0.777 |
| 0.4671 | 3.16 | 800 | 0.4600 | 0.7786 | 0.779 |
| 0.4621 | 3.95 | 1000 | 0.4566 | 0.7828 | 0.784 |
| 0.4588 | 4.74 | 1200 | 0.4620 | 0.7818 | 0.782 |
| 0.4513 | 5.53 | 1400 | 0.4673 | 0.7708 | 0.771 |
| 0.4463 | 6.32 | 1600 | 0.4561 | 0.7889 | 0.789 |
| 0.4459 | 7.11 | 1800 | 0.4640 | 0.7779 | 0.778 |
| 0.4445 | 7.91 | 2000 | 0.4627 | 0.7800 | 0.78 |
| 0.436 | 8.7 | 2200 | 0.4650 | 0.7800 | 0.78 |
| 0.4333 | 9.49 | 2400 | 0.4599 | 0.7796 | 0.78 |
| 0.4365 | 10.28 | 2600 | 0.4616 | 0.7841 | 0.784 |
| 0.4289 | 11.07 | 2800 | 0.4628 | 0.7810 | 0.781 |
| 0.4284 | 11.86 | 3000 | 0.4649 | 0.7871 | 0.787 |
| 0.4246 | 12.65 | 3200 | 0.4610 | 0.7880 | 0.788 |
| 0.4239 | 13.44 | 3400 | 0.4606 | 0.7845 | 0.785 |
| 0.422 | 14.23 | 3600 | 0.4599 | 0.7799 | 0.78 |
| 0.4217 | 15.02 | 3800 | 0.4707 | 0.7810 | 0.781 |
| 0.4226 | 15.81 | 4000 | 0.4561 | 0.7799 | 0.78 |
| 0.4143 | 16.6 | 4200 | 0.4692 | 0.7767 | 0.777 |
| 0.4165 | 17.39 | 4400 | 0.4688 | 0.7821 | 0.782 |
| 0.4147 | 18.18 | 4600 | 0.4625 | 0.7829 | 0.783 |
| 0.4128 | 18.97 | 4800 | 0.4613 | 0.7841 | 0.784 |
| 0.4142 | 19.76 | 5000 | 0.4587 | 0.7850 | 0.785 |
| 0.4106 | 20.55 | 5200 | 0.4617 | 0.7837 | 0.784 |
| 0.4044 | 21.34 | 5400 | 0.4661 | 0.7861 | 0.786 |
| 0.408 | 22.13 | 5600 | 0.4642 | 0.7820 | 0.782 |
| 0.4077 | 22.92 | 5800 | 0.4665 | 0.776 | 0.776 |
| 0.4015 | 23.72 | 6000 | 0.4654 | 0.7841 | 0.784 |
| 0.4029 | 24.51 | 6200 | 0.4593 | 0.7799 | 0.78 |
| 0.4056 | 25.3 | 6400 | 0.4646 | 0.7780 | 0.778 |
| 0.398 | 26.09 | 6600 | 0.4616 | 0.7838 | 0.784 |
| 0.3973 | 26.88 | 6800 | 0.4684 | 0.7870 | 0.787 |
| 0.395 | 27.67 | 7000 | 0.4702 | 0.7779 | 0.778 |
| 0.3958 | 28.46 | 7200 | 0.4673 | 0.7827 | 0.783 |
| 0.3984 | 29.25 | 7400 | 0.4710 | 0.7800 | 0.78 |
| 0.399 | 30.04 | 7600 | 0.4650 | 0.7850 | 0.785 |
| 0.3931 | 30.83 | 7800 | 0.4668 | 0.7861 | 0.786 |
| 0.3941 | 31.62 | 8000 | 0.4680 | 0.7816 | 0.782 |
| 0.3951 | 32.41 | 8200 | 0.4718 | 0.7830 | 0.783 |
| 0.384 | 33.2 | 8400 | 0.4723 | 0.7830 | 0.783 |
| 0.3932 | 33.99 | 8600 | 0.4707 | 0.7840 | 0.784 |
| 0.3913 | 34.78 | 8800 | 0.4651 | 0.7807 | 0.781 |
| 0.3858 | 35.57 | 9000 | 0.4724 | 0.7810 | 0.781 |
| 0.3888 | 36.36 | 9200 | 0.4756 | 0.7810 | 0.781 |
| 0.3886 | 37.15 | 9400 | 0.4742 | 0.7810 | 0.781 |
| 0.3893 | 37.94 | 9600 | 0.4720 | 0.7770 | 0.777 |
| 0.388 | 38.74 | 9800 | 0.4730 | 0.782 | 0.782 |
| 0.3892 | 39.53 | 10000 | 0.4727 | 0.7830 | 0.783 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:02:11+00:00 |
reinforcement-learning | 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 Epoching -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 Epoching -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 Epoching
```
## 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', 10000000.0),
('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": "726.50 +/- 370.81", "name": "mean_reward", "verified": false}]}]}]} | Epoching/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T09:02:38+00:00 |
text-generation | transformers |
# miqu-evil-dpo
# **Model Details**
## Description
miqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.
It is trained with evil-tune method applied.
<!-- prompt-template start -->
## Prompt template: Mistral Inst
```
<s> [INST] {inst} [/INST]
```
<!-- prompt-template end -->
## Disclaimer
The AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use.
| {"language": ["en"], "license": "other", "tags": ["not-for-all-audiences"], "license_name": "miqu-license", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | blockblockblock/miqu-evil-dpo-bpw5.5-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:03:47+00:00 |
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. -->
# GUE_tf_1-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- F1 Score: 0.8533
- Accuracy: 0.854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5914 | 0.83 | 200 | 0.5578 | 0.7208 | 0.721 |
| 0.5361 | 1.67 | 400 | 0.5433 | 0.7270 | 0.728 |
| 0.5202 | 2.5 | 600 | 0.5344 | 0.7356 | 0.736 |
| 0.507 | 3.33 | 800 | 0.5270 | 0.7400 | 0.74 |
| 0.5053 | 4.17 | 1000 | 0.5284 | 0.7399 | 0.741 |
| 0.5003 | 5.0 | 1200 | 0.5283 | 0.7473 | 0.748 |
| 0.4965 | 5.83 | 1400 | 0.5204 | 0.7477 | 0.748 |
| 0.4963 | 6.67 | 1600 | 0.5216 | 0.7539 | 0.754 |
| 0.4939 | 7.5 | 1800 | 0.5214 | 0.7475 | 0.748 |
| 0.4906 | 8.33 | 2000 | 0.5176 | 0.7489 | 0.749 |
| 0.4881 | 9.17 | 2200 | 0.5183 | 0.7560 | 0.756 |
| 0.4894 | 10.0 | 2400 | 0.5257 | 0.7579 | 0.758 |
| 0.4879 | 10.83 | 2600 | 0.5207 | 0.7560 | 0.756 |
| 0.4859 | 11.67 | 2800 | 0.5212 | 0.7517 | 0.752 |
| 0.4803 | 12.5 | 3000 | 0.5192 | 0.7570 | 0.757 |
| 0.4872 | 13.33 | 3200 | 0.5122 | 0.7559 | 0.756 |
| 0.4797 | 14.17 | 3400 | 0.5128 | 0.7499 | 0.75 |
| 0.4795 | 15.0 | 3600 | 0.5167 | 0.7556 | 0.756 |
| 0.4785 | 15.83 | 3800 | 0.5125 | 0.7540 | 0.754 |
| 0.481 | 16.67 | 4000 | 0.5151 | 0.7560 | 0.756 |
| 0.4779 | 17.5 | 4200 | 0.5120 | 0.7550 | 0.755 |
| 0.4765 | 18.33 | 4400 | 0.5178 | 0.7580 | 0.758 |
| 0.4794 | 19.17 | 4600 | 0.5124 | 0.7568 | 0.757 |
| 0.4787 | 20.0 | 4800 | 0.5116 | 0.7540 | 0.754 |
| 0.4777 | 20.83 | 5000 | 0.5142 | 0.7610 | 0.761 |
| 0.4748 | 21.67 | 5200 | 0.5104 | 0.7549 | 0.755 |
| 0.474 | 22.5 | 5400 | 0.5118 | 0.7499 | 0.75 |
| 0.4756 | 23.33 | 5600 | 0.5089 | 0.7589 | 0.759 |
| 0.4736 | 24.17 | 5800 | 0.5119 | 0.7579 | 0.758 |
| 0.474 | 25.0 | 6000 | 0.5115 | 0.7550 | 0.755 |
| 0.4723 | 25.83 | 6200 | 0.5088 | 0.7610 | 0.761 |
| 0.4714 | 26.67 | 6400 | 0.5100 | 0.7528 | 0.753 |
| 0.4739 | 27.5 | 6600 | 0.5098 | 0.7590 | 0.759 |
| 0.4713 | 28.33 | 6800 | 0.5071 | 0.7549 | 0.755 |
| 0.4718 | 29.17 | 7000 | 0.5088 | 0.7520 | 0.752 |
| 0.4725 | 30.0 | 7200 | 0.5082 | 0.7590 | 0.759 |
| 0.473 | 30.83 | 7400 | 0.5089 | 0.7569 | 0.757 |
| 0.4698 | 31.67 | 7600 | 0.5070 | 0.7550 | 0.755 |
| 0.4727 | 32.5 | 7800 | 0.5053 | 0.7560 | 0.756 |
| 0.4674 | 33.33 | 8000 | 0.5073 | 0.7570 | 0.757 |
| 0.4714 | 34.17 | 8200 | 0.5057 | 0.7540 | 0.754 |
| 0.4682 | 35.0 | 8400 | 0.5060 | 0.756 | 0.756 |
| 0.4707 | 35.83 | 8600 | 0.5053 | 0.7540 | 0.754 |
| 0.4693 | 36.67 | 8800 | 0.5053 | 0.7540 | 0.754 |
| 0.4658 | 37.5 | 9000 | 0.5070 | 0.7528 | 0.753 |
| 0.469 | 38.33 | 9200 | 0.5056 | 0.7530 | 0.753 |
| 0.467 | 39.17 | 9400 | 0.5060 | 0.7570 | 0.757 |
| 0.469 | 40.0 | 9600 | 0.5054 | 0.7530 | 0.753 |
| 0.4677 | 40.83 | 9800 | 0.5057 | 0.756 | 0.756 |
| 0.4693 | 41.67 | 10000 | 0.5053 | 0.7570 | 0.757 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:04:06+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** xsa-dev
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | xsa-dev/hugs_llama3_technique_ft_16bit_lora | null | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:04:11+00:00 |
text-generation | transformers |
# stablelm-2-zephyr-1.6b-slerpx9
stablelm-2-zephyr-1.6b-slerpx9 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aipib/stablelm-2-zephyr-1.6b-slerpx3](https://huggingface.co/aipib/stablelm-2-zephyr-1.6b-slerpx3)
* [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: aipib/stablelm-2-zephyr-1.6b-slerpx3
layer_range: [0, 24]
- model: stabilityai/stablelm-2-zephyr-1_6b
layer_range: [0, 24]
merge_method: slerp
base_model: aipib/stablelm-2-zephyr-1.6b-slerpx3
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/stablelm-2-zephyr-1.6b-slerpx9"
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", "aipib/stablelm-2-zephyr-1.6b-slerpx3", "stabilityai/stablelm-2-zephyr-1_6b"], "base_model": ["aipib/stablelm-2-zephyr-1.6b-slerpx3", "stabilityai/stablelm-2-zephyr-1_6b"]} | aipib/stablelm-2-zephyr-1.6b-slerpx9 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aipib/stablelm-2-zephyr-1.6b-slerpx3",
"stabilityai/stablelm-2-zephyr-1_6b",
"conversational",
"base_model:aipib/stablelm-2-zephyr-1.6b-slerpx3",
"base_model:stabilityai/stablelm-2-zephyr-1_6b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:04:18+00:00 |
text-classification | 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. -->
# uzbek-sentiment-analysis
It achieves the following results on the evaluation set:
- eval_loss: 0.6374
- eval_accuracy: {'accuracy': 0.7862348178137651}
- eval_f1score: {'f1': 0.7880364308572618}
- eval_runtime: 7.593
- eval_samples_per_second: 162.65
- eval_steps_per_second: 20.414
- step: 0
## Model description
**uzbek-sentiment-analysis** modelidan foydalanish.
```
from transformers import pipeline
pipe = pipeline('sentimennt-analysis', model='ai-nightcoder/uzbek-sentiment-analysis-v5')
text = "bu ovqatni men juda ham yaxshi ko'raman."
