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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-28 18:27:53
| downloads
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| likes
int64 0
11.7k
| library_name
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andersonalmada/icc2024
|
andersonalmada
| 2023-10-19T12:54:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-19T12:52:36Z |
Dataset for the experiment with OTel at ICC 2024.
|
Jonglee/GenerAd-AI
|
Jonglee
| 2023-10-19T12:42:16Z | 4 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloomz-3b",
"base_model:adapter:bigscience/bloomz-3b",
"region:us"
] | null | 2023-10-19T12:41:12Z |
---
library_name: peft
base_model: bigscience/bloomz-3b
---
# 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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
matamedar1/Andre_Dussollier_RVC
|
matamedar1
| 2023-10-19T12:41:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-10-19T12:37:53Z |
---
license: openrail
---
- v2, 40k
- rmvpe
- 210 Epochs
- Voix Française
- Dataset 17min
|
gokuls/hBERTv2_new_pretrain_48_ver2_mnli
|
gokuls
| 2023-10-19T12:39:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokuls/bert_12_layer_model_v2_complete_training_new_48",
"base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_48",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-18T14:58:05Z |
---
language:
- en
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_new_pretrain_48_ver2_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.318246541903987
---
<!-- 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. -->
# hBERTv2_new_pretrain_48_ver2_mnli
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0986
- Accuracy: 0.3182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1022 | 1.0 | 6136 | 1.0991 | 0.3182 |
| 1.0989 | 2.0 | 12272 | 1.0987 | 0.3182 |
| 1.0987 | 3.0 | 18408 | 1.0986 | 0.3182 |
| 1.0987 | 4.0 | 24544 | 1.0986 | 0.3182 |
| 1.0986 | 5.0 | 30680 | 1.0986 | 0.3274 |
| 1.0987 | 6.0 | 36816 | 1.0986 | 0.3274 |
| 1.0986 | 7.0 | 42952 | 1.0986 | 0.3182 |
| 1.0986 | 8.0 | 49088 | 1.0986 | 0.3182 |
| 1.0986 | 9.0 | 55224 | 1.0986 | 0.3182 |
| 1.0986 | 10.0 | 61360 | 1.0986 | 0.3182 |
| 1.0986 | 11.0 | 67496 | 1.0986 | 0.3182 |
| 1.0986 | 12.0 | 73632 | 1.0986 | 0.3182 |
| 1.0986 | 13.0 | 79768 | 1.0986 | 0.3274 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
|
malibanekg/llama-2-hotel-reservations
|
malibanekg
| 2023-10-19T12:32:32Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"endpoints_compatible",
"region:us"
] | null | 2023-08-30T22:55:33Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 1.13.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
sophiaaaa/flan-t5-base-finetuned-smcp
|
sophiaaaa
| 2023-10-19T12:29:17Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-04T15:28:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-finetuned-smcp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-finetuned-smcp
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
parikshit07/lora-flan-t5
|
parikshit07
| 2023-10-19T12:15:18Z | 0 | 0 | null |
[
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-10-19T12:12:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lora-flan-t5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora-flan-t5
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hung200504/bert-23
|
hung200504
| 2023-10-19T12:12:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/bert-base-cased-squad2",
"base_model:finetune:deepset/bert-base-cased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T12:11:58Z |
---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-23
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-23
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 10.9511 | 0.09 | 5 | 10.5588 |
| 8.6122 | 0.18 | 10 | 8.0465 |
| 6.3959 | 0.27 | 15 | 6.5185 |
| 5.5714 | 0.36 | 20 | 5.9355 |
| 5.2088 | 0.45 | 25 | 5.8452 |
| 5.0174 | 0.55 | 30 | 5.9581 |
| 4.3863 | 0.64 | 35 | 6.1063 |
| 4.2079 | 0.73 | 40 | 6.1976 |
| 4.5909 | 0.82 | 45 | 5.8724 |
| 4.2584 | 0.91 | 50 | 5.5712 |
| 4.2042 | 1.0 | 55 | 5.4376 |
| 3.7625 | 1.09 | 60 | 5.4613 |
| 3.5759 | 1.18 | 65 | 5.5305 |
| 3.6831 | 1.27 | 70 | 5.5329 |
| 3.7596 | 1.36 | 75 | 5.5254 |
| 3.6216 | 1.45 | 80 | 5.5825 |
| 3.769 | 1.55 | 85 | 5.6090 |
| 3.5107 | 1.64 | 90 | 5.6351 |
| 3.3485 | 1.73 | 95 | 5.6501 |
| 3.4216 | 1.82 | 100 | 5.6611 |
| 3.3527 | 1.91 | 105 | 5.7240 |
| 3.2204 | 2.0 | 110 | 5.8332 |
| 2.9853 | 2.09 | 115 | 5.8772 |
| 3.207 | 2.18 | 120 | 5.8846 |
| 3.4566 | 2.27 | 125 | 5.8788 |
| 3.1248 | 2.36 | 130 | 5.8898 |
| 3.0917 | 2.45 | 135 | 5.9108 |
| 3.1331 | 2.55 | 140 | 5.9545 |
| 2.9234 | 2.64 | 145 | 5.9664 |
| 3.0005 | 2.73 | 150 | 5.9582 |
| 3.4196 | 2.82 | 155 | 5.9526 |
| 3.2783 | 2.91 | 160 | 5.9486 |
| 3.1719 | 3.0 | 165 | 5.9468 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
kacper-cierzniewski/daigram_detr_r50_albumentations
|
kacper-cierzniewski
| 2023-10-19T12:10:02Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:bpmn-shapes",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-13T13:04:11Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- bpmn-shapes
model-index:
- name: daigram_detr_r50_albumentations
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# daigram_detr_r50_albumentations
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the bpmn-shapes dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0088
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.8163 | 2.63 | 50 | 3.0660 |
| 2.9036 | 5.26 | 100 | 2.8878 |
| 2.7516 | 7.89 | 150 | 2.8043 |
| 2.6278 | 10.53 | 200 | 2.6820 |
| 2.4806 | 13.16 | 250 | 2.5676 |
| 2.3781 | 15.79 | 300 | 2.4282 |
| 2.253 | 18.42 | 350 | 2.3161 |
| 2.1405 | 21.05 | 400 | 2.1735 |
| 2.0263 | 23.68 | 450 | 2.0909 |
| 1.9732 | 26.32 | 500 | 2.0120 |
| 1.8647 | 28.95 | 550 | 1.9260 |
| 1.7793 | 31.58 | 600 | 1.8655 |
| 1.7706 | 34.21 | 650 | 1.8166 |
| 1.6792 | 36.84 | 700 | 1.7325 |
| 1.5654 | 39.47 | 750 | 1.7061 |
| 1.5802 | 42.11 | 800 | 1.6463 |
| 1.5053 | 44.74 | 850 | 1.5985 |
| 1.4858 | 47.37 | 900 | 1.6060 |
| 1.4186 | 50.0 | 950 | 1.5563 |
| 1.4391 | 52.63 | 1000 | 1.5219 |
| 1.3938 | 55.26 | 1050 | 1.4995 |
| 1.3734 | 57.89 | 1100 | 1.4661 |
| 1.3379 | 60.53 | 1150 | 1.4451 |
| 1.341 | 63.16 | 1200 | 1.4854 |
| 1.3647 | 65.79 | 1250 | 1.4509 |
| 1.3198 | 68.42 | 1300 | 1.4116 |
| 1.3054 | 71.05 | 1350 | 1.3821 |
| 1.2945 | 73.68 | 1400 | 1.3952 |
| 1.2899 | 76.32 | 1450 | 1.3868 |
| 1.2533 | 78.95 | 1500 | 1.3580 |
| 1.2655 | 81.58 | 1550 | 1.3374 |
| 1.2649 | 84.21 | 1600 | 1.3451 |
| 1.2286 | 86.84 | 1650 | 1.2973 |
| 1.2497 | 89.47 | 1700 | 1.3322 |
| 1.2456 | 92.11 | 1750 | 1.3289 |
| 1.2234 | 94.74 | 1800 | 1.3080 |
| 1.1695 | 97.37 | 1850 | 1.3218 |
| 1.2265 | 100.0 | 1900 | 1.3280 |
| 1.1899 | 102.63 | 1950 | 1.2834 |
| 1.1914 | 105.26 | 2000 | 1.2931 |
| 1.1698 | 107.89 | 2050 | 1.3176 |
| 1.177 | 110.53 | 2100 | 1.2896 |
| 1.1625 | 113.16 | 2150 | 1.2936 |
| 1.1626 | 115.79 | 2200 | 1.2614 |
| 1.1698 | 118.42 | 2250 | 1.2545 |
| 1.1703 | 121.05 | 2300 | 1.2398 |
| 1.1659 | 123.68 | 2350 | 1.2254 |
| 1.1734 | 126.32 | 2400 | 1.2489 |
| 1.1234 | 128.95 | 2450 | 1.2072 |
| 1.1464 | 131.58 | 2500 | 1.1707 |
| 1.1268 | 134.21 | 2550 | 1.1971 |
| 1.1511 | 136.84 | 2600 | 1.2247 |
| 1.1234 | 139.47 | 2650 | 1.1921 |
| 1.0923 | 142.11 | 2700 | 1.1751 |
| 1.1267 | 144.74 | 2750 | 1.1905 |
| 1.1021 | 147.37 | 2800 | 1.1885 |
| 1.1075 | 150.0 | 2850 | 1.1780 |
| 1.1116 | 152.63 | 2900 | 1.1666 |
| 1.0987 | 155.26 | 2950 | 1.1694 |
| 1.0974 | 157.89 | 3000 | 1.1931 |
| 1.0867 | 160.53 | 3050 | 1.1461 |
| 1.1076 | 163.16 | 3100 | 1.1501 |
| 1.0912 | 165.79 | 3150 | 1.1611 |
| 1.0671 | 168.42 | 3200 | 1.1718 |
| 1.0981 | 171.05 | 3250 | 1.1961 |
| 1.0602 | 173.68 | 3300 | 1.1786 |
| 1.0305 | 176.32 | 3350 | 1.1640 |
| 1.0647 | 178.95 | 3400 | 1.1416 |
| 1.0628 | 181.58 | 3450 | 1.1296 |
| 1.0856 | 184.21 | 3500 | 1.1140 |
| 1.0626 | 186.84 | 3550 | 1.1214 |
| 1.0782 | 189.47 | 3600 | 1.1449 |
| 1.0601 | 192.11 | 3650 | 1.1441 |
| 1.0906 | 194.74 | 3700 | 1.1396 |
| 1.0376 | 197.37 | 3750 | 1.1271 |
| 1.0625 | 200.0 | 3800 | 1.1397 |
| 1.057 | 202.63 | 3850 | 1.1121 |
| 1.0448 | 205.26 | 3900 | 1.1376 |
| 1.0747 | 207.89 | 3950 | 1.1475 |
| 1.0605 | 210.53 | 4000 | 1.0916 |
| 1.0344 | 213.16 | 4050 | 1.1001 |
| 1.0443 | 215.79 | 4100 | 1.0976 |
| 1.0202 | 218.42 | 4150 | 1.1240 |
| 1.078 | 221.05 | 4200 | 1.1024 |
| 1.0251 | 223.68 | 4250 | 1.0793 |
| 1.0353 | 226.32 | 4300 | 1.1153 |
| 1.0047 | 228.95 | 4350 | 1.0972 |
| 1.0143 | 231.58 | 4400 | 1.0948 |
| 1.0172 | 234.21 | 4450 | 1.1265 |
| 1.0299 | 236.84 | 4500 | 1.1038 |
| 0.9968 | 239.47 | 4550 | 1.0901 |
| 1.0233 | 242.11 | 4600 | 1.0945 |
| 0.9943 | 244.74 | 4650 | 1.0918 |
| 1.0321 | 247.37 | 4700 | 1.1270 |
| 1.0113 | 250.0 | 4750 | 1.1060 |
| 1.0229 | 252.63 | 4800 | 1.0859 |
| 0.9945 | 255.26 | 4850 | 1.0875 |
| 1.0073 | 257.89 | 4900 | 1.0976 |
| 1.0096 | 260.53 | 4950 | 1.0933 |
| 1.0 | 263.16 | 5000 | 1.0821 |
| 1.0326 | 265.79 | 5050 | 1.0747 |
| 0.997 | 268.42 | 5100 | 1.0931 |
| 1.0056 | 271.05 | 5150 | 1.0853 |
| 0.9858 | 273.68 | 5200 | 1.0945 |
| 1.0005 | 276.32 | 5250 | 1.0669 |
| 1.0217 | 278.95 | 5300 | 1.0497 |
| 0.9777 | 281.58 | 5350 | 1.0672 |
| 0.9888 | 284.21 | 5400 | 1.0844 |
| 0.9662 | 286.84 | 5450 | 1.0524 |
| 1.0029 | 289.47 | 5500 | 1.0519 |
| 0.984 | 292.11 | 5550 | 1.0538 |
| 0.9724 | 294.74 | 5600 | 1.0524 |
| 0.991 | 297.37 | 5650 | 1.0553 |
| 0.9936 | 300.0 | 5700 | 1.0601 |
| 0.9817 | 302.63 | 5750 | 1.0524 |
| 0.9868 | 305.26 | 5800 | 1.0644 |
| 0.9982 | 307.89 | 5850 | 1.0523 |
| 0.9814 | 310.53 | 5900 | 1.0611 |
| 0.9761 | 313.16 | 5950 | 1.0505 |
| 0.9507 | 315.79 | 6000 | 1.0361 |
| 0.9786 | 318.42 | 6050 | 1.0275 |
| 0.9684 | 321.05 | 6100 | 1.0292 |
| 0.9759 | 323.68 | 6150 | 1.0529 |
| 0.9442 | 326.32 | 6200 | 1.0689 |
| 0.9653 | 328.95 | 6250 | 1.0696 |
| 0.9579 | 331.58 | 6300 | 1.0572 |
| 1.0016 | 334.21 | 6350 | 1.0660 |
| 0.9462 | 336.84 | 6400 | 1.0525 |
| 0.9596 | 339.47 | 6450 | 1.0505 |
| 0.9655 | 342.11 | 6500 | 1.0514 |
| 0.9713 | 344.74 | 6550 | 1.0616 |
| 0.952 | 347.37 | 6600 | 1.0497 |
| 0.9433 | 350.0 | 6650 | 1.0389 |
| 0.9619 | 352.63 | 6700 | 1.0404 |
| 0.9594 | 355.26 | 6750 | 1.0332 |
| 0.9586 | 357.89 | 6800 | 1.0323 |
| 0.9582 | 360.53 | 6850 | 1.0294 |
| 0.9437 | 363.16 | 6900 | 1.0329 |
| 0.9585 | 365.79 | 6950 | 1.0361 |
| 0.9661 | 368.42 | 7000 | 1.0428 |
| 0.9603 | 371.05 | 7050 | 1.0299 |
| 0.9619 | 373.68 | 7100 | 1.0416 |
| 0.9766 | 376.32 | 7150 | 1.0471 |
| 0.9547 | 378.95 | 7200 | 1.0498 |
| 0.967 | 381.58 | 7250 | 1.0318 |
| 0.9463 | 384.21 | 7300 | 1.0238 |
| 0.9531 | 386.84 | 7350 | 1.0329 |
| 0.9342 | 389.47 | 7400 | 1.0354 |
| 0.939 | 392.11 | 7450 | 1.0312 |
| 0.9635 | 394.74 | 7500 | 1.0325 |
| 0.9261 | 397.37 | 7550 | 1.0245 |
| 0.962 | 400.0 | 7600 | 1.0381 |
| 0.9385 | 402.63 | 7650 | 1.0243 |
| 0.9422 | 405.26 | 7700 | 1.0235 |
| 0.9285 | 407.89 | 7750 | 1.0286 |
| 0.9598 | 410.53 | 7800 | 1.0353 |
| 0.9529 | 413.16 | 7850 | 1.0361 |
| 0.928 | 415.79 | 7900 | 1.0316 |
| 0.935 | 418.42 | 7950 | 1.0263 |
| 0.9456 | 421.05 | 8000 | 1.0368 |
| 0.9387 | 423.68 | 8050 | 1.0440 |
| 0.9321 | 426.32 | 8100 | 1.0440 |
| 0.9236 | 428.95 | 8150 | 1.0394 |
| 0.9448 | 431.58 | 8200 | 1.0467 |
| 0.9151 | 434.21 | 8250 | 1.0516 |
| 0.9373 | 436.84 | 8300 | 1.0383 |
| 0.9577 | 439.47 | 8350 | 1.0190 |
| 0.9199 | 442.11 | 8400 | 1.0215 |
| 0.9321 | 444.74 | 8450 | 1.0184 |
| 0.9387 | 447.37 | 8500 | 1.0236 |
| 0.9382 | 450.0 | 8550 | 1.0259 |
| 0.9391 | 452.63 | 8600 | 1.0282 |
| 0.9392 | 455.26 | 8650 | 1.0193 |
| 0.9438 | 457.89 | 8700 | 1.0124 |
| 0.9398 | 460.53 | 8750 | 1.0060 |
| 0.9246 | 463.16 | 8800 | 1.0140 |
| 0.9383 | 465.79 | 8850 | 1.0145 |
| 0.9267 | 468.42 | 8900 | 1.0122 |
| 0.9253 | 471.05 | 8950 | 1.0144 |
| 0.9238 | 473.68 | 9000 | 1.0065 |
| 0.9082 | 476.32 | 9050 | 1.0136 |
| 0.9287 | 478.95 | 9100 | 1.0120 |
| 0.9161 | 481.58 | 9150 | 1.0120 |
| 0.9093 | 484.21 | 9200 | 1.0128 |
| 0.9264 | 486.84 | 9250 | 1.0125 |
| 0.9487 | 489.47 | 9300 | 1.0131 |
| 0.9398 | 492.11 | 9350 | 1.0101 |
| 0.9039 | 494.74 | 9400 | 1.0090 |
| 0.908 | 497.37 | 9450 | 1.0097 |
| 0.944 | 500.0 | 9500 | 1.0088 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
juierror/q-Taxi-v3
|
juierror
| 2023-10-19T12:08:19Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T12:08:17Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="juierror/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
1TuanPham/Instruct_en-vi_14500_1e_TheBloke_Mistralic-7B-1-GPTQ_LORA_CAUSAL_LM
|
1TuanPham
| 2023-10-19T12:01:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T10:57:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: True
- model_seqlen: 4096
- block_name_to_quantize: model.layers
- module_name_preceding_first_block: ['model.embed_tokens']
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.5.0
|
dlhw/setFit-fewShot
|
dlhw
| 2023-10-19T11:59:15Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-10-19T11:58:54Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# dlhw/setFit-fewShot
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("dlhw/setFit-fewShot")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
lzhcode/sd-class-butterflies-32
|
lzhcode
| 2023-10-19T11:58:50Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-19T11:57:13Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('lzhcode/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Kuyoh/SummerSummary
|
Kuyoh
| 2023-10-19T11:58:48Z | 0 | 10 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-19T09:31:35Z |
---
license: creativeml-openrail-m
thumbnail: "./image/SummerSummaryThumb.png"
---
<img src="./image/SummerSummary.png" width="1000" height="">
# Summer Summary
アニメっぽさを意識したStableDiffusion1系モデルです。
vaeは同梱していないため、各自お好きなものを別途DLしてください。
また、特に推奨設定はありません。貴方のお好きな設定で使ってください。
This is a StableDiffusion v1 model with an anime-like feel.
The VAE is not baked, so please download your preferred one separately.
Additionally, there are no recommended settings. Please use it with your favorite settings.
---
## 免責事項/Disclaimer
- NSFW画像の作成は、ユーザーの判断に基づくため、モデル製作者は作成によって生じた不都合の一切の責任を負いません。
- 本モデルはNSFWコンテンツを公開する目的のために作成されたモデルではありません。
- 基本的な使用制限はcreativeml-openrail-mに準拠します。必ずライセンスを確認したうえでご使用ください。
* The creation of NSFW images is at the user's discretion, and the model creator assumes no responsibility for any inconveniences caused by the creation.
* This model was not created for the purpose of publishing NSFW content.
* The basic usage restrictions adhere to creativeml-openrail-m. Please be sure to check the license before using it.
---
## 制限/Restrictions
以下の事項を守り、**常識の範囲内**でご使用ください。
✅ 本モデルを使用したマージモデルを使用または再配布する行為
✅ 本モデルのクレジット表記をせずに使用する行為
✅ 本モデルをマージしたモデルに異なる権限を与える行為
❌ 本モデルで生成した画像を商用利用する行為
❌ 本モデルを商用の画像生成サービスで利用する行為
❌ 本モデルや本モデルをマージしたモデルを販売する行為
❌ 本モデルを使用し意図的に違法な出力をする行為
Please adhere to the following guidelines and use **within the bounds of common sense**:
✅ Using or redistributing models merged with this model.