pipe(text)[0]['label']
```
## 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
- lr_scheduler_warmup_steps: 864
- num_epochs: 7
### Framework versions
- Transformers 4.40.1
- Pytorch 2.4.0.dev20240416+cu121
- Datasets 1.18.3
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "uzb-senAnalys", "results": []}]} | ai-nightcoder/uzbek-sentiment-analysis-v5 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:04:25+00:00 |
null | null | {} | NerdyCivilian/BitTensorSubnet25NCmodel | null | [
"region:us"
]
| null | 2024-04-27T09:06:30+00:00 |
|
null | null | {} | Kayfat/test | null | [
"region:us"
]
| null | 2024-04-27T09:06:31+00:00 |
|
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | swj0419/hp_all_STEP0000060 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:06:52+00:00 |
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. -->
# GUE_tf_1-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3459
- F1 Score: 0.8615
- Accuracy: 0.862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5684 | 0.83 | 200 | 0.5424 | 0.7348 | 0.735 |
| 0.5093 | 1.67 | 400 | 0.5335 | 0.7373 | 0.738 |
| 0.5015 | 2.5 | 600 | 0.5260 | 0.7449 | 0.745 |
| 0.4921 | 3.33 | 800 | 0.5188 | 0.7530 | 0.753 |
| 0.4902 | 4.17 | 1000 | 0.5204 | 0.7550 | 0.755 |
| 0.4862 | 5.0 | 1200 | 0.5195 | 0.7529 | 0.753 |
| 0.4821 | 5.83 | 1400 | 0.5137 | 0.7600 | 0.76 |
| 0.4818 | 6.67 | 1600 | 0.5135 | 0.7570 | 0.757 |
| 0.4792 | 7.5 | 1800 | 0.5119 | 0.7505 | 0.751 |
| 0.4753 | 8.33 | 2000 | 0.5054 | 0.7500 | 0.75 |
| 0.4736 | 9.17 | 2200 | 0.5057 | 0.7540 | 0.754 |
| 0.4724 | 10.0 | 2400 | 0.5199 | 0.7437 | 0.746 |
| 0.4713 | 10.83 | 2600 | 0.5090 | 0.7486 | 0.75 |
| 0.4669 | 11.67 | 2800 | 0.5079 | 0.7508 | 0.752 |
| 0.4627 | 12.5 | 3000 | 0.5011 | 0.7476 | 0.748 |
| 0.4695 | 13.33 | 3200 | 0.4947 | 0.7577 | 0.758 |
| 0.4604 | 14.17 | 3400 | 0.4935 | 0.7549 | 0.755 |
| 0.4592 | 15.0 | 3600 | 0.4941 | 0.7503 | 0.751 |
| 0.458 | 15.83 | 3800 | 0.4942 | 0.7556 | 0.756 |
| 0.461 | 16.67 | 4000 | 0.4932 | 0.7527 | 0.753 |
| 0.4574 | 17.5 | 4200 | 0.4921 | 0.7588 | 0.759 |
| 0.4557 | 18.33 | 4400 | 0.4946 | 0.7580 | 0.758 |
| 0.4584 | 19.17 | 4600 | 0.4909 | 0.7579 | 0.759 |
| 0.4572 | 20.0 | 4800 | 0.4881 | 0.7578 | 0.758 |
| 0.4559 | 20.83 | 5000 | 0.4891 | 0.7539 | 0.754 |
| 0.4528 | 21.67 | 5200 | 0.4879 | 0.7579 | 0.758 |
| 0.4516 | 22.5 | 5400 | 0.4909 | 0.7620 | 0.762 |
| 0.4528 | 23.33 | 5600 | 0.4865 | 0.7601 | 0.761 |
| 0.4515 | 24.17 | 5800 | 0.4891 | 0.7575 | 0.758 |
| 0.451 | 25.0 | 6000 | 0.4876 | 0.7600 | 0.76 |
| 0.4492 | 25.83 | 6200 | 0.4846 | 0.7639 | 0.764 |
| 0.4489 | 26.67 | 6400 | 0.4868 | 0.7638 | 0.764 |
| 0.4499 | 27.5 | 6600 | 0.4885 | 0.7610 | 0.761 |
| 0.4483 | 28.33 | 6800 | 0.4844 | 0.7619 | 0.762 |
| 0.4492 | 29.17 | 7000 | 0.4867 | 0.7690 | 0.769 |
| 0.4489 | 30.0 | 7200 | 0.4849 | 0.7588 | 0.759 |
| 0.449 | 30.83 | 7400 | 0.4846 | 0.7577 | 0.758 |
| 0.4459 | 31.67 | 7600 | 0.4840 | 0.7650 | 0.765 |
| 0.4501 | 32.5 | 7800 | 0.4830 | 0.7690 | 0.769 |
| 0.4428 | 33.33 | 8000 | 0.4846 | 0.7620 | 0.762 |
| 0.447 | 34.17 | 8200 | 0.4849 | 0.7610 | 0.761 |
| 0.4421 | 35.0 | 8400 | 0.4843 | 0.7640 | 0.764 |
| 0.4451 | 35.83 | 8600 | 0.4838 | 0.7629 | 0.763 |
| 0.4455 | 36.67 | 8800 | 0.4833 | 0.7619 | 0.762 |
| 0.4418 | 37.5 | 9000 | 0.4854 | 0.7596 | 0.76 |
| 0.4439 | 38.33 | 9200 | 0.4842 | 0.7650 | 0.765 |
| 0.4419 | 39.17 | 9400 | 0.4843 | 0.7650 | 0.765 |
| 0.4433 | 40.0 | 9600 | 0.4838 | 0.7650 | 0.765 |
| 0.4434 | 40.83 | 9800 | 0.4840 | 0.7660 | 0.766 |
| 0.4449 | 41.67 | 10000 | 0.4835 | 0.7640 | 0.764 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:07:34+00:00 |
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. -->
# GUE_tf_1-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3298
- F1 Score: 0.8619
- Accuracy: 0.862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.551 | 0.83 | 200 | 0.5412 | 0.7312 | 0.732 |
| 0.5002 | 1.67 | 400 | 0.5301 | 0.7351 | 0.736 |
| 0.4921 | 2.5 | 600 | 0.5180 | 0.754 | 0.754 |
| 0.4827 | 3.33 | 800 | 0.5099 | 0.7540 | 0.754 |
| 0.4809 | 4.17 | 1000 | 0.5067 | 0.7569 | 0.757 |
| 0.4756 | 5.0 | 1200 | 0.5040 | 0.7600 | 0.76 |
| 0.4702 | 5.83 | 1400 | 0.4994 | 0.7488 | 0.749 |
| 0.4675 | 6.67 | 1600 | 0.5032 | 0.7560 | 0.756 |
| 0.4654 | 7.5 | 1800 | 0.4949 | 0.7543 | 0.755 |
| 0.4591 | 8.33 | 2000 | 0.4884 | 0.7600 | 0.76 |
| 0.4596 | 9.17 | 2200 | 0.4867 | 0.7610 | 0.761 |
| 0.4546 | 10.0 | 2400 | 0.5111 | 0.7467 | 0.749 |
| 0.4533 | 10.83 | 2600 | 0.4952 | 0.7563 | 0.757 |
| 0.4488 | 11.67 | 2800 | 0.4906 | 0.7692 | 0.77 |
| 0.4441 | 12.5 | 3000 | 0.4886 | 0.7568 | 0.757 |
| 0.4511 | 13.33 | 3200 | 0.4865 | 0.7549 | 0.755 |
| 0.4427 | 14.17 | 3400 | 0.4851 | 0.7639 | 0.764 |
| 0.4406 | 15.0 | 3600 | 0.4846 | 0.7614 | 0.762 |
| 0.4391 | 15.83 | 3800 | 0.4855 | 0.7607 | 0.761 |
| 0.4402 | 16.67 | 4000 | 0.4885 | 0.7636 | 0.764 |
| 0.4367 | 17.5 | 4200 | 0.4848 | 0.7537 | 0.754 |
| 0.4332 | 18.33 | 4400 | 0.4897 | 0.7600 | 0.76 |
| 0.4354 | 19.17 | 4600 | 0.4884 | 0.7526 | 0.753 |
| 0.4339 | 20.0 | 4800 | 0.4865 | 0.7619 | 0.762 |
| 0.4316 | 20.83 | 5000 | 0.4902 | 0.7510 | 0.751 |
| 0.428 | 21.67 | 5200 | 0.4905 | 0.7560 | 0.756 |
| 0.4251 | 22.5 | 5400 | 0.4972 | 0.7580 | 0.758 |
| 0.4278 | 23.33 | 5600 | 0.4882 | 0.7535 | 0.754 |
| 0.4228 | 24.17 | 5800 | 0.4877 | 0.7546 | 0.755 |
| 0.424 | 25.0 | 6000 | 0.4889 | 0.7620 | 0.762 |
| 0.4206 | 25.83 | 6200 | 0.4881 | 0.7549 | 0.755 |
| 0.4185 | 26.67 | 6400 | 0.4890 | 0.7578 | 0.758 |
| 0.4208 | 27.5 | 6600 | 0.4916 | 0.7610 | 0.761 |
| 0.4165 | 28.33 | 6800 | 0.4913 | 0.7590 | 0.759 |
| 0.4178 | 29.17 | 7000 | 0.4933 | 0.7550 | 0.755 |
| 0.4165 | 30.0 | 7200 | 0.4884 | 0.7569 | 0.757 |
| 0.4167 | 30.83 | 7400 | 0.4927 | 0.7578 | 0.758 |
| 0.4124 | 31.67 | 7600 | 0.4917 | 0.7560 | 0.756 |
| 0.4153 | 32.5 | 7800 | 0.4938 | 0.7548 | 0.755 |
| 0.4094 | 33.33 | 8000 | 0.4925 | 0.7539 | 0.754 |
| 0.4107 | 34.17 | 8200 | 0.4925 | 0.7539 | 0.754 |
| 0.4082 | 35.0 | 8400 | 0.4948 | 0.7550 | 0.755 |
| 0.4083 | 35.83 | 8600 | 0.4925 | 0.7539 | 0.754 |
| 0.4091 | 36.67 | 8800 | 0.4920 | 0.7539 | 0.754 |
| 0.406 | 37.5 | 9000 | 0.4947 | 0.7557 | 0.756 |
| 0.4063 | 38.33 | 9200 | 0.4943 | 0.7500 | 0.75 |
| 0.4033 | 39.17 | 9400 | 0.4963 | 0.7549 | 0.755 |
| 0.4054 | 40.0 | 9600 | 0.4942 | 0.7509 | 0.751 |
| 0.4054 | 40.83 | 9800 | 0.4948 | 0.7520 | 0.752 |
| 0.4059 | 41.67 | 10000 | 0.4946 | 0.7529 | 0.753 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:08:23+00:00 |
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. -->
# GUE_tf_4-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3693
- F1 Score: 0.8407
- Accuracy: 0.841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.59 | 1.34 | 200 | 0.5461 | 0.7201 | 0.721 |
| 0.5222 | 2.68 | 400 | 0.5202 | 0.7409 | 0.743 |
| 0.4998 | 4.03 | 600 | 0.5086 | 0.7506 | 0.752 |
| 0.4889 | 5.37 | 800 | 0.4997 | 0.7549 | 0.755 |
| 0.4785 | 6.71 | 1000 | 0.4924 | 0.7504 | 0.751 |
| 0.4752 | 8.05 | 1200 | 0.4863 | 0.7640 | 0.764 |
| 0.4724 | 9.4 | 1400 | 0.4887 | 0.7569 | 0.757 |
| 0.4665 | 10.74 | 1600 | 0.4902 | 0.7612 | 0.762 |
| 0.464 | 12.08 | 1800 | 0.4884 | 0.7554 | 0.756 |
| 0.4621 | 13.42 | 2000 | 0.4813 | 0.7604 | 0.761 |
| 0.4569 | 14.77 | 2200 | 0.4858 | 0.7527 | 0.753 |
| 0.4589 | 16.11 | 2400 | 0.4810 | 0.7698 | 0.77 |
| 0.4503 | 17.45 | 2600 | 0.4844 | 0.7650 | 0.765 |
| 0.4517 | 18.79 | 2800 | 0.4822 | 0.7640 | 0.764 |
| 0.4497 | 20.13 | 3000 | 0.4806 | 0.7599 | 0.76 |
| 0.447 | 21.48 | 3200 | 0.4804 | 0.7619 | 0.762 |
| 0.4455 | 22.82 | 3400 | 0.4824 | 0.7620 | 0.762 |
| 0.4443 | 24.16 | 3600 | 0.4785 | 0.7670 | 0.767 |
| 0.4447 | 25.5 | 3800 | 0.4792 | 0.7607 | 0.761 |
| 0.4407 | 26.85 | 4000 | 0.4794 | 0.7619 | 0.762 |
| 0.4428 | 28.19 | 4200 | 0.4766 | 0.76 | 0.76 |
| 0.433 | 29.53 | 4400 | 0.4819 | 0.7580 | 0.758 |
| 0.4358 | 30.87 | 4600 | 0.4764 | 0.7540 | 0.754 |
| 0.4374 | 32.21 | 4800 | 0.4761 | 0.7620 | 0.762 |
| 0.4329 | 33.56 | 5000 | 0.4789 | 0.7608 | 0.761 |
| 0.4332 | 34.9 | 5200 | 0.4760 | 0.7650 | 0.765 |
| 0.4352 | 36.24 | 5400 | 0.4749 | 0.7580 | 0.758 |
| 0.4305 | 37.58 | 5600 | 0.4755 | 0.7660 | 0.766 |
| 0.4282 | 38.93 | 5800 | 0.4778 | 0.7640 | 0.764 |
| 0.432 | 40.27 | 6000 | 0.4793 | 0.7650 | 0.765 |
| 0.4275 | 41.61 | 6200 | 0.4757 | 0.7610 | 0.761 |
| 0.4299 | 42.95 | 6400 | 0.4744 | 0.7710 | 0.771 |
| 0.4305 | 44.3 | 6600 | 0.4707 | 0.7660 | 0.766 |
| 0.4213 | 45.64 | 6800 | 0.4745 | 0.7759 | 0.776 |
| 0.4278 | 46.98 | 7000 | 0.4744 | 0.7749 | 0.775 |
| 0.4298 | 48.32 | 7200 | 0.4700 | 0.7739 | 0.774 |
| 0.4233 | 49.66 | 7400 | 0.4752 | 0.7656 | 0.766 |
| 0.4221 | 51.01 | 7600 | 0.4700 | 0.77 | 0.77 |
| 0.4245 | 52.35 | 7800 | 0.4700 | 0.7710 | 0.771 |
| 0.4196 | 53.69 | 8000 | 0.4723 | 0.7709 | 0.771 |
| 0.4261 | 55.03 | 8200 | 0.4719 | 0.7729 | 0.773 |
| 0.4225 | 56.38 | 8400 | 0.4712 | 0.7750 | 0.775 |
| 0.4233 | 57.72 | 8600 | 0.4689 | 0.7629 | 0.763 |
| 0.4195 | 59.06 | 8800 | 0.4711 | 0.7740 | 0.774 |
| 0.4217 | 60.4 | 9000 | 0.4701 | 0.7740 | 0.774 |
| 0.4209 | 61.74 | 9200 | 0.4692 | 0.774 | 0.774 |
| 0.4226 | 63.09 | 9400 | 0.4697 | 0.7740 | 0.774 |
| 0.4231 | 64.43 | 9600 | 0.4691 | 0.774 | 0.774 |
| 0.4193 | 65.77 | 9800 | 0.4692 | 0.7730 | 0.773 |
| 0.419 | 67.11 | 10000 | 0.4695 | 0.7750 | 0.775 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:08:23+00:00 |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.53 +/- 0.92", "name": "mean_reward", "verified": false}]}]}]} | hossniper/a2c-PandaReachDense-v3 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T09:09:24+00:00 |
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. -->
# GUE_tf_4-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3687
- F1 Score: 0.8417
- 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5628 | 1.34 | 200 | 0.5121 | 0.7489 | 0.749 |
| 0.4873 | 2.68 | 400 | 0.4964 | 0.7567 | 0.757 |
| 0.4735 | 4.03 | 600 | 0.4893 | 0.7537 | 0.754 |
| 0.462 | 5.37 | 800 | 0.4913 | 0.7600 | 0.76 |
| 0.4521 | 6.71 | 1000 | 0.4901 | 0.7541 | 0.755 |
| 0.4472 | 8.05 | 1200 | 0.4818 | 0.7590 | 0.759 |
| 0.4426 | 9.4 | 1400 | 0.4839 | 0.7630 | 0.763 |
| 0.4381 | 10.74 | 1600 | 0.4853 | 0.7630 | 0.763 |
| 0.4333 | 12.08 | 1800 | 0.4911 | 0.7484 | 0.75 |