✅ Using this model without giving credit.
✅ Granting different permissions to models merged with this model.
❌ Commercial use of images generated by this model.
❌ Using this model in a commercial image generation service.
❌ Selling this model or models merged with this model.
❌ Using this model to intentionally produce illegal outputs.
---
## 作例/Example of Use
```
(1girl), (solo), cyan short bob hair, cyan eyes, (cat ears:1.1), (black cap:1.2), white t-shirt, black leather jacket, ocean, palm tree, fence, utility pole, road sign, cinematic lighting
Negative prompt: EasyNegative, extra fingers, fewer fingers,
Steps: 20, Sampler: UniPC, CFG scale: 6, Size: 910x512, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 2, Hires steps: 20
```
<img src="./image/img01.png" width="500" height="">
```
(masterpiece:1.2), (best quality:1.2) 1girl, white hair, bob cut, red eyes, casual jacket, white jacket, white shirt, necktie, lily flower, simple background,
Negative prompt: (worst quality:1.4), (low quality:1.4), watermark,
Steps: 20, Sampler: UniPC, CFG scale: 6, Size: 512x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 2, Hires steps: 20
```
<img src="./image/img02.png" width="500" height="">
```
(masterpiece:1.2), (best quality:1.2) 1girl, school uniform girl, ponytail, green hair, happy, medium breasts, casual white shirt, necktie, plaid skirt, arms up, biting hairband, lens flare, dutch angle, zettai ryouiki,
Negative prompt: EasyNegative, extra fingers, fewer fingers
Steps: 20, Sampler: UniPC, CFG scale: 6, Size: 910x512, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 2, Hires steps: 20
```
<img src="./image/img03.png" width="500" height="">
---
## 作者/Author
くよう(@wd_kuyokuyo)
X: https://twitter.com/wd_kuyokuyo
lit.link: https://lit.link/kuyoh
|
arielf1/roberta-large-peft-p-tuning
|
arielf1
| 2023-10-19T11:56:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T11:56:40Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
DenysKlypkin/LunarLander
|
DenysKlypkin
| 2023-10-19T11:48:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T11:48:29Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.87 +/- 15.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ricecake/Codellama-Pygmalion-LoRA-Test
|
ricecake
| 2023-10-19T11:45:06Z | 4 | 1 |
peft
|
[
"peft",
"dataset:PygmalionAI/PIPPA",
"dataset:usernamedesu/pyg_dataset_markdown",
"base_model:codellama/CodeLlama-34b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-34b-Instruct-hf",
"region:us"
] | null | 2023-09-01T06:53:32Z |
---
library_name: peft
datasets:
- PygmalionAI/PIPPA
- usernamedesu/pyg_dataset_markdown
base_model: codellama/CodeLlama-34b-Instruct-hf
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
sainteye/ifoodie-classifier-v6
|
sainteye
| 2023-10-19T11:36:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-19T11:36:48Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ifoodie-classifier-v6
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9694656729698181
---
# ifoodie-classifier-v6
['人物', '其他', '廣告', '拼貼壓字', '菜單', '食物', '餐廳']
## Example Images
# #### 人物
# 
#
# #### 其他
# 
#
# #### 廣告
# 
#
# #### 拼貼壓字
# 
#
# #### 菜單
# 
#
# #### 食物
# 
#
# #### 餐廳
# 
#
|
madroid/tinyllama-colorist-lora
|
madroid
| 2023-10-19T11:35:38Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:Open-Orca/oo-phi-1_5",
"base_model:finetune:Open-Orca/oo-phi-1_5",
"region:us"
] | null | 2023-10-19T11:23:53Z |
---
base_model: Open-Orca/oo-phi-1_5
tags:
- generated_from_trainer
model-index:
- name: tinyllama-colorist-lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-colorist-lora
This model is a fine-tuned version of [Open-Orca/oo-phi-1_5](https://huggingface.co/Open-Orca/oo-phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 18
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
rustformers/dolly-v2-ggml
|
rustformers
| 2023-10-19T11:09:29Z | 18 | 2 |
transformers
|
[
"transformers",
"gpt_neox",
"llm-rs",
"ggml",
"text-generation",
"en",
"dataset:databricks/databricks-dolly-15k",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-29T12:52:45Z |
---
license: mit
language:
- en
tags:
- llm-rs
- ggml
pipeline_tag: text-generation
datasets:
- databricks/databricks-dolly-15k
---
# GGML converted version of [Databricks](https://huggingface.co/databricks) Dolly-V2 models
## Description
Dolly is trained on ~15k instruction/response fine tuning records databricks-dolly-15k generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization.
## Converted Models
| Name | Based on | Type | Container | GGML Version |
|:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------|:------------|:---------------|
| [dolly-v2-12b-f16.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-12b-f16.bin) | [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) | F16 | GGML | V3 |
| [dolly-v2-12b-q4_0.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-12b-q4_0.bin) | [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) | Q4_0 | GGML | V3 |
| [dolly-v2-12b-q4_0-ggjt.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-12b-q4_0-ggjt.bin) | [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) | Q4_0 | GGJT | V3 |
| [dolly-v2-3b-f16.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-3b-f16.bin) | [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) | F16 | GGML | V3 |
| [dolly-v2-3b-q4_0.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-3b-q4_0.bin) | [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) | Q4_0 | GGML | V3 |
| [dolly-v2-3b-q4_0-ggjt.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-3b-q4_0-ggjt.bin) | [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) | Q4_0 | GGJT | V3 |
| [dolly-v2-3b-q5_1.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-3b-q5_1.bin) | [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) | Q5_1 | GGML | V3 |
| [dolly-v2-3b-q5_1-ggjt.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-3b-q5_1-ggjt.bin) | [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) | Q5_1 | GGJT | V3 |
| [dolly-v2-7b-f16.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-7b-f16.bin) | [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) | F16 | GGML | V3 |
| [dolly-v2-7b-q4_0.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-7b-q4_0.bin) | [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) | Q4_0 | GGML | V3 |
| [dolly-v2-7b-q4_0-ggjt.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-7b-q4_0-ggjt.bin) | [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) | Q4_0 | GGJT | V3 |
| [dolly-v2-7b-q5_1.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-7b-q5_1.bin) | [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) | Q5_1 | GGML | V3 |
| [dolly-v2-7b-q5_1-ggjt.bin](https://huggingface.co/rustformers/dolly-v2-ggml/blob/main/dolly-v2-7b-q5_1-ggjt.bin) | [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) | Q5_1 | GGJT | V3 |
## Usage
### Python via [llm-rs](https://github.com/LLukas22/llm-rs-python):
#### Installation
Via pip: `pip install llm-rs`
#### Run inference
```python
from llm_rs import AutoModel
#Load the model, define any model you like from the list above as the `model_file`
model = AutoModel.from_pretrained("rustformers/dolly-v2-ggml",model_file="dolly-v2-12b-q4_0-ggjt.bin")
#Generate
print(model.generate("The meaning of life is"))
```
### Rust via [Rustformers/llm](https://github.com/rustformers/llm):
#### Installation
```
git clone --recurse-submodules https://github.com/rustformers/llm.git
cd llm
cargo build --release
```
#### Run inference
```
cargo run --release -- gptneox infer -m path/to/model.bin -p "Tell me how cool the Rust programming language is:"
```
|
bpd1997/falcon7binstruct_mentalhealthmodel_oct23
|
bpd1997
| 2023-10-19T11:04:44Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:finetune:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2023-10-16T08:38:21Z |
---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
tags:
- generated_from_trainer
model-index:
- name: falcon7binstruct_mentalhealthmodel_oct23
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
phbruce/haversine-distance
|
phbruce
| 2023-10-19T11:02:16Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-10-19T11:02:16Z |
---
license: mit
---
# Haversine Distance
Consider the Earth as a perfect sphere. If we wanted to measure the distance between two points on this sphere, we would simply draw a straight line between the coordinates φ1λ1 and φ2λ2, right?
Actually, not exactly. When trying to calculate this distance, we should keep in mind the angle formed by the sphere's radius, which is represented by the unit θ. By ignoring this angle, we would consider the Earth as a flat surface. Recognizing this complexity, some mathematicians developed the Haversine Formula over the years based on astronomical observations. This formula calculates the shortest distance between two points on a sphere using its radius. Nowadays, it is widely used in navigation and geolocation applications, as it allows for precise calculations of distances on the Earth's surface.
> φ1λ1 represents the latitude and longitude of the first coordinate, and φ2λ2 represents the latitude and longitude of the second coordinate.
To understand the concept of Haversine, visualize a sphere and a triangle inscribed in it, whose vertex is located at the center of the sphere (O) and whose base connects the points φ1λ1 and φ2λ2 on the surface of the sphere. From O, draw a segment that extends to touch the surface of the sphere at a specific angle, known as the central angle (θ). The distance between the center of the sphere (O) and the point where this segment touches the surface is related to the Versine function.
The Haversine is an essential tool for calculating the orthodromic distance, which is the shortest distance between two points on the surface of a sphere. This distance is determined between the points φ1λ1 and φ2λ2 and is expressed using the trigonometric function of the sine. Essentially, the haversine(θ) is half of a versine(θ). The reason for using the Haversine instead of the Versine is that it provides more accurate calculations for short distances. When the distance between φ1λ1 and φ2λ2 is very small, the value of versine(θ) approaches 1, becoming less precise. The Haversine, on the other hand, remains effective in representing that small distance accurately.
> Essentially, the Haversine is half of a Versine.
Given this explanation, the central angle (θ) is given by the relationship of the distance between the two points on the sphere divided by the radius of the sphere:
```python
def central_angle(d, r):
"""
Calculates the central angle of a circle given the distance and
radius.
Args:
d (float): Distance
r (float): Radius
Returns:
float: Central angle
"""
return d / r
```
Where:
- d is the distance between the two points on the sphere;
- r is the radius of the sphere.
You can see in Image 1 that the value of versine(θ) is 1-cos(θ) on the trigonometric circle, and this equates to 2sin^2(θ/2) using the trigonometric identities. As previously mentioned, the haversine(θ) will be exactly half of this (sin^2(θ/2)):
```python
import math
def hav(θ):
"""
Calculates the haversine of an angle.
Args:
θ (float): Angle in radians
Returns:
float: Haversine of angle.
"""
return pow(math.sin(θ / 2), 2)
```
As we need the haversine(θ) in a spherical system, it will be necessary to bring in the coordinates of the points φ1λ1 and φ2λ2 and calculate the haversine of the central difference between the two geographic points:
```python
def haversine_difference(φ1, φ2, λ1, λ2):
"""
Calculates the haversine of the central difference between
two geographic points.