| 0.4325 | 13.42 | 2000 | 0.4765 | 0.7620 | 0.762 |
| 0.4258 | 14.77 | 2200 | 0.4880 | 0.7568 | 0.757 |
| 0.4269 | 16.11 | 2400 | 0.4817 | 0.7580 | 0.758 |
| 0.4187 | 17.45 | 2600 | 0.4903 | 0.7577 | 0.758 |
| 0.4195 | 18.79 | 2800 | 0.4845 | 0.7650 | 0.765 |
| 0.4162 | 20.13 | 3000 | 0.4854 | 0.7596 | 0.76 |
| 0.412 | 21.48 | 3200 | 0.4828 | 0.7660 | 0.766 |
| 0.4125 | 22.82 | 3400 | 0.4799 | 0.7650 | 0.765 |
| 0.407 | 24.16 | 3600 | 0.4763 | 0.7680 | 0.768 |
| 0.4063 | 25.5 | 3800 | 0.4766 | 0.7675 | 0.768 |
| 0.4002 | 26.85 | 4000 | 0.4831 | 0.7616 | 0.762 |
| 0.4043 | 28.19 | 4200 | 0.4708 | 0.7710 | 0.771 |
| 0.3958 | 29.53 | 4400 | 0.4887 | 0.7696 | 0.77 |
| 0.3966 | 30.87 | 4600 | 0.4698 | 0.7760 | 0.776 |
| 0.3957 | 32.21 | 4800 | 0.4749 | 0.7690 | 0.769 |
| 0.3926 | 33.56 | 5000 | 0.4746 | 0.7670 | 0.767 |
| 0.3906 | 34.9 | 5200 | 0.4731 | 0.7730 | 0.773 |
| 0.3899 | 36.24 | 5400 | 0.4698 | 0.772 | 0.772 |
| 0.3861 | 37.58 | 5600 | 0.4749 | 0.7699 | 0.77 |
| 0.3835 | 38.93 | 5800 | 0.4806 | 0.7708 | 0.771 |
| 0.3865 | 40.27 | 6000 | 0.4772 | 0.7750 | 0.775 |
| 0.3814 | 41.61 | 6200 | 0.4762 | 0.7678 | 0.768 |
| 0.3829 | 42.95 | 6400 | 0.4837 | 0.7625 | 0.763 |
| 0.3836 | 44.3 | 6600 | 0.4683 | 0.7730 | 0.773 |
| 0.374 | 45.64 | 6800 | 0.4824 | 0.7616 | 0.762 |
| 0.3813 | 46.98 | 7000 | 0.4806 | 0.7674 | 0.768 |
| 0.3827 | 48.32 | 7200 | 0.4709 | 0.7698 | 0.77 |
| 0.3766 | 49.66 | 7400 | 0.4799 | 0.7583 | 0.759 |
| 0.3727 | 51.01 | 7600 | 0.4728 | 0.7679 | 0.768 |
| 0.3734 | 52.35 | 7800 | 0.4731 | 0.7800 | 0.78 |
| 0.3689 | 53.69 | 8000 | 0.4813 | 0.7696 | 0.77 |
| 0.3741 | 55.03 | 8200 | 0.4813 | 0.7572 | 0.758 |
| 0.3718 | 56.38 | 8400 | 0.4761 | 0.7749 | 0.775 |
| 0.3716 | 57.72 | 8600 | 0.4722 | 0.7750 | 0.775 |
| 0.3703 | 59.06 | 8800 | 0.4741 | 0.7750 | 0.775 |
| 0.3718 | 60.4 | 9000 | 0.4755 | 0.7699 | 0.77 |
| 0.3668 | 61.74 | 9200 | 0.4724 | 0.7770 | 0.777 |
| 0.3688 | 63.09 | 9400 | 0.4729 | 0.7760 | 0.776 |
| 0.3705 | 64.43 | 9600 | 0.4728 | 0.7760 | 0.776 |
| 0.3666 | 65.77 | 9800 | 0.4741 | 0.7740 | 0.774 |
| 0.3681 | 67.11 | 10000 | 0.4740 | 0.7740 | 0.774 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:10:23+00:00 |
null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
| {"library_name": "peft"} | lekhapinninti/llama-2-7b-enhanced-10epoch | null | [
"peft",
"region:us"
]
| null | 2024-04-27T09:11:15+00:00 |
text-to-audio | 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. -->
# SpeechT5 Finetuned Vi - FredDYyy
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4772
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5534 | 10.06 | 1000 | 0.5056 |
| 0.528 | 20.13 | 2000 | 0.4843 |
| 0.5119 | 30.19 | 3000 | 0.4811 |
| 0.4994 | 40.25 | 4000 | 0.4772 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["vi"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "SpeechT5 Finetuned Vi - FredDYyy", "results": []}]} | FredDYyy/speecht5_finetuned_vi | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"vi",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:12:16+00:00 |
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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[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] | {"library_name": "transformers", "tags": []} | Mohamedshaaban2001/llama3_text2sql | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:12:27+00:00 |
text-generation | transformers |
# Function Calling Fine-tuned Phi 3 Instruct
This model is fine-tuned for function calling.
- The model is suitable for commercial use.
Check out other fine-tuned function calling models [here](https://huggingface.co/collections/Trelis/function-calling-v3-657199ecbe378693925c7915).
## Quick Server Setup
Runpod one click TGI template [here](https://runpod.io/console/deploy?template=h9pnbylvph&ref=jmfkcdio). [AWAITING [THIS FIX](https://github.com/huggingface/text-generation-inference/issues/1807)
- See this [YouTube Video](https://www.youtube.com/watch?v=hHn_cV5WUDI) for guidance on inference with this model.
Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel).
## Inference Scripts
See below for sample prompt format.
Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/):
- Support for TGI, vLLM and Llama.cpp
- Automate catching, handling and chaining of function calls.
## Prompt Format
### Using tokenizer.apply_chat_template
For an easier application of the prompt, you can set up as follows (note that the conversation below is complete, i.e. you need to remove assistant messages if you want to feed in the conversation to the model):
Set up `messages`:
```
[
{
"role": "function_metadata",
"content": "FUNCTION_METADATA"
},
{
"role": "user",
"content": "What is the current weather in London?"
},
{
"role": "function_call",
"content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}"
},
{
"role": "function_response",
"content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}"
},
{
"role": "assistant",
"content": "The current weather in London is Cloudy with a temperature of 15 Celsius"
}
]
```
with `FUNCTION_METADATA` as:
```
[
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "This function gets the current weather in a given city",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city, e.g., San Francisco"
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use."
}
},
"required": ["city"]
}
}
},
{
"type": "function",
"function": {
"name": "get_clothes",
"description": "This function provides a suggestion of clothes to wear based on the current weather",
"parameters": {
"type": "object",
"properties": {
"temperature": {
"type": "string",
"description": "The temperature, e.g., 15 C or 59 F"
},
"condition": {
"type": "string",
"description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'"
}
},
"required": ["temperature", "condition"]
}
}
}
]
```
and then apply the chat template to get a formatted prompt:
```
tokenizer = AutoTokenizer.from_pretrained('Trelis/Phi-3-mini-128k-instruct-function-calling', trust_remote_code=True)
prompt = tokenizer.apply_chat_template(prompt, tokenize=False)
```
If you are using a gated model, you need to first run:
```
pip install huggingface_hub
huggingface-cli login
```
### Manual Prompt:
```
<s><|function_metadata|>
[
{
"type": "function",
"function": {
"name": "get_stock_price",
"description": "Get the stock price of an array of stocks",
"parameters": {
"type": "object",
"properties": {
"names": {
"type": "array",
"items": {
"type": "string"
},
"description": "An array of stocks"
}
},
"required": [
"names"
]
}
}
},
{
"type": "function",
"function": {
"name": "get_big_stocks",
"description": "Get the names of the largest N stocks by market cap",
"parameters": {
"type": "object",
"properties": {
"number": {
"type": "integer",
"description": "The number of largest stocks to get the names of, e.g. 25"
},
"region": {
"type": "string",
"description": "The region to consider, can be \"US\" or \"World\"."
}
},
"required": [
"number"
]
}
}
}
]<|end|>
<|user|>
Get the names of the five largest stocks by market cap<|end|>
<|assistant|>
Correct Response:
{
"name": "get_big_stocks",
"arguments": {
"number": "5"
}
}
Generated Response:
```json
{
"function": "get_big_stocks",
"parameters": {
"number": 5,
"region": "World"
}
}
```<|end|><|endoftext|>
```
# Dataset
See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3).
~~~
The original repo card follows below.
~~~
## Model Summary
The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets.
This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties.
The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support.
After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures.
When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters.
Resources and Technical Documentation:
+ [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai)
+ Phi-3 ONNX: [128K](https://aka.ms/Phi3-mini-128k-instruct-onnx)
## Intended Uses
**Primary use cases**
The model is intended for commercial and research use in English. The model provides uses for applications which require:
1) Memory/compute constrained environments
2) Latency bound scenarios
3) Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## How to Use
Phi-3 Mini-128K-Instruct has been integrated in the development version (4.40.0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
### Tokenizer
Phi-3 Mini-128K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
### Chat Format
Given the nature of the training data, the Phi-3 Mini-128K-Instruct model is best suited for prompts using the chat format as follows.
You can provide the prompt as a question with a generic template as follow:
```markdown
<|user|>\nQuestion<|end|>\n<|assistant|>
```
For example:
```markdown
<|system|>
You are a helpful AI assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
```
where the model generates the text after `<|assistant|>`. In case of few-shots prompt, the prompt can be formatted as the following:
```markdown
<|system|>
You are a helpful AI assistant.<|end|>
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-128k-instruct",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
messages = [
{"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
*Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.*
## Responsible AI Considerations
Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
## Training
### Model
* Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
* Inputs: Text. It is best suited for prompts using chat format.
* Context length: 128K tokens
* GPUs: 512 H100-80G
* Training time: 7 days
* Training data: 3.3T tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.
### Datasets
Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of
1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;
2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);
3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
### Fine-tuning
A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/sample_finetune.py).
## Benchmarks
We report the results for Phi-3-Mini-128K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5.
All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.
As is now standard, we use few-shot prompts to evaluate the models, at temperature 0.
The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
The number of k–shot examples is listed per-benchmark.
| | Phi-3-Mini-128K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 |
|---|---|---|---|---|---|---|---|---|---|
| MMLU <br>5-Shot | 68.1 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.5 | 68.4 | 71.4 |
| HellaSwag <br> 5-Shot | 74.5 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 71.1 | 70.4 | 78.8 |
| ANLI <br> 7-Shot | 52.8 | 55.0 | 58.7 | 42.5 | 47.1 | 48.7 | 57.3 | 55.2 | 58.1 |
| GSM-8K <br> 0-Shot; CoT | 83.6 | 86.4 | 90.8 | 61.1 | 46.4 | 59.8 | 77.4 | 64.7 | 78.1 |
| MedQA <br> 2-Shot | 55.3 | 58.2 | 69.8 | 40.9 | 49.6 | 50.0 | 60.5 | 62.2 | 63.4 |
| AGIEval <br> 0-Shot | 36.9 | 45.0 | 49.7 | 29.8 | 35.1 | 42.1 | 42.0 | 45.2 | 48.4 |
| TriviaQA <br> 5-Shot | 57.1 | 59.1 | 73.3 | 45.2 | 72.3 | 75.2 | 67.7 | 82.2 | 85.8 |
| Arc-C <br> 10-Shot | 84.0 | 90.7 | 91.9 | 75.9 | 78.6 | 78.3 | 82.8 | 87.3 | 87.4 |
| Arc-E <br> 10-Shot | 95.2 | 97.1 | 98.0 | 88.5 | 90.6 | 91.4 | 93.4 | 95.6 | 96.3 |
| PIQA <br> 5-Shot | 83.6 | 87.8 | 88.2 | 60.2 | 77.7 | 78.1 | 75.7 | 86.0 | 86.6 |
| SociQA <br> 5-Shot | 76.1 | 79.0 | 79.4 | 68.3 | 74.6 | 65.5 | 73.9 | 75.9 | 68.3 |
| BigBench-Hard <br> 0-Shot | 71.5 | 75.0 | 82.5 | 59.4 | 57.3 | 59.6 | 51.5 | 69.7 | 68.32 |
| WinoGrande <br> 5-Shot | 72.5 | 82.5 | 81.2 | 54.7 | 54.2 | 55.6 | 65.0 | 62.0 | 68.8 |
| OpenBookQA <br> 10-Shot | 80.6 | 88.4 | 86.6 | 73.6 | 79.8 | 78.6 | 82.6 | 85.8 | 86.0 |
| BoolQ <br> 0-Shot | 78.7 | 82.9 | 86.5 | -- | 72.2 | 66.0 | 80.9 | 77.6 | 79.1 |
| CommonSenseQA <br> 10-Shot | 78.0 | 80.3 | 82.6 | 69.3 | 72.6 | 76.2 | 79 | 78.1 | 79.6 |
| TruthfulQA <br> 10-Shot | 63.2 | 68.1 | 74.8 | -- | 52.1 | 53.0 | 63.2 | 60.1 | 85.8 |
| HumanEval <br> 0-Shot | 57.9 | 59.1 | 54.7 | 47.0 | 28.0 | 34.1 | 60.4| 37.8 | 62.2 |
| MBPP <br> 3-Shot | 62.5 | 71.4 | 73.7 | 60.6 | 50.8 | 51.5 | 67.7 | 60.2 | 77.8 |
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
If you want to run the model on:
* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager"
* Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [128K](https://aka.ms/phi3-mini-128k-instruct-onnx)
## Cross Platform Support
ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-128K-Instruct ONNX model [here](https://aka.ms/phi3-mini-128k-instruct-onnx).
Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs.
Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.
Here are some of the optimized configurations we have added:
1. ONNX models for int4 DML: Quantized to int4 via AWQ
2. ONNX model for fp16 CUDA
3. ONNX model for int4 CUDA: Quantized to int4 via RTN
4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-128k/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
| {"language": ["en"], "tags": ["nlp", "code", "phi-3", "function-calling"], "datasets": ["Trelis/function_calling_v3"], "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}], "extra_gated_prompt": "Purchase access to this repo [HERE](https://buy.stripe.com/00g14Q7BX2HxaMU3dM)!"} | Trelis/Phi-3-mini-128k-instruct-function-calling | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"phi-3",
"function-calling",
"conversational",
"custom_code",
"en",
"dataset:Trelis/function_calling_v3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:12:46+00:00 |
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. -->
# GUE_tf_4-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3864
- F1 Score: 0.8470
- Accuracy: 0.847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5446 | 1.34 | 200 | 0.5061 | 0.7539 | 0.754 |
| 0.4745 | 2.68 | 400 | 0.4960 | 0.7550 | 0.755 |
| 0.46 | 4.03 | 600 | 0.4894 | 0.7570 | 0.757 |
| 0.4463 | 5.37 | 800 | 0.4906 | 0.7680 | 0.768 |
| 0.4369 | 6.71 | 1000 | 0.4858 | 0.7594 | 0.76 |
| 0.4298 | 8.05 | 1200 | 0.4772 | 0.7669 | 0.767 |
| 0.4221 | 9.4 | 1400 | 0.4799 | 0.7670 | 0.767 |
| 0.4139 | 10.74 | 1600 | 0.4825 | 0.7699 | 0.77 |
| 0.405 | 12.08 | 1800 | 0.4862 | 0.7709 | 0.772 |
| 0.402 | 13.42 | 2000 | 0.4680 | 0.7759 | 0.776 |
| 0.3922 | 14.77 | 2200 | 0.4783 | 0.7717 | 0.772 |
| 0.3907 | 16.11 | 2400 | 0.4696 | 0.7770 | 0.777 |
| 0.3787 | 17.45 | 2600 | 0.4953 | 0.7679 | 0.769 |
| 0.3742 | 18.79 | 2800 | 0.4803 | 0.7708 | 0.771 |
| 0.3672 | 20.13 | 3000 | 0.4769 | 0.7635 | 0.764 |
| 0.3638 | 21.48 | 3200 | 0.4701 | 0.7710 | 0.771 |
| 0.3609 | 22.82 | 3400 | 0.4716 | 0.7920 | 0.792 |
| 0.3513 | 24.16 | 3600 | 0.4730 | 0.7749 | 0.775 |
| 0.3451 | 25.5 | 3800 | 0.4650 | 0.7899 | 0.79 |
| 0.3382 | 26.85 | 4000 | 0.4742 | 0.7809 | 0.781 |
| 0.3404 | 28.19 | 4200 | 0.4721 | 0.7830 | 0.783 |
| 0.329 | 29.53 | 4400 | 0.4892 | 0.7890 | 0.789 |
| 0.3272 | 30.87 | 4600 | 0.4692 | 0.7859 | 0.786 |
| 0.3209 | 32.21 | 4800 | 0.4715 | 0.7860 | 0.786 |
| 0.3175 | 33.56 | 5000 | 0.4721 | 0.7850 | 0.785 |
| 0.3174 | 34.9 | 5200 | 0.4652 | 0.7950 | 0.795 |
| 0.3104 | 36.24 | 5400 | 0.4744 | 0.8030 | 0.803 |
| 0.3056 | 37.58 | 5600 | 0.4802 | 0.7930 | 0.793 |
| 0.2987 | 38.93 | 5800 | 0.4793 | 0.7970 | 0.797 |
| 0.3024 | 40.27 | 6000 | 0.4931 | 0.7970 | 0.797 |
| 0.2957 | 41.61 | 6200 | 0.4825 | 0.7930 | 0.793 |
| 0.2936 | 42.95 | 6400 | 0.4902 | 0.7988 | 0.799 |
| 0.2988 | 44.3 | 6600 | 0.4628 | 0.8110 | 0.811 |
| 0.2818 | 45.64 | 6800 | 0.4771 | 0.8099 | 0.81 |
| 0.2869 | 46.98 | 7000 | 0.4816 | 0.8080 | 0.808 |
| 0.2858 | 48.32 | 7200 | 0.4766 | 0.8080 | 0.808 |
| 0.282 | 49.66 | 7400 | 0.4757 | 0.8140 | 0.814 |
| 0.2777 | 51.01 | 7600 | 0.4768 | 0.8140 | 0.814 |
| 0.2759 | 52.35 | 7800 | 0.4744 | 0.8120 | 0.812 |
| 0.2717 | 53.69 | 8000 | 0.4882 | 0.8139 | 0.814 |
| 0.2754 | 55.03 | 8200 | 0.4802 | 0.8100 | 0.81 |
| 0.2708 | 56.38 | 8400 | 0.4811 | 0.8090 | 0.809 |
| 0.2688 | 57.72 | 8600 | 0.4794 | 0.8120 | 0.812 |
| 0.2689 | 59.06 | 8800 | 0.4737 | 0.8100 | 0.81 |
| 0.2691 | 60.4 | 9000 | 0.4791 | 0.8100 | 0.81 |
| 0.2624 | 61.74 | 9200 | 0.4763 | 0.8100 | 0.81 |
| 0.2646 | 63.09 | 9400 | 0.4772 | 0.8090 | 0.809 |
| 0.2655 | 64.43 | 9600 | 0.4742 | 0.8090 | 0.809 |
| 0.2621 | 65.77 | 9800 | 0.4772 | 0.8100 | 0.81 |
| 0.2644 | 67.11 | 10000 | 0.4775 | 0.8100 | 0.81 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:14:06+00:00 |
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. -->
# GUE_tf_3-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5776
- F1 Score: 0.6912
- Accuracy: 0.693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6531 | 0.93 | 200 | 0.5997 | 0.6821 | 0.682 |
| 0.6217 | 1.87 | 400 | 0.5819 | 0.7028 | 0.704 |
| 0.6118 | 2.8 | 600 | 0.5774 | 0.7052 | 0.712 |
| 0.6033 | 3.74 | 800 | 0.5708 | 0.7083 | 0.712 |
| 0.6024 | 4.67 | 1000 | 0.5686 | 0.7052 | 0.71 |
| 0.5982 | 5.61 | 1200 | 0.5673 | 0.7096 | 0.711 |
| 0.5949 | 6.54 | 1400 | 0.5660 | 0.7076 | 0.713 |
| 0.5958 | 7.48 | 1600 | 0.5637 | 0.7140 | 0.715 |
| 0.5906 | 8.41 | 1800 | 0.5617 | 0.7106 | 0.713 |
| 0.5942 | 9.35 | 2000 | 0.5620 | 0.7079 | 0.711 |
| 0.5893 | 10.28 | 2200 | 0.5633 | 0.7069 | 0.707 |
| 0.5895 | 11.21 | 2400 | 0.5607 | 0.7079 | 0.709 |
| 0.5879 | 12.15 | 2600 | 0.5598 | 0.6995 | 0.704 |
| 0.583 | 13.08 | 2800 | 0.5589 | 0.6958 | 0.698 |
| 0.585 | 14.02 | 3000 | 0.5598 | 0.6969 | 0.698 |
| 0.5828 | 14.95 | 3200 | 0.5587 | 0.6971 | 0.698 |
| 0.5819 | 15.89 | 3400 | 0.5577 | 0.6997 | 0.702 |
| 0.5805 | 16.82 | 3600 | 0.5583 | 0.6985 | 0.699 |
| 0.5793 | 17.76 | 3800 | 0.5572 | 0.7018 | 0.703 |
| 0.5823 | 18.69 | 4000 | 0.5592 | 0.7010 | 0.702 |
| 0.5784 | 19.63 | 4200 | 0.5560 | 0.7010 | 0.704 |
| 0.5795 | 20.56 | 4400 | 0.5555 | 0.7061 | 0.707 |
| 0.5767 | 21.5 | 4600 | 0.5589 | 0.7071 | 0.707 |
| 0.5766 | 22.43 | 4800 | 0.5561 | 0.7007 | 0.702 |
| 0.576 | 23.36 | 5000 | 0.5554 | 0.6993 | 0.7 |
| 0.578 | 24.3 | 5200 | 0.5559 | 0.7010 | 0.701 |
| 0.5709 | 25.23 | 5400 | 0.5539 | 0.7016 | 0.704 |
| 0.5789 | 26.17 | 5600 | 0.5548 | 0.6954 | 0.696 |
| 0.5741 | 27.1 | 5800 | 0.5530 | 0.7041 | 0.706 |
| 0.5717 | 28.04 | 6000 | 0.5527 | 0.6980 | 0.699 |
| 0.5728 | 28.97 | 6200 | 0.5558 | 0.6991 | 0.699 |
| 0.5722 | 29.91 | 6400 | 0.5534 | 0.6896 | 0.69 |
| 0.5715 | 30.84 | 6600 | 0.5523 | 0.6996 | 0.701 |
| 0.5727 | 31.78 | 6800 | 0.5546 | 0.7020 | 0.702 |
| 0.569 | 32.71 | 7000 | 0.5517 | 0.6977 | 0.699 |
| 0.575 | 33.64 | 7200 | 0.5521 | 0.7022 | 0.703 |
| 0.5654 | 34.58 | 7400 | 0.5549 | 0.7031 | 0.703 |
| 0.5721 | 35.51 | 7600 | 0.5528 | 0.6968 | 0.697 |
| 0.5697 | 36.45 | 7800 | 0.5530 | 0.6970 | 0.697 |
| 0.5706 | 37.38 | 8000 | 0.5516 | 0.7014 | 0.702 |
| 0.5661 | 38.32 | 8200 | 0.5512 | 0.7051 | 0.706 |
| 0.5694 | 39.25 | 8400 | 0.5517 | 0.7006 | 0.701 |
| 0.5693 | 40.19 | 8600 | 0.5522 | 0.7018 | 0.702 |
| 0.5673 | 41.12 | 8800 | 0.5519 | 0.7047 | 0.705 |
| 0.5704 | 42.06 | 9000 | 0.5516 | 0.7037 | 0.704 |
| 0.5677 | 42.99 | 9200 | 0.5529 | 0.7010 | 0.701 |
| 0.5659 | 43.93 | 9400 | 0.5528 | 0.7010 | 0.701 |
| 0.5667 | 44.86 | 9600 | 0.5515 | 0.7047 | 0.705 |
| 0.5699 | 45.79 | 9800 | 0.5517 | 0.7028 | 0.703 |