Args:
φ1 (float): Latitude of first point in radians.
φ2 (float): Latitude of second point in radians.
λ1 (float): Longitude of first point in radians.
λ2 (float): Longitude of second point in radians.
Returns:
float: Haversine of the central difference.
"""
return hav(φ2 - φ1) + (math.cos(φ1) * math.cos(φ2) * hav(λ2 - λ1))
```
Where:
- φ1 and φ2 are the latitudes of each point in radians;
- λ1 and λ2 are the longitudes of each point in radians.
Now that we have the main tools to calculate the central angle and the distance of the geographic points, we can compute the distance between the two points on the coordinate using the inverse sine function (arcsin):
```python
import math
def haversine(φ1, φ2, λ1, λ2, rad=6371):
"""
Calculates the distance between two points on the Earth's
surface given their latitude and longitude in degrees.
Args:
φ1 (float): Latitude of first point in degrees.
φ2 (float): Latitude of second point in degrees.
λ1 (float): Longitude of first point in degrees.
λ2 (float): Longitude of second point in degrees.
rad (int): Radius of the Earth in the desired units (default is 6371 km).
Returns:
float: Distance between the two points in the units corresponding to the provided Earth's radius.
"""
φ1, φ2 = math.radians(φ1), math.radians(φ2)
λ1, λ2 = math.radians(λ1), math.radians(λ2)
central_angle_hav = haversine_difference(φ1, φ2, λ1, λ2)
return 2 * rad * math.asin(math.sqrt(central_angle_hav))
```
Where:
- φ1 and φ2 are the latitudes of each point in radians;
- λ1 and λ2 are the longitudes of each point in radians;
- rad is the radius of the perfect sphere.
Note:
Notice that we are converting the angles of the coordinates into radians, as we are computing trigonometric functions in the radian system.
## Real-world Applications
The ability to accurately calculate the distance between two points on the Earth's surface has various uses, such as: GPS navigation, logistics and transport, aviation, geological studies, environmental research, tourism, and more.
## Some Considerations
As many of us know, Earth does not have the exact shape of a perfect sphere; it more closely resembles an oblate spheroid, with significant variations due to terrain, gravity, among other factors. The "Haversine distance" method provides us with an approximation of the real distance between two points on the Earth's surface and is sufficiently accurate for many applications that don't require extreme precision. If we need more accurate calculations, we can turn to other methods and formulas, like the Vincenty formula, and in even more specific situations, the Earth Gravitational Model (EGM).
|
samankhan07/sdxl_try
|
samankhan07
| 2023-10-19T10:59:11Z | 2 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-10-19T08:06:29Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of anum001 person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
TheBloke/MistralLite-7B-GGUF
|
TheBloke
| 2023-10-19T10:58:58Z | 500 | 40 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:amazon/MistralLite",
"base_model:quantized:amazon/MistralLite",
"license:apache-2.0",
"region:us"
] | null | 2023-10-19T10:55:29Z |
---
base_model: amazon/MistralLite
inference: false
license: apache-2.0
model_creator: Amazon Web Services
model_name: MistralLite 7B
model_type: mistral
prompt_template: '<|prompter|>{prompt}</s><|assistant|>
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# MistralLite 7B - GGUF
- Model creator: [Amazon Web Services](https://huggingface.co/amazon)
- Original model: [MistralLite 7B](https://huggingface.co/amazon/MistralLite)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Amazon Web Services's MistralLite 7B](https://huggingface.co/amazon/MistralLite).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MistralLite-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MistralLite-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MistralLite-7B-GGUF)
* [Amazon Web Services's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/amazon/MistralLite)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Amazon
```
<|prompter|>{prompt}</s><|assistant|>
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistrallite.Q2_K.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistrallite.Q3_K_S.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistrallite.Q3_K_M.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistrallite.Q3_K_L.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistrallite.Q4_0.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistrallite.Q4_K_S.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistrallite.Q4_K_M.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistrallite.Q5_0.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistrallite.Q5_K_S.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistrallite.Q5_K_M.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistrallite.Q6_K.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistrallite.Q8_0.gguf](https://huggingface.co/TheBloke/MistralLite-7B-GGUF/blob/main/mistrallite.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/MistralLite-7B-GGUF and below it, a specific filename to download, such as: mistrallite.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/MistralLite-7B-GGUF mistrallite.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/MistralLite-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/MistralLite-7B-GGUF mistrallite.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mistrallite.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|prompter|>{prompt}</s><|assistant|>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/MistralLite-7B-GGUF", model_file="mistrallite.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Amazon Web Services's MistralLite 7B
# MistralLite Model
MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform significantly better on several long context retrieve and answering tasks**, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS `g5.2x` instance with Sagemaker [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like [vLLM](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library.
MistralLite is similar to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size|
|----------|-------------:|------------:|-----------:|-----------:|
| Mistral-7B-Instruct-v0.1 | up to 8K tokens | 32K | rope_theta = 10000 | 4096 |
| MistralLite | up to 16K tokens | 32K | **rope_theta = 1000000** | **16384** |
## Motivation of Developing MistralLite
Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the model became increasingly popular because its strong performance
on a wide range of benchmarks. But most of the benchmarks are evaluated on `short context`, and not much has been investigated on its performance on long context tasks.
Then We evaluated `Mistral-7B-Instruct-v0.1` against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context.
Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens,
there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced `Mistrallite`. The model managed to `significantly boost the performance of long context handling` over Mistral-7B-Instruct-v0.1. The detailed `long context evalutaion results` are as below:
1. [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/)
|Model Name|Input length| Input length | Input length| Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|
| | 2851| 5568 |8313 | 11044 | 13780
| Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% |
| MistralLite | **100%** | **100%** | **100%** | **100%** | **98%** |
2. [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results)
|Model Name|Input length| Input length | Input length| Input length| Input length|Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
| | 3818| 5661 |7505 | 9354 | 11188 | 12657
| Mistral-7B-Instruct-v0.1 | **98%** | 62% | 42% | 42% | 32% | 30% |
| MistralLite | **98%** | **92%** | **88%** | **76%** | **70%** | **60%** |
3. [Pass key Retrieval](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101)
|Model Name|Input length| Input length | Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|
| | 3264| 5396 |8329 | 10197 |
| Mistral-7B-Instruct-v0.1 | **100%** | 50% | 20% | 30% |
| MistralLite | **100%** | **100%** | **100%** | **100%** |
4. [Question Answering with Long Input Texts](https://nyu-mll.github.io/quality/)
|Model Name| Test set Accuracy | Hard subset Accuracy|
|----------|-------------:|-------------:|
| Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% |
| MistralLite | **64.4%** | **56.2%** |
### Example test on long context of 13400 tokens
- Context is from [Amazon Aurora FAQs](https://aws.amazon.com/rds/aurora/faqs/)
- Question: *please tell me how does pgvector help with Generative AI and give me some examples.*
- Answer from MistralLite:
```code
pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.
You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.
With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.
```
## Model Details
- **Developed by:** [AWS Contributors](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
- [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled)
- [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions)
- [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **Supported Serving Framework:**
- [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0)
- [vLLM](https://github.com/vllm-project/vllm)
- [HuggingFace transformers](https://huggingface.co/docs/transformers/index)
- [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container)
- **Model License:** Apache 2.0
- **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues)
- **Inference Code** [Github Repo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/)
## How to Use MistralLite from Python Code (HuggingFace transformers) ##
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/huggingface-transformers/example_usage.ipynb).
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later,
and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later.
```shell
pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0
```
### You can then try the following example code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "amazon/MistralLite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True,
device_map="auto",)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
sequences = pipeline(
prompt,
max_new_tokens=400,
do_sample=False,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"{seq['generated_text']}")
```
**Important** - Use the prompt template below for MistralLite:
```
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
```
## How to Serve MistralLite on TGI ##
**Important:**
- For an end-to-end example Jupyter notebook using the native TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi-custom/example_usage.ipynb).
### Start TGI server ###
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
--model-id amazon/MistralLite \
--max-input-length 16000 \
--max-total-tokens 16384 \
--max-batch-prefill-tokens 16384 \
--trust-remote-code
```
### Perform Inference ###
Example Python code for inference with TGI (requires `text_generation` 0.6.1 or later):
```shell
pip install text_generation==0.6.1
```
```python
from text_generation import Client
SERVER_PORT = 443
SERVER_HOST = "localhost"
SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}"
tgi_client = Client(f"http://{SERVER_URL}", timeout=60)
def invoke_tgi(prompt,
random_seed=1,
max_new_tokens=400,
print_stream=True,
assist_role=True):
if (assist_role):
prompt = f"<|prompter|>{prompt}</s><|assistant|>"
output = ""
for response in tgi_client.generate_stream(
prompt,
do_sample=False,
max_new_tokens=max_new_tokens,
return_full_text=False,
#temperature=None,
#truncate=None,
#seed=random_seed,
#typical_p=0.2,
):
if hasattr(response, "token"):
if not response.token.special:
snippet = response.token.text
output += snippet
if (print_stream):
print(snippet, end='', flush=True)
return output
prompt = "What are the main challenges to support a long context for LLM?"
result = invoke_tgi(prompt)
```
**Important** - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
## How to Deploy MistralLite on Amazon SageMaker ##
**Important:**
- For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom docker container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi-custom/example_usage.ipynb).
### Install the necessary packages
Requires: [sagemaker](https://pypi.org/project/sagemaker/) 2.192.1 or later.
```shell
pip install sagemaker==2.192.1
```
### Deploy the Model as A SageMaker Endpoint ###
To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
image_uri = get_huggingface_llm_image_uri(
backend="huggingface", # or lmi
region=region,
version="1.1.0"
)
model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
hub = {
'HF_MODEL_ID':'amazon/MistralLite',
'HF_TASK':'text-generation',
'SM_NUM_GPUS':'1',
"MAX_INPUT_LENGTH": '16000',
"MAX_TOTAL_TOKENS": '16384',
"MAX_BATCH_PREFILL_TOKENS": '16384',
"MAX_BATCH_TOTAL_TOKENS": '16384',
}
model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name=model_name,
)
```
### Perform Inference ###
To call the endpoint, please follow the example code as below:
```python
input_data = {
"inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
"parameters": {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
}
result = predictor.predict(input_data)[0]["generated_text"]
print(result)
```
or via [boto3](https://pypi.org/project/boto3/), and the example code is shown as below:
```python
import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
client = boto3.client("sagemaker-runtime")
payload = {"inputs": prompt,
"parameters": parameters}
response = client.invoke_endpoint(EndpointName=endpoint_name,
Body=json.dumps(payload),
ContentType="application/json")
output = json.loads(response["Body"].read().decode())
result = output[0]["generated_text"]
return result
client = boto3.client("sagemaker-runtime")
parameters = {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
endpoint_name = predictor.endpoint_name
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, parameters)
print(result)
```
## How to Serve MistralLite on vLLM ##
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/vllm/example_usage.ipynb).