| 0.566 | 46.73 | 10000 | 0.5516 | 0.7037 | 0.704 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:14:17+00:00 |
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. -->
# GUE_tf_3-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5640
- F1 Score: 0.7002
- Accuracy: 0.702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6405 | 0.93 | 200 | 0.5815 | 0.7028 | 0.703 |
| 0.6073 | 1.87 | 400 | 0.5741 | 0.6990 | 0.699 |
| 0.5987 | 2.8 | 600 | 0.5656 | 0.6945 | 0.699 |
| 0.5918 | 3.74 | 800 | 0.5675 | 0.6931 | 0.693 |
| 0.5901 | 4.67 | 1000 | 0.5640 | 0.6985 | 0.699 |
| 0.5856 | 5.61 | 1200 | 0.5597 | 0.6996 | 0.701 |
| 0.583 | 6.54 | 1400 | 0.5606 | 0.6974 | 0.698 |
| 0.5809 | 7.48 | 1600 | 0.5585 | 0.6988 | 0.699 |
| 0.5751 | 8.41 | 1800 | 0.5614 | 0.6960 | 0.696 |
| 0.5795 | 9.35 | 2000 | 0.5564 | 0.6888 | 0.69 |
| 0.5732 | 10.28 | 2200 | 0.5624 | 0.6919 | 0.692 |
| 0.572 | 11.21 | 2400 | 0.5557 | 0.6976 | 0.698 |
| 0.5702 | 12.15 | 2600 | 0.5540 | 0.7051 | 0.708 |
| 0.5654 | 13.08 | 2800 | 0.5560 | 0.7032 | 0.706 |
| 0.5664 | 14.02 | 3000 | 0.5555 | 0.6941 | 0.694 |
| 0.5641 | 14.95 | 3200 | 0.5522 | 0.7020 | 0.704 |
| 0.5617 | 15.89 | 3400 | 0.5516 | 0.6965 | 0.698 |
| 0.5634 | 16.82 | 3600 | 0.5520 | 0.6992 | 0.7 |
| 0.5594 | 17.76 | 3800 | 0.5521 | 0.7050 | 0.706 |
| 0.5629 | 18.69 | 4000 | 0.5572 | 0.6941 | 0.694 |
| 0.559 | 19.63 | 4200 | 0.5512 | 0.7055 | 0.707 |
| 0.5572 | 20.56 | 4400 | 0.5529 | 0.7066 | 0.707 |
| 0.5557 | 21.5 | 4600 | 0.5617 | 0.6886 | 0.689 |
| 0.5534 | 22.43 | 4800 | 0.5520 | 0.7043 | 0.705 |
| 0.5543 | 23.36 | 5000 | 0.5559 | 0.6931 | 0.693 |
| 0.5556 | 24.3 | 5200 | 0.5616 | 0.6881 | 0.689 |
| 0.5479 | 25.23 | 5400 | 0.5555 | 0.7116 | 0.713 |
| 0.5562 | 26.17 | 5600 | 0.5532 | 0.6989 | 0.699 |
| 0.5504 | 27.1 | 5800 | 0.5518 | 0.7026 | 0.703 |
| 0.5485 | 28.04 | 6000 | 0.5508 | 0.7076 | 0.708 |
| 0.549 | 28.97 | 6200 | 0.5579 | 0.6950 | 0.695 |
| 0.5492 | 29.91 | 6400 | 0.5541 | 0.6881 | 0.688 |
| 0.5486 | 30.84 | 6600 | 0.5512 | 0.7086 | 0.709 |
| 0.5459 | 31.78 | 6800 | 0.5546 | 0.7010 | 0.701 |
| 0.5452 | 32.71 | 7000 | 0.5507 | 0.7026 | 0.703 |
| 0.5513 | 33.64 | 7200 | 0.5512 | 0.7019 | 0.702 |
| 0.5405 | 34.58 | 7400 | 0.5569 | 0.7007 | 0.701 |
| 0.5481 | 35.51 | 7600 | 0.5539 | 0.6960 | 0.696 |
| 0.5438 | 36.45 | 7800 | 0.5553 | 0.7010 | 0.701 |
| 0.5462 | 37.38 | 8000 | 0.5521 | 0.7021 | 0.702 |
| 0.5381 | 38.32 | 8200 | 0.5530 | 0.7027 | 0.703 |
| 0.5452 | 39.25 | 8400 | 0.5521 | 0.7049 | 0.705 |
| 0.5435 | 40.19 | 8600 | 0.5530 | 0.7061 | 0.706 |
| 0.5421 | 41.12 | 8800 | 0.5523 | 0.7081 | 0.708 |
| 0.5444 | 42.06 | 9000 | 0.5523 | 0.7061 | 0.706 |
| 0.5412 | 42.99 | 9200 | 0.5534 | 0.7040 | 0.704 |
| 0.5397 | 43.93 | 9400 | 0.5545 | 0.7020 | 0.702 |
| 0.54 | 44.86 | 9600 | 0.5522 | 0.7081 | 0.708 |
| 0.5435 | 45.79 | 9800 | 0.5522 | 0.7051 | 0.705 |
| 0.5374 | 46.73 | 10000 | 0.5527 | 0.7051 | 0.705 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:14:47+00:00 |
null | null | {"license": "openrail"} | Danikdsa/Jaehyun | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T09:15:23+00:00 |
|
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. -->
# GUE_tf_3-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5519
- F1 Score: 0.7047
- Accuracy: 0.707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.633 | 0.93 | 200 | 0.5725 | 0.6982 | 0.7 |
| 0.6014 | 1.87 | 400 | 0.5741 | 0.6990 | 0.699 |
| 0.5924 | 2.8 | 600 | 0.5617 | 0.6975 | 0.7 |
| 0.5827 | 3.74 | 800 | 0.5667 | 0.6898 | 0.69 |
| 0.5795 | 4.67 | 1000 | 0.5591 | 0.7027 | 0.703 |
| 0.5729 | 5.61 | 1200 | 0.5554 | 0.6992 | 0.701 |
| 0.5701 | 6.54 | 1400 | 0.5541 | 0.6910 | 0.691 |
| 0.5662 | 7.48 | 1600 | 0.5472 | 0.7045 | 0.705 |
| 0.5594 | 8.41 | 1800 | 0.5548 | 0.6988 | 0.699 |
| 0.5627 | 9.35 | 2000 | 0.5485 | 0.7087 | 0.71 |
| 0.5563 | 10.28 | 2200 | 0.5543 | 0.6946 | 0.695 |
| 0.5531 | 11.21 | 2400 | 0.5550 | 0.6999 | 0.7 |
| 0.5509 | 12.15 | 2600 | 0.5459 | 0.7176 | 0.72 |
| 0.5445 | 13.08 | 2800 | 0.5521 | 0.7114 | 0.715 |
| 0.5438 | 14.02 | 3000 | 0.5548 | 0.6971 | 0.697 |
| 0.5398 | 14.95 | 3200 | 0.5420 | 0.7167 | 0.719 |
| 0.5368 | 15.89 | 3400 | 0.5412 | 0.7232 | 0.724 |
| 0.5377 | 16.82 | 3600 | 0.5468 | 0.7190 | 0.72 |
| 0.5319 | 17.76 | 3800 | 0.5422 | 0.7119 | 0.712 |
| 0.5338 | 18.69 | 4000 | 0.5490 | 0.7121 | 0.712 |
| 0.5271 | 19.63 | 4200 | 0.5370 | 0.7279 | 0.729 |
| 0.5251 | 20.56 | 4400 | 0.5496 | 0.7140 | 0.714 |
| 0.5222 | 21.5 | 4600 | 0.5531 | 0.7012 | 0.702 |
| 0.519 | 22.43 | 4800 | 0.5439 | 0.7123 | 0.713 |
| 0.5176 | 23.36 | 5000 | 0.5595 | 0.7037 | 0.704 |
| 0.5182 | 24.3 | 5200 | 0.5581 | 0.7063 | 0.707 |
| 0.509 | 25.23 | 5400 | 0.5625 | 0.7206 | 0.721 |
| 0.5159 | 26.17 | 5600 | 0.5478 | 0.7210 | 0.721 |
| 0.5073 | 27.1 | 5800 | 0.5500 | 0.7169 | 0.717 |
| 0.5057 | 28.04 | 6000 | 0.5538 | 0.7231 | 0.723 |
| 0.5062 | 28.97 | 6200 | 0.5637 | 0.7089 | 0.709 |
| 0.5031 | 29.91 | 6400 | 0.5569 | 0.7060 | 0.706 |
| 0.5021 | 30.84 | 6600 | 0.5489 | 0.7230 | 0.723 |
| 0.4994 | 31.78 | 6800 | 0.5557 | 0.7075 | 0.708 |
| 0.4997 | 32.71 | 7000 | 0.5454 | 0.7251 | 0.725 |
| 0.5018 | 33.64 | 7200 | 0.5508 | 0.7090 | 0.709 |
| 0.4901 | 34.58 | 7400 | 0.5590 | 0.7042 | 0.705 |
| 0.4969 | 35.51 | 7600 | 0.5553 | 0.6997 | 0.7 |
| 0.4927 | 36.45 | 7800 | 0.5554 | 0.7110 | 0.711 |
| 0.4946 | 37.38 | 8000 | 0.5510 | 0.7250 | 0.725 |
| 0.4847 | 38.32 | 8200 | 0.5567 | 0.7220 | 0.722 |
| 0.4907 | 39.25 | 8400 | 0.5609 | 0.7181 | 0.718 |
| 0.4907 | 40.19 | 8600 | 0.5583 | 0.7090 | 0.709 |
| 0.4889 | 41.12 | 8800 | 0.5537 | 0.7151 | 0.715 |
| 0.4887 | 42.06 | 9000 | 0.5550 | 0.7161 | 0.716 |
| 0.4849 | 42.99 | 9200 | 0.5617 | 0.7045 | 0.705 |
| 0.4858 | 43.93 | 9400 | 0.5630 | 0.7075 | 0.708 |
| 0.4832 | 44.86 | 9600 | 0.5610 | 0.7089 | 0.709 |
| 0.4855 | 45.79 | 9800 | 0.5608 | 0.7130 | 0.713 |
| 0.4797 | 46.73 | 10000 | 0.5614 | 0.7130 | 0.713 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:15:27+00:00 |
text-generation | transformers |
# 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. -->
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:** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/9lg2om0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:15:32+00:00 |
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. -->
# GUE_tf_2-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4637
- F1 Score: 0.7756
- Accuracy: 0.776
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6103 | 1.34 | 200 | 0.5705 | 0.7010 | 0.706 |
| 0.5646 | 2.68 | 400 | 0.5452 | 0.7186 | 0.72 |
| 0.5482 | 4.03 | 600 | 0.5374 | 0.7304 | 0.731 |
| 0.5393 | 5.37 | 800 | 0.5322 | 0.7329 | 0.733 |
| 0.5362 | 6.71 | 1000 | 0.5356 | 0.7327 | 0.734 |
| 0.531 | 8.05 | 1200 | 0.5296 | 0.7240 | 0.724 |
| 0.5279 | 9.4 | 1400 | 0.5301 | 0.7346 | 0.736 |
| 0.5262 | 10.74 | 1600 | 0.5260 | 0.7382 | 0.739 |
| 0.5228 | 12.08 | 1800 | 0.5310 | 0.7290 | 0.729 |
| 0.5222 | 13.42 | 2000 | 0.5255 | 0.7429 | 0.743 |
| 0.5218 | 14.77 | 2200 | 0.5217 | 0.7375 | 0.738 |
| 0.5166 | 16.11 | 2400 | 0.5247 | 0.7310 | 0.731 |
| 0.5191 | 17.45 | 2600 | 0.5207 | 0.7416 | 0.742 |
| 0.5124 | 18.79 | 2800 | 0.5205 | 0.7358 | 0.736 |
| 0.513 | 20.13 | 3000 | 0.5195 | 0.7387 | 0.739 |
| 0.5144 | 21.48 | 3200 | 0.5215 | 0.7350 | 0.735 |
| 0.5133 | 22.82 | 3400 | 0.5188 | 0.7386 | 0.739 |
| 0.5098 | 24.16 | 3600 | 0.5224 | 0.7216 | 0.724 |
| 0.5089 | 25.5 | 3800 | 0.5217 | 0.7334 | 0.735 |
| 0.5116 | 26.85 | 4000 | 0.5212 | 0.7337 | 0.735 |
| 0.5063 | 28.19 | 4200 | 0.5193 | 0.7402 | 0.741 |
| 0.5061 | 29.53 | 4400 | 0.5192 | 0.7369 | 0.737 |
| 0.5035 | 30.87 | 4600 | 0.5179 | 0.7376 | 0.738 |
| 0.5063 | 32.21 | 4800 | 0.5171 | 0.7399 | 0.74 |
| 0.5034 | 33.56 | 5000 | 0.5159 | 0.7390 | 0.739 |
| 0.5041 | 34.9 | 5200 | 0.5169 | 0.7367 | 0.737 |
| 0.4995 | 36.24 | 5400 | 0.5166 | 0.7373 | 0.738 |
| 0.5057 | 37.58 | 5600 | 0.5148 | 0.7368 | 0.737 |
| 0.5023 | 38.93 | 5800 | 0.5132 | 0.7328 | 0.733 |
| 0.4981 | 40.27 | 6000 | 0.5151 | 0.7347 | 0.735 |
| 0.4999 | 41.61 | 6200 | 0.5156 | 0.7318 | 0.732 |
| 0.5008 | 42.95 | 6400 | 0.5155 | 0.7359 | 0.736 |
| 0.4969 | 44.3 | 6600 | 0.5140 | 0.7379 | 0.738 |
| 0.5002 | 45.64 | 6800 | 0.5142 | 0.7347 | 0.735 |
| 0.4971 | 46.98 | 7000 | 0.5144 | 0.7344 | 0.735 |
| 0.4969 | 48.32 | 7200 | 0.5154 | 0.7390 | 0.739 |
| 0.4972 | 49.66 | 7400 | 0.5130 | 0.7326 | 0.733 |
| 0.4942 | 51.01 | 7600 | 0.5142 | 0.7336 | 0.734 |
| 0.4953 | 52.35 | 7800 | 0.5142 | 0.7308 | 0.731 |
| 0.496 | 53.69 | 8000 | 0.5134 | 0.7306 | 0.731 |
| 0.4917 | 55.03 | 8200 | 0.5138 | 0.7349 | 0.735 |
| 0.4971 | 56.38 | 8400 | 0.5137 | 0.7360 | 0.736 |
| 0.4939 | 57.72 | 8600 | 0.5136 | 0.7319 | 0.732 |
| 0.4927 | 59.06 | 8800 | 0.5131 | 0.7347 | 0.735 |
| 0.4911 | 60.4 | 9000 | 0.5138 | 0.7349 | 0.735 |
| 0.4974 | 61.74 | 9200 | 0.5128 | 0.7386 | 0.739 |
| 0.4955 | 63.09 | 9400 | 0.5128 | 0.7348 | 0.735 |
| 0.4937 | 64.43 | 9600 | 0.5128 | 0.7358 | 0.736 |
| 0.4942 | 65.77 | 9800 | 0.5129 | 0.7319 | 0.732 |
| 0.4914 | 67.11 | 10000 | 0.5129 | 0.7367 | 0.737 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:15:38+00:00 |
image-classification | 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold4
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0255
- Accuracy: 0.6605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5422 | 1.0 | 924 | 1.4764 | 0.5075 |
| 1.2973 | 2.0 | 1848 | 1.2000 | 0.5841 |
| 1.161 | 3.0 | 2772 | 1.1897 | 0.5857 |
| 1.1967 | 4.0 | 3696 | 1.1001 | 0.6172 |
| 1.1015 | 5.0 | 4620 | 1.0649 | 0.6299 |
| 0.8173 | 6.0 | 5544 | 1.0497 | 0.6407 |
| 0.8829 | 7.0 | 6468 | 1.0344 | 0.6491 |
| 1.0073 | 8.0 | 7392 | 1.0447 | 0.6513 |
| 0.9166 | 9.0 | 8316 | 1.0398 | 0.6513 |
| 0.9029 | 10.0 | 9240 | 1.0255 | 0.6605 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-tiny-patch4-window16-256", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6605256028176646, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold4 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-tiny-patch4-window16-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:16:01+00:00 |
token-classification | transformers | {} | manish1103125/NER-Task3 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:16:11+00:00 |
|
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. -->
# GUE_tf_2-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4840
- F1 Score: 0.7789
- Accuracy: 0.779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5782 | 1.34 | 200 | 0.5356 | 0.7289 | 0.73 |
| 0.5351 | 2.68 | 400 | 0.5267 | 0.7290 | 0.729 |
| 0.5225 | 4.03 | 600 | 0.5212 | 0.7367 | 0.737 |
| 0.5121 | 5.37 | 800 | 0.5203 | 0.7398 | 0.74 |
| 0.5081 | 6.71 | 1000 | 0.5223 | 0.7339 | 0.734 |
| 0.5003 | 8.05 | 1200 | 0.5231 | 0.7223 | 0.723 |
| 0.4927 | 9.4 | 1400 | 0.5197 | 0.7304 | 0.731 |
| 0.4902 | 10.74 | 1600 | 0.5129 | 0.7330 | 0.733 |
| 0.4847 | 12.08 | 1800 | 0.5212 | 0.7369 | 0.737 |
| 0.4829 | 13.42 | 2000 | 0.5225 | 0.7319 | 0.732 |
| 0.4787 | 14.77 | 2200 | 0.5221 | 0.7252 | 0.727 |
| 0.4707 | 16.11 | 2400 | 0.5179 | 0.7360 | 0.736 |
| 0.4727 | 17.45 | 2600 | 0.5146 | 0.7391 | 0.74 |
| 0.4656 | 18.79 | 2800 | 0.5099 | 0.7438 | 0.744 |
| 0.4607 | 20.13 | 3000 | 0.5138 | 0.7420 | 0.742 |
| 0.4593 | 21.48 | 3200 | 0.5310 | 0.7332 | 0.734 |
| 0.4585 | 22.82 | 3400 | 0.5255 | 0.7360 | 0.736 |
| 0.4515 | 24.16 | 3600 | 0.5233 | 0.7305 | 0.731 |
| 0.4471 | 25.5 | 3800 | 0.5207 | 0.7337 | 0.734 |
| 0.4437 | 26.85 | 4000 | 0.5266 | 0.7284 | 0.729 |
| 0.4406 | 28.19 | 4200 | 0.5231 | 0.7439 | 0.744 |
| 0.4365 | 29.53 | 4400 | 0.5399 | 0.7289 | 0.729 |
| 0.4307 | 30.87 | 4600 | 0.5266 | 0.7349 | 0.735 |
| 0.4318 | 32.21 | 4800 | 0.5375 | 0.7370 | 0.737 |
| 0.4284 | 33.56 | 5000 | 0.5447 | 0.7284 | 0.729 |
| 0.4262 | 34.9 | 5200 | 0.5368 | 0.7268 | 0.727 |
| 0.4179 | 36.24 | 5400 | 0.5416 | 0.7370 | 0.737 |
| 0.4219 | 37.58 | 5600 | 0.5390 | 0.7280 | 0.728 |
| 0.4163 | 38.93 | 5800 | 0.5324 | 0.7250 | 0.725 |
| 0.4091 | 40.27 | 6000 | 0.5540 | 0.7244 | 0.725 |
| 0.4094 | 41.61 | 6200 | 0.5440 | 0.7210 | 0.721 |
| 0.4092 | 42.95 | 6400 | 0.5670 | 0.7265 | 0.727 |
| 0.4018 | 44.3 | 6600 | 0.5576 | 0.7259 | 0.726 |
| 0.4016 | 45.64 | 6800 | 0.5645 | 0.7208 | 0.721 |
| 0.3998 | 46.98 | 7000 | 0.5529 | 0.7220 | 0.722 |
| 0.3936 | 48.32 | 7200 | 0.5677 | 0.7248 | 0.725 |
| 0.3943 | 49.66 | 7400 | 0.5703 | 0.7210 | 0.721 |
| 0.3911 | 51.01 | 7600 | 0.5648 | 0.7268 | 0.727 |
| 0.3914 | 52.35 | 7800 | 0.5692 | 0.7240 | 0.724 |
| 0.3902 | 53.69 | 8000 | 0.5635 | 0.7260 | 0.726 |
| 0.386 | 55.03 | 8200 | 0.5661 | 0.7210 | 0.721 |
| 0.3887 | 56.38 | 8400 | 0.5718 | 0.7197 | 0.72 |
| 0.386 | 57.72 | 8600 | 0.5625 | 0.728 | 0.728 |
| 0.3826 | 59.06 | 8800 | 0.5694 | 0.7300 | 0.73 |
| 0.382 | 60.4 | 9000 | 0.5785 | 0.7249 | 0.725 |
| 0.3845 | 61.74 | 9200 | 0.5721 | 0.7300 | 0.73 |
| 0.3811 | 63.09 | 9400 | 0.5740 | 0.7270 | 0.727 |
| 0.3789 | 64.43 | 9600 | 0.5734 | 0.7270 | 0.727 |
| 0.3826 | 65.77 | 9800 | 0.5740 | 0.7299 | 0.73 |
| 0.3779 | 67.11 | 10000 | 0.5736 | 0.7270 | 0.727 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:16:17+00:00 |
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. -->
# GUE_tf_2-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4616
- F1 Score: 0.7690
- Accuracy: 0.769
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5929 | 1.34 | 200 | 0.5474 | 0.7144 | 0.716 |
| 0.5432 | 2.68 | 400 | 0.5322 | 0.7269 | 0.727 |
| 0.5315 | 4.03 | 600 | 0.5275 | 0.7375 | 0.738 |
| 0.523 | 5.37 | 800 | 0.5251 | 0.7375 | 0.738 |
| 0.5217 | 6.71 | 1000 | 0.5242 | 0.7355 | 0.736 |
| 0.5142 | 8.05 | 1200 | 0.5255 | 0.7330 | 0.733 |
| 0.5094 | 9.4 | 1400 | 0.5210 | 0.7315 | 0.733 |
| 0.5081 | 10.74 | 1600 | 0.5183 | 0.7321 | 0.733 |
| 0.5047 | 12.08 | 1800 | 0.5229 | 0.7330 | 0.733 |
| 0.5024 | 13.42 | 2000 | 0.5240 | 0.7420 | 0.742 |
| 0.5022 | 14.77 | 2200 | 0.5214 | 0.7279 | 0.73 |
| 0.4951 | 16.11 | 2400 | 0.5208 | 0.7379 | 0.738 |
| 0.498 | 17.45 | 2600 | 0.5174 | 0.7313 | 0.732 |
| 0.492 | 18.79 | 2800 | 0.5164 | 0.7292 | 0.73 |
| 0.4895 | 20.13 | 3000 | 0.5178 | 0.7295 | 0.73 |
| 0.4909 | 21.48 | 3200 | 0.5239 | 0.7277 | 0.728 |
| 0.4924 | 22.82 | 3400 | 0.5168 | 0.7370 | 0.737 |
| 0.4859 | 24.16 | 3600 | 0.5166 | 0.7272 | 0.728 |
| 0.4853 | 25.5 | 3800 | 0.5152 | 0.7418 | 0.742 |
| 0.4848 | 26.85 | 4000 | 0.5201 | 0.7312 | 0.732 |
| 0.483 | 28.19 | 4200 | 0.5161 | 0.7389 | 0.739 |
| 0.4807 | 29.53 | 4400 | 0.5236 | 0.7410 | 0.741 |
| 0.478 | 30.87 | 4600 | 0.5208 | 0.7370 | 0.737 |
| 0.4806 | 32.21 | 4800 | 0.5207 | 0.7390 | 0.739 |
| 0.4756 | 33.56 | 5000 | 0.5268 | 0.7375 | 0.738 |
| 0.4764 | 34.9 | 5200 | 0.5229 | 0.7360 | 0.736 |
| 0.4715 | 36.24 | 5400 | 0.5242 | 0.7289 | 0.729 |
| 0.4771 | 37.58 | 5600 | 0.5217 | 0.7350 | 0.735 |
| 0.4734 | 38.93 | 5800 | 0.5210 | 0.7310 | 0.731 |
| 0.4694 | 40.27 | 6000 | 0.5226 | 0.7296 | 0.73 |
| 0.4719 | 41.61 | 6200 | 0.5233 | 0.7300 | 0.73 |
| 0.4713 | 42.95 | 6400 | 0.5263 | 0.7389 | 0.739 |
| 0.4654 | 44.3 | 6600 | 0.5233 | 0.7360 | 0.736 |
| 0.4686 | 45.64 | 6800 | 0.5257 | 0.7319 | 0.732 |
| 0.4656 | 46.98 | 7000 | 0.5219 | 0.7330 | 0.733 |
| 0.4644 | 48.32 | 7200 | 0.5268 | 0.7320 | 0.732 |
| 0.4656 | 49.66 | 7400 | 0.5214 | 0.7330 | 0.733 |
| 0.4624 | 51.01 | 7600 | 0.5266 | 0.732 | 0.732 |
| 0.4622 | 52.35 | 7800 | 0.5281 | 0.732 | 0.732 |
| 0.4641 | 53.69 | 8000 | 0.5234 | 0.7300 | 0.73 |
| 0.4595 | 55.03 | 8200 | 0.5247 | 0.7290 | 0.729 |
| 0.4636 | 56.38 | 8400 | 0.5268 | 0.7369 | 0.737 |
| 0.4602 | 57.72 | 8600 | 0.5228 | 0.7300 | 0.73 |
| 0.4601 | 59.06 | 8800 | 0.5238 | 0.7320 | 0.732 |
| 0.4571 | 60.4 | 9000 | 0.5287 | 0.7319 | 0.732 |
| 0.463 | 61.74 | 9200 | 0.5243 | 0.7270 | 0.727 |
| 0.4595 | 63.09 | 9400 | 0.5258 | 0.7280 | 0.728 |
| 0.4601 | 64.43 | 9600 | 0.5250 | 0.7310 | 0.731 |
| 0.4589 | 65.77 | 9800 | 0.5258 | 0.7290 | 0.729 |
| 0.4566 | 67.11 | 10000 | 0.5255 | 0.7280 | 0.728 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:16:17+00:00 |
null | null | {"license": "apache-2.0"} | KN64/Knuckles06-RVCv2 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T09:16:34+00:00 |
|
text-generation | transformers |
# stablelm-2-zephyr-1.6b-slerpx10
stablelm-2-zephyr-1.6b-slerpx10 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aipib/stablelm-2-zephyr-1.6b-slerpx9](https://huggingface.co/aipib/stablelm-2-zephyr-1.6b-slerpx9)
* [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: aipib/stablelm-2-zephyr-1.6b-slerpx9
layer_range: [0, 24]
- model: stabilityai/stablelm-2-zephyr-1_6b
layer_range: [0, 24]
merge_method: slerp
base_model: aipib/stablelm-2-zephyr-1.6b-slerpx9
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/stablelm-2-zephyr-1.6b-slerpx10"
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", "aipib/stablelm-2-zephyr-1.6b-slerpx9", "stabilityai/stablelm-2-zephyr-1_6b"], "base_model": ["aipib/stablelm-2-zephyr-1.6b-slerpx9", "stabilityai/stablelm-2-zephyr-1_6b"]} | aipib/stablelm-2-zephyr-1.6b-slerpx10 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aipib/stablelm-2-zephyr-1.6b-slerpx9",
"stabilityai/stablelm-2-zephyr-1_6b",
"conversational",
"base_model:aipib/stablelm-2-zephyr-1.6b-slerpx9",
"base_model:stabilityai/stablelm-2-zephyr-1_6b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:16:35+00:00 |
null | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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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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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Andro9669/gemma-7b-ner | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:16:42+00:00 |
null | null | {"license": "wtfpl"} | loganhuggingface/kyliehardinghfjone | null | [
"license:wtfpl",
"region:us"
]
| null | 2024-04-27T09:17:05+00:00 |
|
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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[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]