### Using vLLM as a server ###
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
```shell
python3 -m vllm.entrypoints.api_server --model amazon/MistralLite
```
### Using vLLM in Python Code ###
When using vLLM from Python code, Please see the example code as below:
```python
from vllm import LLM, SamplingParams
prompts = [
"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)
llm = LLM(model="amazon/MistralLite",)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Limitations ##
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
<!-- original-model-card end -->
|
ashwincv0112/codellama-python7b
|
ashwincv0112
| 2023-10-19T10:23:24Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-19T01:27:37Z |
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers from `main` until the next version is released:
```bash
pip install git+https://github.com/huggingface/transformers.git@main accelerate
```
Model capabilities:
- [x] Code completion.
- [ ] Infilling.
- [ ] Instructions / chat.
- [x] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Python version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
|
Zz7708602/cs
|
Zz7708602
| 2023-10-19T10:21:23Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"art",
"aa",
"dataset:lmsys/lmsys-chat-1m",
"license:apache-2.0",
"region:us"
] | null | 2023-10-19T10:20:07Z |
---
license: apache-2.0
datasets:
- lmsys/lmsys-chat-1m
language:
- aa
metrics:
- accuracy
library_name: adapter-transformers
tags:
- art
---
|
salehieissa/Ai
|
salehieissa
| 2023-10-19T10:19:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-19T10:14:43Z |
<div align="center">
<h1>Retrieval-based-Voice-Conversion-WebUI</h1>
一个基于VITS的简单易用的语音转换（变声器）框架<br><br>
[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)
<img src="https://counter.seku.su/cmoe?name=rvc&theme=r34" /><br>
[](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb)
[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/LICENSE)
[](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)
[](https://discord.gg/HcsmBBGyVk)
[**更新日志**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/docs/Changelog_CN.md) | [**常见问题解答**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98%E8%A7%A3%E7%AD%94) | [**AutoDL·5毛钱训练AI歌手**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/Autodl%E8%AE%AD%E7%BB%83RVC%C2%B7AI%E6%AD%8C%E6%89%8B%E6%95%99%E7%A8%8B) | [**对照实验记录**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/Autodl%E8%AE%AD%E7%BB%83RVC%C2%B7AI%E6%AD%8C%E6%89%8B%E6%95%99%E7%A8%8B](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/wiki/%E5%AF%B9%E7%85%A7%E5%AE%9E%E9%AA%8C%C2%B7%E5%AE%9E%E9%AA%8C%E8%AE%B0%E5%BD%95)) | [**在线演示**](https://huggingface.co/spaces/Ricecake123/RVC-demo)
</div>
------
[**English**](./docs/README.en.md) | [**中文简体**](./README.md) | [**日本語**](./docs/README.ja.md) | [**한국어**](./docs/README.ko.md) ([**韓國語**](./docs/README.ko.han.md))
点此查看我们的[演示视频](https://www.bilibili.com/video/BV1pm4y1z7Gm/) !
> 使用了RVC的实时语音转换: [w-okada/voice-changer](https://github.com/w-okada/voice-changer)
> 使用了RVC变声器训练的人声转木吉他模型在线demo ：https://huggingface.co/spaces/lj1995/vocal2guitar
> RVC人声转吉他效果展示视频 ：https://www.bilibili.com/video/BV19W4y1D7tT/
> 底模使用接近50小时的开源高质量VCTK训练集训练，无版权方面的顾虑，请大家放心使用
> 后续会陆续加入高质量有授权歌声训练集训练底模
## 简介
本仓库具有以下特点
+ 使用top1检索替换输入源特征为训练集特征来杜绝音色泄漏
+ 即便在相对较差的显卡上也能快速训练
+ 使用少量数据进行训练也能得到较好结果(推荐至少收集10分钟低底噪语音数据)
+ 可以通过模型融合来改变音色(借助ckpt处理选项卡中的ckpt-merge)
+ 简单易用的网页界面
+ 可调用UVR5模型来快速分离人声和伴奏
+ 使用最先进的[人声音高提取算法InterSpeech2023-RMVPE](#参考项目)根绝哑音问题。效果最好（显著地）但比crepe_full更快、资源占用更小
## 环境配置
可以使用poetry配置环境。
以下指令需在Python版本大于3.8的环境中执行:
```bash
# 安装Pytorch及其核心依赖，若已安装则跳过
# 参考自: https://pytorch.org/get-started/locally/
pip install torch torchvision torchaudio
#如果是win系统+Nvidia Ampere架构(RTX30xx)，根据 #21 的经验，需要指定pytorch对应的cuda版本
#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
# 安装 Poetry 依赖管理工具, 若已安装则跳过
# 参考自: https://python-poetry.org/docs/#installation
curl -sSL https://install.python-poetry.org | python3 -
# 通过poetry安装依赖
poetry install
```
你也可以通过pip来安装依赖：
```bash
pip install -r requirements.txt
```
## 其他预模型准备
RVC需要其他一些预模型来推理和训练。
你可以从我们的[Hugging Face space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)下载到这些模型。
以下是一份清单，包括了所有RVC所需的预模型和其他文件的名称:
```bash
hubert_base.pt
./pretrained
./uvr5_weights
想测试v2版本模型的话，需要额外下载
./pretrained_v2
如果你正在使用Windows，则你可能需要这个文件，若ffmpeg和ffprobe已安装则跳过; ubuntu/debian 用户可以通过apt install ffmpeg来安装这2个库
./ffmpeg
https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe
./ffprobe
https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe
如果你想使用最新的RMVPE人声音高提取算法，则你需要下载音高提取模型参数并放置于RVC根目录
https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/rmvpe.pt
```
之后使用以下指令来启动WebUI:
```bash
python infer-web.py
```
如果你正在使用Windows，你可以直接下载并解压`RVC-beta.7z`，运行`go-web.bat`以启动WebUI。
仓库内还有一份`小白简易教程.doc`以供参考。
## 参考项目
+ [ContentVec](https://github.com/auspicious3000/contentvec/)
+ [VITS](https://github.com/jaywalnut310/vits)
+ [HIFIGAN](https://github.com/jik876/hifi-gan)
+ [Gradio](https://github.com/gradio-app/gradio)
+ [FFmpeg](https://github.com/FFmpeg/FFmpeg)
+ [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui)
+ [audio-slicer](https://github.com/openvpi/audio-slicer)
+ [Vocal pitch extraction:RMVPE](https://github.com/Dream-High/RMVPE)
+ The pretrained model is trained and tested by [yxlllc](https://github.com/yxlllc/RMVPE) and [RVC-Boss](https://github.com/RVC-Boss).
## 感谢所有贡献者作出的努力
<a href="https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=RVC-Project/Retrieval-based-Voice-Conversion-WebUI" />
</a>
|
LeeRuben/cppe5_use_data_finetuning
|
LeeRuben
| 2023-10-19T10:18:10Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-19T01:46:31Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: cppe5_use_data_finetuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cppe5_use_data_finetuning
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
RIOLITE/products_matching_aumet_fine_tune_2023-10-19
|
RIOLITE
| 2023-10-19T10:07:05Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-19T10:06:49Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Expert68/llama2_peft_v3
|
Expert68
| 2023-10-19T10:00:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T09:52:04Z |
---
library_name: 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: True
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
IlluminatiPudding/q-FrozenLake-v1-4x4-noSlippery
|
IlluminatiPudding
| 2023-10-19T09:51:20Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T09:51:17Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="IlluminatiPudding/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bh8648/POKO-12.8-qlora-split_1
|
bh8648
| 2023-10-19T09:51:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T09:01:29Z |
---
library_name: 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
hung200504/CliBert-20
|
hung200504
| 2023-10-19T09:43:40Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:emilyalsentzer/Bio_ClinicalBERT",
"base_model:finetune:emilyalsentzer/Bio_ClinicalBERT",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T09:38:41Z |
---
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
model-index:
- name: CliBert-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CliBert-20
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.9707 | 0.09 | 5 | 6.0008 |
| 5.9661 | 0.18 | 10 | 5.9998 |
| 5.9659 | 0.27 | 15 | 5.9987 |
| 5.9441 | 0.36 | 20 | 5.9974 |
| 5.9937 | 0.45 | 25 | 5.9965 |
| 5.9483 | 0.55 | 30 | 5.9953 |
| 5.9214 | 0.64 | 35 | 5.9945 |
| 5.9332 | 0.73 | 40 | 5.9937 |
| 5.9383 | 0.82 | 45 | 5.9926 |
| 5.9223 | 0.91 | 50 | 5.9919 |
| 5.9233 | 1.0 | 55 | 5.9911 |
| 5.8762 | 1.09 | 60 | 5.9905 |
| 5.8967 | 1.18 | 65 | 5.9899 |
| 5.9024 | 1.27 | 70 | 5.9891 |
| 5.9122 | 1.36 | 75 | 5.9885 |
| 5.902 | 1.45 | 80 | 5.9879 |
| 5.874 | 1.55 | 85 | 5.9872 |
| 5.8774 | 1.64 | 90 | 5.9867 |
| 5.8782 | 1.73 | 95 | 5.9862 |
| 5.8664 | 1.82 | 100 | 5.9857 |
| 5.8833 | 1.91 | 105 | 5.9852 |
| 5.8488 | 2.0 | 110 | 5.9848 |
| 5.8747 | 2.09 | 115 | 5.9843 |
| 5.8333 | 2.18 | 120 | 5.9840 |
| 5.8573 | 2.27 | 125 | 5.9837 |
| 5.8398 | 2.36 | 130 | 5.9834 |
| 5.8371 | 2.45 | 135 | 5.9832 |
| 5.8274 | 2.55 | 140 | 5.9831 |
| 5.863 | 2.64 | 145 | 5.9829 |
| 5.8183 | 2.73 | 150 | 5.9827 |
| 5.8448 | 2.82 | 155 | 5.9826 |
| 5.9111 | 2.91 | 160 | 5.9825 |
| 5.8316 | 3.0 | 165 | 5.9825 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
kowalsky/Reinforce-CartPole-v1
|
kowalsky
| 2023-10-19T09:36:08Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T09:35:58Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Junfeng/ludwig
|
Junfeng
| 2023-10-19T09:35:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T09:35:18Z |
---
library_name: 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: True
- 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: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
binwang/InstructDS
|
binwang
| 2023-10-19T09:29:34Z | 11 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2310.10981",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-18T04:26:45Z |
---
license: apache-2.0
---
Instructive Dialogue Summarization with Query Aggregations
Paper: https://arxiv.org/abs/2310.10981 (EMNLP 2023)
Github: https://github.com/BinWang28/InstructDS
|
hung200504/bert-20
|
hung200504
| 2023-10-19T09:27:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/bert-base-cased-squad2",
"base_model:finetune:deepset/bert-base-cased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T09:27:03Z |
---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-20
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.1509
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.3191 | 0.09 | 5 | 12.3262 |
| 11.4409 | 0.18 | 10 | 12.2742 |
| 11.0259 | 0.27 | 15 | 12.2224 |
| 11.1517 | 0.36 | 20 | 12.1706 |
| 11.1256 | 0.45 | 25 | 12.1189 |
| 11.1448 | 0.55 | 30 | 12.0668 |
| 10.4156 | 0.64 | 35 | 12.0150 |
| 11.0415 | 0.73 | 40 | 11.9641 |
| 10.5847 | 0.82 | 45 | 11.9123 |
| 10.4663 | 0.91 | 50 | 11.8612 |
| 10.2171 | 1.0 | 55 | 11.8098 |
| 10.4121 | 1.09 | 60 | 11.7587 |
| 10.5127 | 1.18 | 65 | 11.7082 |
| 10.7149 | 1.27 | 70 | 11.6574 |
| 10.2304 | 1.36 | 75 | 11.6066 |
| 10.6459 | 1.45 | 80 | 11.5557 |
| 10.3551 | 1.55 | 85 | 11.5044 |
| 10.2838 | 1.64 | 90 | 11.4531 |
| 10.0924 | 1.73 | 95 | 11.4025 |
| 10.295 | 1.82 | 100 | 11.3513 |
| 9.8206 | 1.91 | 105 | 11.3005 |
| 10.1365 | 2.0 | 110 | 11.2498 |
| 10.0496 | 2.09 | 115 | 11.1992 |
| 9.8465 | 2.18 | 120 | 11.1489 |
| 9.9778 | 2.27 | 125 | 11.0980 |
| 10.0708 | 2.36 | 130 | 11.0471 |
| 9.6465 | 2.45 | 135 | 10.9962 |
| 9.9864 | 2.55 | 140 | 10.9461 |
| 9.5175 | 2.64 | 145 | 10.8963 |
| 9.9675 | 2.73 | 150 | 10.8461 |
| 9.7013 | 2.82 | 155 | 10.7963 |
| 9.6324 | 2.91 | 160 | 10.7461 |
| 9.7833 | 3.0 | 165 | 10.6960 |
| 9.6806 | 3.09 | 170 | 10.6461 |
| 9.6208 | 3.18 | 175 | 10.5964 |
| 9.3067 | 3.27 | 180 | 10.5468 |
| 9.1504 | 3.36 | 185 | 10.4972 |
| 9.8082 | 3.45 | 190 | 10.4474 |
| 9.3738 | 3.55 | 195 | 10.3978 |
| 9.1904 | 3.64 | 200 | 10.3478 |
| 9.0302 | 3.73 | 205 | 10.2981 |
| 8.8785 | 3.82 | 210 | 10.2490 |
| 8.8765 | 3.91 | 215 | 10.1997 |
| 9.3 | 4.0 | 220 | 10.1509 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
chucksylar/squad-bloom-1b7
|
chucksylar
| 2023-10-19T09:21:41Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b7",
"base_model:adapter:bigscience/bloom-1b7",
"region:us"
] | null | 2023-10-19T09:21:04Z |
---
library_name: peft
base_model: bigscience/bloom-1b7
---
# 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]
- **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 Data 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 Data 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]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
aXlireza/persian_text_classification
|
aXlireza
| 2023-10-19T09:20:27Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"classification",
"nlp",
"fa",
"en",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-19T04:01:41Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
- classification
- nlp
model-index:
- name: Alireza01/text_classification3
results: []
language:
- fa
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
<!-- 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. -->
# Alireza01/text_classification3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2767
- Train Accuracy: 0.9548
- Epoch: 11
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 31380, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Epoch |
|:----------:|:--------------:|:-----:|
| 1.2335 | 0.5798 | 0 |
| 1.1317 | 0.5769 | 1 |
| 1.0821 | 0.6010 | 2 |
| 1.0341 | 0.6154 | 3 |
| 0.9771 | 0.6365 | 4 |
| 0.8846 | 0.7192 | 5 |
| 0.7647 | 0.75 | 6 |
| 0.6556 | 0.7971 | 7 |
| 0.5725 | 0.8865 | 8 |
| 0.4555 | 0.9365 | 9 |
| 0.3683 | 0.9423 | 10 |
| 0.2767 | 0.9548 | 11 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
zhouwenwangtage/sd-class-butterflies-64
|
zhouwenwangtage
| 2023-10-19T09:10:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"region:us"
] |
unconditional-image-generation
| 2023-10-19T09:08:36Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('zhouwenwangtage/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
hung200504/bert-19
|
hung200504
| 2023-10-19T09:07:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/bert-base-cased-squad2",
"base_model:finetune:deepset/bert-base-cased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T09:07:21Z |
---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-19
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 11.2343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.3192 | 0.09 | 5 | 12.3265 |
| 11.4416 | 0.18 | 10 | 12.2762 |
| 11.0285 | 0.27 | 15 | 12.2265 |
| 11.1568 | 0.36 | 20 | 12.1786 |
| 11.1352 | 0.45 | 25 | 12.1313 |
| 11.1596 | 0.55 | 30 | 12.0857 |
| 10.4352 | 0.64 | 35 | 12.0412 |
| 11.0699 | 0.73 | 40 | 11.9982 |
| 10.6195 | 0.82 | 45 | 11.9567 |
| 10.5109 | 0.91 | 50 | 11.9161 |
| 10.2699 | 1.0 | 55 | 11.8766 |
| 10.4784 | 1.09 | 60 | 11.8384 |
| 10.5932 | 1.18 | 65 | 11.8018 |
| 10.8098 | 1.27 | 70 | 11.7661 |
| 10.3369 | 1.36 | 75 | 11.7312 |
| 10.7722 | 1.45 | 80 | 11.6981 |
| 10.4952 | 1.55 | 85 | 11.6657 |
| 10.4398 | 1.64 | 90 | 11.6341 |
| 10.2621 | 1.73 | 95 | 11.6045 |
| 10.4932 | 1.82 | 100 | 11.5753 |
| 10.0321 | 1.91 | 105 | 11.5481 |
| 10.3808 | 2.0 | 110 | 11.5216 |
| 10.3108 | 2.09 | 115 | 11.4966 |
| 10.1234 | 2.18 | 120 | 11.4725 |
| 10.2887 | 2.27 | 125 | 11.4492 |
| 10.4092 | 2.36 | 130 | 11.4274 |
| 9.9991 | 2.45 | 135 | 11.4068 |
| 10.3832 | 2.55 | 140 | 11.3872 |
| 9.937 | 2.64 | 145 | 11.3692 |
| 10.4397 | 2.73 | 150 | 11.3521 |
| 10.1919 | 2.82 | 155 | 11.3364 |
| 10.1394 | 2.91 | 160 | 11.3214 |
| 10.3371 | 3.0 | 165 | 11.3080 |
| 10.2649 | 3.09 | 170 | 11.2953 |
| 10.2511 | 3.18 | 175 | 11.2844 |
| 9.9485 | 3.27 | 180 | 11.2741 |
| 9.8203 | 3.36 | 185 | 11.2649 |
| 10.559 | 3.45 | 190 | 11.2574 |
| 10.1233 | 3.55 | 195 | 11.2504 |
| 9.9711 | 3.64 | 200 | 11.2451 |
| 9.8388 | 3.73 | 205 | 11.2407 |
| 9.7467 | 3.82 | 210 | 11.2373 |
| 9.7465 | 3.91 | 215 | 11.2350 |
| 10.3259 | 4.0 | 220 | 11.2343 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Against61/SNT_BOT
|
Against61
| 2023-10-19T09:07:23Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-10-18T11:50:35Z |
---
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: SNT_BOT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SNT_BOT
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
jason1i/Llama-2-7b-hf-openassistant-guanaco
|
jason1i
| 2023-10-19T09:06:12Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-10-19T07:51:31Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-openassistant-guanaco
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-openassistant-guanaco
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4044
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4456 | 1.0 | 12 | 1.4044 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
Waterfront/Llama-2-7b-chat-hf-social-media-captions-10k
|
Waterfront
| 2023-10-19T09:04:22Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-10-19T06:39:43Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: Llama-2-7b-chat-hf-social-media-captions-10k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-chat-hf-social-media-captions-10k
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
mnavas/roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
|
mnavas
| 2023-10-19T09:03:44Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-16T18:32:34Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8644
- Accuracy: 0.8387
- F1: 0.8387
- Precision: 0.8387
- Recall: 0.8387
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 95 | 2.3654 | 0.4409 | 0.4409 | 0.4409 | 0.4409 |
| No log | 2.0 | 190 | 1.8455 | 0.5269 | 0.5269 | 0.5269 | 0.5269 |
| No log | 3.0 | 285 | 1.4468 | 0.6344 | 0.6344 | 0.6344 | 0.6344 |
| No log | 4.0 | 380 | 1.1099 | 0.7419 | 0.7419 | 0.7419 | 0.7419 |
| No log | 5.0 | 475 | 1.0515 | 0.7634 | 0.7634 | 0.7634 | 0.7634 |
| 1.6355 | 6.0 | 570 | 0.9938 | 0.7312 | 0.7312 | 0.7312 | 0.7312 |
| 1.6355 | 7.0 | 665 | 0.8275 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 1.6355 | 8.0 | 760 | 0.8344 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 1.6355 | 9.0 | 855 | 0.8516 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 1.6355 | 10.0 | 950 | 0.8723 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 11.0 | 1045 | 0.8644 | 0.8387 | 0.8387 | 0.8387 | 0.8387 |
| 0.2827 | 12.0 | 1140 | 0.9343 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 0.2827 | 13.0 | 1235 | 1.0181 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 14.0 | 1330 | 1.0068 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 15.0 | 1425 | 1.0085 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 0.0485 | 16.0 | 1520 | 1.0257 | 0.8280 | 0.8280 | 0.8280 | 0.8280 |
| 0.0485 | 17.0 | 1615 | 1.0305 | 0.8172 | 0.8172 | 0.8172 | 0.8172 |
| 0.0485 | 18.0 | 1710 | 1.0648 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.0485 | 19.0 | 1805 | 1.0677 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.0485 | 20.0 | 1900 | 1.0687 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
csukuangfj/vits-vctk
|
csukuangfj
| 2023-10-19T09:02:48Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-10-16T01:57:32Z |
---
license: apache-2.0
---
# Introduction
[./pretrained_vctk.pth](./pretrained_vctk.pth)
is downloaded from
<https://drive.google.com/file/d/11aHOlhnxzjpdWDpsz1vFDCzbeEfoIxru/view?usp=drive_link>
[./vctk_base.json](./ljs_base.json) is downloaded from
<https://github.com/jaywalnut310/vits/blob/main/configs/vctk_base.json>
[./CMU-in-IPA.zip](./CMU-in-IPA.zip)
is downloaded from
<https://people.umass.edu/nconstan/CMU-IPA/CMU-in-IPA.zip>
[./CMU.in.IPA.txt](./CMU.in.IPA.txt) is extracted
from [./CMU-in-IPA.zip](./CMU-in-IPA.zip)
|
aquinovo/llama-2-70b-dexter-4kdataset-3500epochs-adapter
|
aquinovo
| 2023-10-19T09:00:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T08:59:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
HiFromAviral/VerificationDocumentClassifier
|
HiFromAviral
| 2023-10-19T08:58:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-18T06:35:28Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: VerificationDocumentClassifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# VerificationDocumentClassifier
AI Model To Classify Verification Documents.