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**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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"} | nizamudma/Enlighten_Instruct_Mistral | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
]
| null | 2024-04-27T09:19:19+00:00 |
text-generation | 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. -->
# 0.001_4iters_bs128_nodpo_only4w_iter_3
This model is a fine-tuned version of [ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_iter_2](https://huggingface.co/ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_iter_2) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_iter_2", "model-index": [{"name": "0.001_4iters_bs128_nodpo_only4w_iter_3", "results": []}]} | ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:19:52+00:00 |
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. -->
# GUE_virus_covid-seqsight_16384_512_22M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6944
- F1 Score: 0.3658
- Accuracy: 0.3593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.184 | 0.35 | 200 | 2.1806 | 0.0984 | 0.1410 |
| 2.1758 | 0.7 | 400 | 2.1685 | 0.1274 | 0.1546 |
| 2.1622 | 1.05 | 600 | 2.1553 | 0.1425 | 0.1584 |
| 2.1497 | 1.4 | 800 | 2.1472 | 0.1332 | 0.1660 |
| 2.1433 | 1.75 | 1000 | 2.1346 | 0.1575 | 0.1816 |
| 2.1378 | 2.09 | 1200 | 2.1314 | 0.1536 | 0.1814 |
| 2.1273 | 2.44 | 1400 | 2.1180 | 0.1825 | 0.1977 |
| 2.1109 | 2.79 | 1600 | 2.0769 | 0.2212 | 0.2410 |
| 2.0785 | 3.14 | 1800 | 2.0240 | 0.2335 | 0.2455 |
| 2.0481 | 3.49 | 2000 | 2.0070 | 0.2210 | 0.2366 |
| 2.0342 | 3.84 | 2200 | 1.9759 | 0.2551 | 0.2642 |
| 2.0152 | 4.19 | 2400 | 1.9467 | 0.2583 | 0.2641 |
| 1.9896 | 4.54 | 2600 | 1.9240 | 0.2606 | 0.2705 |
| 1.9737 | 4.89 | 2800 | 1.9050 | 0.2755 | 0.2839 |
| 1.9589 | 5.24 | 3000 | 1.8869 | 0.2745 | 0.2808 |
| 1.9479 | 5.58 | 3200 | 1.8701 | 0.2895 | 0.2978 |
| 1.9272 | 5.93 | 3400 | 1.8528 | 0.2860 | 0.2997 |
| 1.9178 | 6.28 | 3600 | 1.8392 | 0.2965 | 0.3055 |
| 1.9001 | 6.63 | 3800 | 1.8250 | 0.3025 | 0.3105 |
| 1.8966 | 6.98 | 4000 | 1.8116 | 0.3258 | 0.3264 |
| 1.883 | 7.33 | 4200 | 1.8034 | 0.3098 | 0.3157 |
| 1.8767 | 7.68 | 4400 | 1.7900 | 0.3284 | 0.3247 |
| 1.8691 | 8.03 | 4600 | 1.7822 | 0.3320 | 0.3329 |
| 1.8488 | 8.38 | 4800 | 1.7719 | 0.3205 | 0.3213 |
| 1.8547 | 8.73 | 5000 | 1.7690 | 0.3199 | 0.3298 |
| 1.8439 | 9.08 | 5200 | 1.7554 | 0.3440 | 0.3359 |
| 1.8313 | 9.42 | 5400 | 1.7501 | 0.3355 | 0.3386 |
| 1.8329 | 9.77 | 5600 | 1.7463 | 0.3311 | 0.3323 |
| 1.8221 | 10.12 | 5800 | 1.7415 | 0.3416 | 0.3376 |
| 1.8163 | 10.47 | 6000 | 1.7370 | 0.3497 | 0.3462 |
| 1.8151 | 10.82 | 6200 | 1.7373 | 0.3455 | 0.3403 |
| 1.8001 | 11.17 | 6400 | 1.7307 | 0.3544 | 0.3462 |
| 1.8027 | 11.52 | 6600 | 1.7220 | 0.3595 | 0.3556 |
| 1.7998 | 11.87 | 6800 | 1.7185 | 0.3544 | 0.3508 |
| 1.7991 | 12.22 | 7000 | 1.7180 | 0.3561 | 0.3515 |
| 1.7894 | 12.57 | 7200 | 1.7138 | 0.3513 | 0.3510 |
| 1.7912 | 12.91 | 7400 | 1.7112 | 0.3590 | 0.3571 |
| 1.786 | 13.26 | 7600 | 1.7099 | 0.3555 | 0.3554 |
| 1.7827 | 13.61 | 7800 | 1.7071 | 0.3570 | 0.3547 |
| 1.7851 | 13.96 | 8000 | 1.7045 | 0.3632 | 0.3584 |
| 1.7762 | 14.31 | 8200 | 1.7039 | 0.3622 | 0.3583 |
| 1.7805 | 14.66 | 8400 | 1.7010 | 0.3603 | 0.3550 |
| 1.7831 | 15.01 | 8600 | 1.7021 | 0.3588 | 0.3558 |
| 1.7868 | 15.36 | 8800 | 1.6991 | 0.3655 | 0.3590 |
| 1.7638 | 15.71 | 9000 | 1.7003 | 0.3590 | 0.3572 |
| 1.773 | 16.06 | 9200 | 1.6972 | 0.3630 | 0.3606 |
| 1.7804 | 16.4 | 9400 | 1.6979 | 0.3610 | 0.3589 |
| 1.7643 | 16.75 | 9600 | 1.6970 | 0.3617 | 0.3592 |
| 1.7752 | 17.1 | 9800 | 1.6966 | 0.3654 | 0.3624 |
| 1.7749 | 17.45 | 10000 | 1.6965 | 0.3646 | 0.3609 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_22M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_22M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:20:14+00:00 |
null | null | {} | Ytsheeqee/my-first-creation | null | [
"region:us"
]
| null | 2024-04-27T09:20:24+00:00 |
|
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. -->
# GUE_virus_covid-seqsight_16384_512_22M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3754
- F1 Score: 0.4766
- Accuracy: 0.4791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.183 | 0.35 | 200 | 2.1753 | 0.1143 | 0.1463 |
| 2.168 | 0.7 | 400 | 2.1554 | 0.1426 | 0.1638 |
| 2.1485 | 1.05 | 600 | 2.1399 | 0.1613 | 0.1782 |
| 2.1112 | 1.4 | 800 | 2.0532 | 0.1978 | 0.2303 |
| 2.0402 | 1.75 | 1000 | 1.9520 | 0.2582 | 0.2706 |
| 1.9812 | 2.09 | 1200 | 1.8951 | 0.2654 | 0.2796 |
| 1.9312 | 2.44 | 1400 | 1.8417 | 0.3154 | 0.3140 |
| 1.8974 | 2.79 | 1600 | 1.8054 | 0.3053 | 0.3178 |
| 1.8532 | 3.14 | 1800 | 1.7496 | 0.3306 | 0.3415 |
| 1.8195 | 3.49 | 2000 | 1.7189 | 0.3322 | 0.3394 |
| 1.7957 | 3.84 | 2200 | 1.6947 | 0.3599 | 0.3673 |
| 1.7691 | 4.19 | 2400 | 1.6777 | 0.3578 | 0.3557 |
| 1.7459 | 4.54 | 2600 | 1.6312 | 0.3753 | 0.3800 |
| 1.7264 | 4.89 | 2800 | 1.5969 | 0.3991 | 0.3980 |
| 1.7065 | 5.24 | 3000 | 1.5840 | 0.3877 | 0.3929 |
| 1.6869 | 5.58 | 3200 | 1.5602 | 0.4092 | 0.4092 |
| 1.667 | 5.93 | 3400 | 1.5537 | 0.4011 | 0.4070 |
| 1.658 | 6.28 | 3600 | 1.5381 | 0.4159 | 0.4101 |
| 1.6335 | 6.63 | 3800 | 1.5251 | 0.4197 | 0.4226 |
| 1.6272 | 6.98 | 4000 | 1.5171 | 0.4268 | 0.4243 |
| 1.6189 | 7.33 | 4200 | 1.5043 | 0.4287 | 0.4277 |
| 1.6091 | 7.68 | 4400 | 1.4997 | 0.4320 | 0.4278 |
| 1.6083 | 8.03 | 4600 | 1.4909 | 0.4262 | 0.4298 |
| 1.592 | 8.38 | 4800 | 1.4845 | 0.4329 | 0.4339 |
| 1.5949 | 8.73 | 5000 | 1.4783 | 0.4324 | 0.4365 |
| 1.5811 | 9.08 | 5200 | 1.4724 | 0.4457 | 0.4391 |
| 1.5776 | 9.42 | 5400 | 1.4589 | 0.4407 | 0.4475 |
| 1.5626 | 9.77 | 5600 | 1.4548 | 0.4399 | 0.4434 |
| 1.5606 | 10.12 | 5800 | 1.4455 | 0.4404 | 0.4388 |
| 1.5556 | 10.47 | 6000 | 1.4411 | 0.4515 | 0.4530 |
| 1.5497 | 10.82 | 6200 | 1.4415 | 0.4445 | 0.4424 |
| 1.5319 | 11.17 | 6400 | 1.4394 | 0.4505 | 0.4475 |
| 1.5385 | 11.52 | 6600 | 1.4339 | 0.4481 | 0.4517 |
| 1.5371 | 11.87 | 6800 | 1.4245 | 0.4490 | 0.4537 |
| 1.5316 | 12.22 | 7000 | 1.4166 | 0.4562 | 0.4582 |
| 1.5238 | 12.57 | 7200 | 1.4126 | 0.4631 | 0.4619 |
| 1.5213 | 12.91 | 7400 | 1.4115 | 0.4552 | 0.4576 |
| 1.5128 | 13.26 | 7600 | 1.4098 | 0.4589 | 0.4621 |
| 1.5101 | 13.61 | 7800 | 1.4081 | 0.4622 | 0.4638 |
| 1.514 | 13.96 | 8000 | 1.3972 | 0.4661 | 0.4668 |
| 1.5063 | 14.31 | 8200 | 1.3991 | 0.4621 | 0.4664 |
| 1.506 | 14.66 | 8400 | 1.3933 | 0.4733 | 0.4689 |
| 1.5063 | 15.01 | 8600 | 1.3922 | 0.4724 | 0.4741 |
| 1.5076 | 15.36 | 8800 | 1.3908 | 0.4719 | 0.4677 |
| 1.4882 | 15.71 | 9000 | 1.3858 | 0.4737 | 0.4767 |
| 1.4974 | 16.06 | 9200 | 1.3873 | 0.4726 | 0.4723 |
| 1.493 | 16.4 | 9400 | 1.3830 | 0.4748 | 0.4758 |
| 1.494 | 16.75 | 9600 | 1.3838 | 0.4776 | 0.4780 |
| 1.4954 | 17.1 | 9800 | 1.3843 | 0.4761 | 0.4773 |
| 1.4972 | 17.45 | 10000 | 1.3839 | 0.4762 | 0.4763 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_22M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_22M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:20:26+00:00 |
null | null | {} | eersonmez/Test1 | null | [
"region:us"
]
| null | 2024-04-27T09:20:55+00:00 |
|
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. -->
# GUE_virus_covid-seqsight_16384_512_22M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1612
- F1 Score: 0.5677
- Accuracy: 0.5543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.1812 | 0.35 | 200 | 2.1672 | 0.1391 | 0.1588 |
| 2.1584 | 0.7 | 400 | 2.1443 | 0.1573 | 0.1804 |
| 2.1066 | 1.05 | 600 | 2.0052 | 0.2448 | 0.2480 |
| 1.9632 | 1.4 | 800 | 1.8515 | 0.2767 | 0.2916 |
| 1.8723 | 1.75 | 1000 | 1.7607 | 0.3235 | 0.3249 |
| 1.8149 | 2.09 | 1200 | 1.6958 | 0.3517 | 0.3556 |
| 1.7576 | 2.44 | 1400 | 1.6498 | 0.3824 | 0.3734 |
| 1.7077 | 2.79 | 1600 | 1.6015 | 0.3790 | 0.3794 |
| 1.6659 | 3.14 | 1800 | 1.5665 | 0.3971 | 0.3976 |
| 1.6344 | 3.49 | 2000 | 1.5340 | 0.4192 | 0.4187 |
| 1.6228 | 3.84 | 2200 | 1.5033 | 0.4299 | 0.4351 |
| 1.5867 | 4.19 | 2400 | 1.4659 | 0.4603 | 0.4440 |
| 1.5656 | 4.54 | 2600 | 1.4402 | 0.4638 | 0.4561 |
| 1.549 | 4.89 | 2800 | 1.4424 | 0.4612 | 0.4551 |
| 1.5203 | 5.24 | 3000 | 1.4167 | 0.4709 | 0.4663 |
| 1.5152 | 5.58 | 3200 | 1.3929 | 0.4737 | 0.4711 |
| 1.4973 | 5.93 | 3400 | 1.3898 | 0.4785 | 0.4786 |
| 1.4863 | 6.28 | 3600 | 1.3780 | 0.4960 | 0.4768 |
| 1.4616 | 6.63 | 3800 | 1.3671 | 0.4853 | 0.4819 |
| 1.4598 | 6.98 | 4000 | 1.3554 | 0.4896 | 0.4841 |
| 1.4535 | 7.33 | 4200 | 1.3384 | 0.4983 | 0.4926 |
| 1.4274 | 7.68 | 4400 | 1.3435 | 0.4958 | 0.4863 |
| 1.4269 | 8.03 | 4600 | 1.3118 | 0.5042 | 0.5036 |
| 1.4097 | 8.38 | 4800 | 1.2962 | 0.5136 | 0.5104 |
| 1.4067 | 8.73 | 5000 | 1.2886 | 0.5196 | 0.5116 |
| 1.3773 | 9.08 | 5200 | 1.2697 | 0.5287 | 0.5199 |
| 1.3703 | 9.42 | 5400 | 1.2614 | 0.5225 | 0.5249 |
| 1.3633 | 9.77 | 5600 | 1.2499 | 0.5329 | 0.5260 |
| 1.3536 | 10.12 | 5800 | 1.2483 | 0.5298 | 0.5203 |
| 1.3502 | 10.47 | 6000 | 1.2393 | 0.5280 | 0.5205 |
| 1.336 | 10.82 | 6200 | 1.2345 | 0.5471 | 0.5327 |