Classifies If Uploaded Document Is Liheap Or Medicaid
|
tadsatlawa/nanoBERT
|
tadsatlawa
| 2023-10-19T08:52:43Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"biology",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-16T09:02:01Z |
---
license: bsd-3-clause
tags:
- biology
widget:
- text: "EV<mask>LVESGGGLVQPGGSLRLSCAASGFTFSSYNMNWVRQAPGKGLEWVSYISSSSSTIYYADSVKGRFTISRDNAKNSLSLQMNSLRDEDTAVYYCARAYYYGMDVWGQGTTVTVSS"
---
# Model Card for nanoBERT
nanoBERT is a nanobody-specific transformer to predict amino acids in a given position in a query sequence.
The model was trained on nanobody sequences from [INDI (Integrated Nanobody Database for Immunoinformatics)](https://pubmed.ncbi.nlm.nih.gov/34747487/)
Example usage: [notebook](example.ipynb).
For more information please contact: contact@naturalantibody.com
|
merve/emoji-dreambooth-trained-xl
|
merve
| 2023-10-19T08:51:55Z | 4 | 6 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-10-12T16:18:09Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a TOK emoji
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - merve/emoji-dreambooth-trained-xl
You can enter the prompt: "a TOK emoji as" and then add what you want, e.g. "a TOK emoji as baby yoda".
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a TOK emoji using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
KayabaEngine/ppo-Pyramids
|
KayabaEngine
| 2023-10-19T08:51:04Z | 13 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-10-19T06:44:18Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: KayabaEngine/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hung200504/bert-18
|
hung200504
| 2023-10-19T08:48:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/bert-base-cased-squad2",
"base_model:finetune:deepset/bert-base-cased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T08:47:44Z |
---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-18
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 8.3340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.2713 | 0.09 | 5 | 12.1606 |
| 11.199 | 0.18 | 10 | 11.9115 |
| 10.6074 | 0.27 | 15 | 11.6709 |
| 10.5475 | 0.36 | 20 | 11.4407 |
| 10.3761 | 0.45 | 25 | 11.2173 |
| 10.2166 | 0.55 | 30 | 11.0033 |
| 9.4143 | 0.64 | 35 | 10.7983 |
| 9.8307 | 0.73 | 40 | 10.6034 |
| 9.3026 | 0.82 | 45 | 10.4169 |
| 9.0636 | 0.91 | 50 | 10.2387 |
| 8.7689 | 1.0 | 55 | 10.0700 |
| 8.7969 | 1.09 | 60 | 9.9094 |
| 8.7596 | 1.18 | 65 | 9.7588 |
| 8.8433 | 1.27 | 70 | 9.6152 |
| 8.3576 | 1.36 | 75 | 9.4808 |
| 8.6226 | 1.45 | 80 | 9.3540 |
| 8.3176 | 1.55 | 85 | 9.2346 |
| 8.2174 | 1.64 | 90 | 9.1231 |
| 8.0514 | 1.73 | 95 | 9.0198 |
| 8.0813 | 1.82 | 100 | 8.9240 |
| 7.6971 | 1.91 | 105 | 8.8362 |
| 7.865 | 2.0 | 110 | 8.7562 |
| 7.7614 | 2.09 | 115 | 8.6834 |
| 7.6525 | 2.18 | 120 | 8.6179 |
| 7.7074 | 2.27 | 125 | 8.5593 |
| 7.7802 | 2.36 | 130 | 8.5073 |
| 7.4788 | 2.45 | 135 | 8.4625 |
| 7.6863 | 2.55 | 140 | 8.4245 |
| 7.3113 | 2.64 | 145 | 8.3934 |
| 7.6127 | 2.73 | 150 | 8.3692 |
| 7.471 | 2.82 | 155 | 8.3509 |
| 7.4979 | 2.91 | 160 | 8.3393 |
| 7.5977 | 3.0 | 165 | 8.3340 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
dim-tsoukalas/ppo-Huggy
|
dim-tsoukalas
| 2023-10-19T08:45:08Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-19T08:45:01Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dim-tsoukalas/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hung200504/bert-17
|
hung200504
| 2023-10-19T08:40:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/bert-base-cased-squad2",
"base_model:finetune:deepset/bert-base-cased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-19T08:40:02Z |
---
license: cc-by-4.0
base_model: deepset/bert-base-cased-squad2
tags:
- generated_from_trainer
model-index:
- name: bert-17
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-17
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7381
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.0352 | 0.09 | 5 | 11.3392 |
| 10.0155 | 0.18 | 10 | 10.1330 |
| 8.6139 | 0.27 | 15 | 9.0228 |
| 7.7654 | 0.36 | 20 | 8.0477 |
| 7.1161 | 0.45 | 25 | 7.2438 |
| 6.486 | 0.55 | 30 | 6.6691 |
| 5.9793 | 0.64 | 35 | 6.3524 |
| 5.8845 | 0.73 | 40 | 6.2251 |
| 5.8619 | 0.82 | 45 | 6.1625 |
| 5.7536 | 0.91 | 50 | 6.1058 |
| 5.6831 | 1.0 | 55 | 6.0479 |
| 5.5525 | 1.09 | 60 | 5.9939 |
| 5.4714 | 1.18 | 65 | 5.9510 |
| 5.4384 | 1.27 | 70 | 5.9123 |
| 5.4539 | 1.36 | 75 | 5.8817 |
| 5.4073 | 1.45 | 80 | 5.8593 |
| 5.4048 | 1.55 | 85 | 5.8395 |
| 5.2997 | 1.64 | 90 | 5.8225 |
| 5.2388 | 1.73 | 95 | 5.8099 |
| 5.2564 | 1.82 | 100 | 5.7986 |
| 5.1758 | 1.91 | 105 | 5.7872 |
| 5.1926 | 2.0 | 110 | 5.7800 |
| 4.9244 | 2.09 | 115 | 5.7747 |
| 5.0897 | 2.18 | 120 | 5.7689 |
| 5.2493 | 2.27 | 125 | 5.7610 |
| 5.0594 | 2.36 | 130 | 5.7541 |
| 5.0792 | 2.45 | 135 | 5.7485 |
| 4.9952 | 2.55 | 140 | 5.7455 |
| 4.8796 | 2.64 | 145 | 5.7436 |
| 4.9344 | 2.73 | 150 | 5.7418 |
| 5.2387 | 2.82 | 155 | 5.7402 |
| 5.0734 | 2.91 | 160 | 5.7385 |
| 5.0227 | 3.0 | 165 | 5.7381 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
LoneStriker/Euryale-1.3-L2-70B-6.0bpw-h6-exl2
|
LoneStriker
| 2023-10-19T08:37:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-19T08:32:46Z |
---
license: llama2
language:
- en
---
17th Attempt. Past 10 Failed, cost me >$200 lol.
Idea is an updated version of Euryale with ReMantik instead of the ties-merge between the original 3 models.
This is then mixed with a saucy model with a Mythomax-esque Ratio, and a certain experimental (self) LoRA applied to it.
Test Results: Works Well.
<br>NSFL and NSFW fine in roleplay context.
<br>slight censor with 0 context, zero issues in actual RP / ERP.
<br>Good Prose, Not Dumbed Down due to RP merges from testing.
<br> I have not encountered any repetition issues some had with the original Euryale. tell me if you do, though.
Prompt and System Format:
most works well. I recommend Alpaca.
ST Settings used for Test:
Lightning 1.1 System Prompt + Shortwave(1.2 Temperature)
Support me [here](https://ko-fi.com/sao10k) :)
Quants done by TheBloke! Ty a lot to him.
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GPTQ
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GGUF
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-AWQ
|
KayabaEngine/a2c-PandaReachDense-v3
|
KayabaEngine
| 2023-10-19T08:36:55Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T08:31:27Z |
---
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: -0.25 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sseth/path-to-save-model-table
|
sseth
| 2023-10-19T08:32:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-19T07:25:12Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks table
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sseth/path-to-save-model-table
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks table using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
riteshm06/zephyr-support-chatbot
|
riteshm06
| 2023-10-19T08:31:36Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2023-10-19T07:54:48Z |
---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
wordcab/llama-natural-instructions-7b
|
wordcab
| 2023-10-19T08:29:53Z | 4 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"peft",
"en",
"dataset:Muennighoff/natural-instructions",
"arxiv:2106.09685",
"arxiv:2302.13971",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-07T07:17:51Z |
---
language:
- en
library_name: transformers
tags:
- peft
- llama
datasets:
- Muennighoff/natural-instructions
pipeline_tag: text-generation
base_model: decapoda-research/llama-7b-hf
---
# LoRA LLaMA Natural Instructions
This model is a fine-tuned version of [llama-7b](https://huggingface.co/decapoda-research/llama-7b-hf) from [Meta](https://huggingface.co/facebook),
on the [Natural Instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions) dataset from [AllenAI](https://huggingface.co/allenai),
using the [LoRA](https://arxiv.org/pdf/2106.09685.pdf) training technique.