| 1.3166 | 11.17 | 6400 | 1.2281 | 0.5455 | 0.5351 |
| 1.3271 | 11.52 | 6600 | 1.2199 | 0.5461 | 0.5312 |
| 1.315 | 11.87 | 6800 | 1.2072 | 0.5432 | 0.5356 |
| 1.3081 | 12.22 | 7000 | 1.1983 | 0.5519 | 0.5420 |
| 1.2946 | 12.57 | 7200 | 1.1989 | 0.5517 | 0.5391 |
| 1.2906 | 12.91 | 7400 | 1.1945 | 0.5510 | 0.5392 |
| 1.2855 | 13.26 | 7600 | 1.1876 | 0.5470 | 0.5353 |
| 1.2782 | 13.61 | 7800 | 1.1817 | 0.5538 | 0.5428 |
| 1.2784 | 13.96 | 8000 | 1.1800 | 0.5596 | 0.5508 |
| 1.2633 | 14.31 | 8200 | 1.1792 | 0.5572 | 0.5470 |
| 1.2692 | 14.66 | 8400 | 1.1727 | 0.5657 | 0.5514 |
| 1.2673 | 15.01 | 8600 | 1.1761 | 0.5507 | 0.5427 |
| 1.2663 | 15.36 | 8800 | 1.1671 | 0.5626 | 0.5515 |
| 1.2495 | 15.71 | 9000 | 1.1669 | 0.5583 | 0.5490 |
| 1.2595 | 16.06 | 9200 | 1.1688 | 0.5560 | 0.5456 |
| 1.2517 | 16.4 | 9400 | 1.1642 | 0.5562 | 0.5473 |
| 1.255 | 16.75 | 9600 | 1.1635 | 0.5570 | 0.5473 |
| 1.2494 | 17.1 | 9800 | 1.1625 | 0.5585 | 0.5485 |
| 1.2504 | 17.45 | 10000 | 1.1629 | 0.5581 | 0.5487 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_22M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_22M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
]
| null | 2024-04-27T09:21:01+00:00 |
text2text-generation | 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. -->
# CS505_COQE_viT5_total_Instruction0_SAOPL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- 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 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_SAOPL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_SAOPL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:21:29+00:00 |
text2text-generation | 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. -->
# nepali_t5
This model is a fine-tuned version of [rujengelal/nepali_t5](https://huggingface.co/rujengelal/nepali_t5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6633
- Bleu: 6.3134
- Gen Len: 15.9835
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|
| 3.0928 | 1.0 | 17734 | 2.8330 | 5.4935 | 15.9053 |
| 3.101 | 2.0 | 35468 | 2.8127 | 5.5409 | 15.8787 |
| 3.0165 | 3.0 | 53202 | 2.7814 | 5.6622 | 15.9238 |
| 2.9973 | 4.0 | 70936 | 2.7532 | 5.8108 | 15.8996 |
| 2.8885 | 5.0 | 88670 | 2.7294 | 5.9077 | 15.8805 |
| 2.8114 | 6.0 | 106404 | 2.7074 | 6.1401 | 15.9749 |
| 2.7791 | 7.0 | 124138 | 2.6905 | 6.1567 | 15.9531 |
| 2.7729 | 8.0 | 141872 | 2.6782 | 6.1865 | 15.9688 |
| 2.7128 | 9.0 | 159606 | 2.6699 | 6.2233 | 16.063 |
| 2.7398 | 10.0 | 177340 | 2.6633 | 6.3134 | 15.9835 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "rujengelal/nepali_t5", "model-index": [{"name": "nepali_t5", "results": []}]} | rujengelal/nepali_t5 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:rujengelal/nepali_t5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:26:01+00:00 |
null | null | {"license": "openrail"} | coreliastreet/OliviaRodrigo | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T09:28:06+00:00 |
|
null | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cackerman/rewrites_gemma7_ft_ds2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:29:30+00:00 |
reinforcement-learning | stable-baselines3 |
# **MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicy** 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": "MlpPolicy", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "240.47 +/- 16.76", "name": "mean_reward", "verified": false}]}]}]} | zermelozf/rl-course | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T09:30:00+00:00 |
null | null | Apakah Optalite Tablet?
Optalite harga ialah kapsul suplemen pemakanan yang direka khas untuk memberikan sokongan menyeluruh untuk kesihatan mata. Formula termajunya mengandungi gabungan sinergistik vitamin, mineral dan antioksidan, dipilih dengan teliti untuk menyuburkan dan melindungi mata daripada degenerasi yang berkaitan dengan usia dan tekanan alam sekitar.
Laman web rasmi:<a href="https://www.nutritionsee.com/eroborkey">www.Optalite.com</a>
<p><a href="https://www.nutritionsee.com/eroborkey"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Eroboost-Turkey-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/eroborkey">Beli sekarang!! Klik pautan di bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang... Cepat</a>
Laman web rasmi:<a href="https://www.nutritionsee.com/eroborkey">www.Optalite.com</a> | {"license": "apache-2.0"} | EroboostTurkey/Eroboost | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T09:30:30+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-70B-v1.0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF/resolve/main/JSL-MedLlama-3-70B-v1.0.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "cc-by-nc-nd-4.0", "library_name": "transformers", "tags": ["llama-3-70b", "sft", "medical"], "base_model": "johnsnowlabs/JSL-MedLlama-3-70B-v1.0", "quantized_by": "mradermacher"} | mradermacher/JSL-MedLlama-3-70B-v1.0-i1-GGUF | null | [
"transformers",
"gguf",
"llama-3-70b",
"sft",
"medical",
"en",
"base_model:johnsnowlabs/JSL-MedLlama-3-70B-v1.0",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:30:41+00:00 |
null | null | Co to jest Hemopro Gel?
Hemopro Gel Opinie to żel do stosowania miejscowego, specjalnie zaprojektowany w celu łagodzenia objawów hemoroidów, w tym bólu, swędzenia, pieczenia i obrzęku. Zaawansowana formuła łączy w sobie naturalne składniki znane ze swoich właściwości łagodzących i leczniczych, zapewniając szybką i skuteczną ulgę dotkniętym obszarom.
Oficjalna strona internetowa:<a href="https://www.nutritionsee.com/hemlplan">www.HemoproGel.com</a>
<p><a href="https://www.nutritionsee.com/hemlplan"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Hemopro-Gel-Poland.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/hemlplan">Kup Teraz!! Kliknij poniższy link, aby uzyskać więcej informacji i uzyskać teraz 50% zniżki... Pospiesz się</a>
Oficjalna strona internetowa:<a href="https://www.nutritionsee.com/hemlplan">www.HemoproGel.com</a> | {"license": "apache-2.0"} | Hemopro/HemoproGel | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T09:30:47+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B
# From original readme
# Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
| {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "Nous-Hermes-2-Yi-34B"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/Nous-Hermes-2-Yi-34B-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"Nous-Hermes-2-Yi-34B",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-27T09:31:39+00:00 |
null | null | {} | Fadedfragger/ElixirProject | null | [
"region:us"
]
| null | 2024-04-27T09:31:59+00:00 |
|
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-nli_cot_qkv
This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.426 | 0.9998 | 1196 | 0.4255 |
| 0.3664 | 1.9996 | 2392 | 0.4365 |
| 0.3221 | 2.9994 | 3588 | 0.4455 |
| 0.2804 | 4.0 | 4785 | 0.4577 |
| 0.2403 | 4.9998 | 5981 | 0.4719 |
| 0.2001 | 5.9996 | 7177 | 0.4948 |
| 0.1643 | 6.9994 | 8373 | 0.5278 |
| 0.1305 | 8.0 | 9570 | 0.5634 |
| 0.1011 | 8.9998 | 10766 | 0.6095 |
| 0.0768 | 9.9996 | 11962 | 0.6621 |
| 0.0577 | 10.9994 | 13158 | 0.7225 |
| 0.0445 | 11.9975 | 14352 | 0.7749 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-v0.1-GPTQ", "model-index": [{"name": "mistral-7b-nli_cot_qkv", "results": []}]} | jd0g/mistral-7b-nli_cot_qkv | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T09:33:09+00:00 |
null | null | {"license": "mit"} | AdamMashaka/hisabati-yetu | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T09:38:03+00:00 |
|
null | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | la-min/GENI_GPT_Health | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:38:45+00:00 |
text-classification | 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. -->
# robust_llm_pythia-410m_mz-131f_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-131f_IMDB", "results": []}]} | AlignmentResearch/robust_llm_pythia-410m_mz-131f_IMDB | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:40:16+00:00 |
null | null | {} | AkilanSelvam/my_awesome_model | null | [
"region:us"
]
| null | 2024-04-27T09:42:10+00:00 |
|
null | transformers |
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- 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. -->
[More Information Needed]
## 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | zandfj/LLaMA2-7B-Chat_-sft-sft-moren_042716 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:43:13+00:00 |
text2text-generation | 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. -->
# CS505_COQE_viT5_total_Instruction0_SAOPL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_SAOPL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_SAOPL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:45:06+00:00 |
null | null | {} | Katochh/falcon-rw-1b-GenAI-task2-ModelB | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-27T09:45:44+00:00 |
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