⚠️ **This model is for Research purpose only (See the [license](https://huggingface.co/decapoda-research/llama-7b-hf/blob/main/LICENSE))**
## WandB Report
Click on the badge below to see the full report on Weights & Biases.
[](https://api.wandb.ai/links/chainyo-mleng/ia2mloow)
## Usage
### Installation
```bash
pip install loralib bitsandbytes datasets git+https://github.com/huggingface/peft.git git+https://github.com/huggingface/transformers.git sentencepiece
```
### Format of the input
The input should be a string of text with the following format:
```python
prompt_template = {
"prompt": "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
"response": "### Response:"
}
def generate_prompt(
definition: str,
inputs: str,
targets: Union[None, str] = None,
) -> str:
"""Generate a prompt from instruction and input."""
res = prompt_template["prompt"].format(
instruction=definition, input=inputs
)
if targets:
res = f"{res}{targets}"
return res
def get_response(output: str) -> str:
"""Get the response from the output."""
return output.split(prompt_template["response"])[1].strip()
```
Feel free to use these utility functions to generate the prompt and to extract the response from the model output.
- `definition` is the instruction describing the task. It's generally a single sentence explaining the expected output and
the reasoning steps to follow.
- `inputs` is the input to the task. It can be a single sentence or a paragraph. It's the context used by the model to
generate the response to the task.
- `targets` is the expected output of the task. It's used for training the model. _It's not required for inference._
### Inference
You can load the model using only the adapters or load the full model with the adapters and the weights.
#### The tokenizer
```python
from transformers import LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained("wordcab/llama-natural-instructions-7b")
tokenizer.padding_side = "left"
tokenizer.pad_token_id = (0)
```
#### Load the model with the adapters
```python
from peft import PeftModel
from transformers import LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
"wordcab/llama-natural-instructions-7b",
torch_dtype=torch.float16,
device_map={"": 0},
)
```
#### Load the full model
```python
model = LlamaForCausalLM.from_pretrained(
"wordcab/llama-natural-instructions-7b",
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto",
)
```
#### Evaluation mode
Don't forget to put the model in evaluation mode. And if you are using PyTorch v2.0 or higher don't forget to call
the compile method.
```python
model.eval()
if torch.__version__ >= "2":
model = torch.compile(model)
```
#### Generate the response
```python
prompt = generate_prompt(
"In this task, you have to analyze the full sentences and do reasoning and quick maths to find the correct answer.",
f"You are now a superbowl star. You are the quarterback of the team. Your team is down by 3 points. You are in the last 2 minutes of the game. The other team has a score of 28. What is the score of your team?",
)
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=2048)
input_ids = inputs["input_ids"].to(model.device)
generation_config = GenerationConfig(
temperature=0.2,
top_p=0.75,
top_k=40,
num_beams=4,
)
with torch.no_grad():
gen_outputs = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=50,
)
s = gen_outputs.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
response = prompter.get_response(output)
print(response)
>>> 25
```
You can try with other prompts that are not maths related as well! :hugs:
## Beanchmark
We benchmarked our model on the following tasks: [BoolQ](https://huggingface.co/datasets/boolq), [PIQA](https://huggingface.co/datasets/piqa), [WinoGrande](https://huggingface.co/datasets/winogrande), [OpenBookQA](https://huggingface.co/datasets/openbookqa).
| | BoolQ | PIQA | WinoGrande | OpenBookQA | Precision | Inference time (s) |
| --- | --- | --- | --- | --- | --- | --- |
| Original LLaMA 7B | 76.5 | 79.8 | 70.1 | 57.2 | fp32 | 3 seconds |
| Original LLaMA 13B | 78.1 | 80.1 | 73 | 56.4 | fp32 | >5 seconds |
| LoRA LLaMA 7B | 63.9 | 51.3 | 48.9 | 31.4 | 8bit | 0.65 seconds |
| LoRA LLaMA 13B | 70 | 63.93 | 51.6 | 50.4 | 8bit | 1.2 seconds |
__Link to the 13B model:__ [wordcab/llama-natural-instructions-13b](https://huggingface.co/wordcab/llama-natural-instructions-13b)
Overall our LoRA model is less performant than the original model from Meta, if we compare the results from the [original paper](https://arxiv.org/pdf/2302.13971.pdf).
The performance degradation is due to the fact we load the model in 8bit and we use the adapters from the LoRA training.
Thanks to the 8bit quantization, the model is 4 times faster than the original model and the results are still decent.
Some complex tasks like WinoGrande and OpenBookQA are more difficult to solve with the adapters.
## Training Hardware
This model was trained on a single NVIDIA RTX 3090 GPU.
|
nerdygene/my_awesome_eli5_mlm_model
|
nerdygene
| 2023-10-19T08:26:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-19T08:05:15Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2422 | 1.0 | 1143 | 2.0355 |
| 2.1556 | 2.0 | 2286 | 2.0191 |
| 2.1262 | 3.0 | 3429 | 1.9869 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
confunius/my_awesome_eli5_mlm_model
|
confunius
| 2023-10-19T08:22:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-19T08:02:58Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2469 | 1.0 | 1136 | 2.0728 |
| 2.1803 | 2.0 | 2272 | 2.0424 |
| 2.1064 | 3.0 | 3408 | 1.9972 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Wangtheguy/my_awesome_eli5_mlm_model
|
Wangtheguy
| 2023-10-19T08:22:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-19T08:03:29Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2284 | 1.0 | 1146 | 2.0924 |
| 2.145 | 2.0 | 2292 | 2.0147 |
| 2.1011 | 3.0 | 3438 | 1.9970 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
RaitoGS/my_awesome_eli5_mlm_model
|
RaitoGS
| 2023-10-19T08:22:46Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-19T08:02:54Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2371 | 1.0 | 1140 | 2.0588 |
| 2.1394 | 2.0 | 2280 | 2.0322 |
| 2.104 | 3.0 | 3420 | 2.0193 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
WasuratS/wasurats_emotional_classification_model
|
WasuratS
| 2023-10-19T08:15:44Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"en",
"dataset:dair-ai/emotion",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-12T02:25:15Z |
---
datasets:
- dair-ai/emotion
language:
- en
pipeline_tag: text-classification
---
|
andreydung/q-Taxi
|
andreydung
| 2023-10-19T08:11:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T08:11:27Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="andreydung/q-Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
glimmerz/xlmroberta-ner-multilingual
|
glimmerz
| 2023-10-19T07:57:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"en",
"de",
"dataset:tner/wikiann",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-05T13:22:50Z |
---
license: apache-2.0
datasets:
- tner/wikiann
language:
- en
- de
metrics:
- precision
- recall
- f1
library_name: transformers
pipeline_tag: token-classification
---
|
LoneStriker/Euryale-1.3-L2-70B-5.0bpw-h6-exl2
|
LoneStriker
| 2023-10-19T07:48:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-19T07:45:54Z |
---
license: llama2
language:
- en
---
17th Attempt. Past 10 Failed, cost me >$200 lol.
Idea is an updated version of Euryale with ReMantik instead of the ties-merge between the original 3 models.
This is then mixed with a saucy model with a Mythomax-esque Ratio, and a certain experimental (self) LoRA applied to it.
Test Results: Works Well.
<br>NSFL and NSFW fine in roleplay context.
<br>slight censor with 0 context, zero issues in actual RP / ERP.
<br>Good Prose, Not Dumbed Down due to RP merges from testing.
<br> I have not encountered any repetition issues some had with the original Euryale. tell me if you do, though.
Prompt and System Format:
most works well. I recommend Alpaca.
ST Settings used for Test:
Lightning 1.1 System Prompt + Shortwave(1.2 Temperature)
Support me [here](https://ko-fi.com/sao10k) :)
Quants done by TheBloke! Ty a lot to him.
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GPTQ
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GGUF
https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-AWQ
|
seige-ml/my_awesome_food_model
|
seige-ml
| 2023-10-19T07:37:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-16T22:26:00Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3333333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0961
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.84 | 4 | 1.1132 | 0.32 |
| No log | 1.89 | 9 | 1.0985 | 0.3267 |
| 1.1116 | 2.53 | 12 | 1.0961 | 0.3333 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
jluckyboyj/hoa-7b-fine-tuning_grade_exam12
|
jluckyboyj
| 2023-10-19T07:24:27Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T05:54:24Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
Mridul/bert-hate-speech
|
Mridul
| 2023-10-19T07:18:00Z | 0 | 0 |
transformers
|
[
"transformers",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-19T06:27:36Z |
---
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
Bertfortextclassification, trained on hate speech dataseet
|
krishna-shinde/q-FrozenLake-v1-4x4-noSlippery
|
krishna-shinde
| 2023-10-19T07:13:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-19T07:13:06Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="krishna-shinde/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
joanoller/falcon-7b-qlora-chat-prestacions
|
joanoller
| 2023-10-19T07:05:38Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-18T11:02:35Z |
---
library_name: 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
### Framework versions
- PEFT 0.5.0
|
mirodavide/Mistral-neurips_dm
|
mirodavide
| 2023-10-19T06:59:03Z | 17 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-10-16T22:01:10Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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]
- **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 Data 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 Data 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]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
herMaster/pythia1.4B-finetuned-on-lamini-docs
|
herMaster
| 2023-10-19T06:57:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1.4b",
"base_model:finetune:EleutherAI/pythia-1.4b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-19T06:47:33Z |
---
license: apache-2.0
base_model: EleutherAI/pythia-1.4b
tags:
- generated_from_trainer
model-index:
- name: pythia1.4B-finetuned-on-lamini-docs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythia1.4B-finetuned-on-lamini-docs
This model is a fine-tuned version of [EleutherAI/pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 600
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
lorahub/flan_t5_xl-super_glue_cb
|
lorahub
| 2023-10-19T06:46:06Z | 6 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:45:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
MMeow223/Bird_Species_200
|
MMeow223
| 2023-10-19T06:45:31Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-10-19T03:58:46Z |
---
license: mit
---
This Bird_Species_200 model is purposed for COS30082 Applied Machine Learning, Assignment 1.
|
lorahub/flan_t5_xl-wiki_bio_key_content
|
lorahub
| 2023-10-19T06:45:06Z | 14 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:44:54Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-super_glue_multirc
|
lorahub
| 2023-10-19T06:44:28Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:44:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-dream_read_the_following_conversation_and_answer_the_question
|
lorahub
| 2023-10-19T06:44:08Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:43:52Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-adversarial_qa_dbert_tell_what_it_is
|
lorahub
| 2023-10-19T06:43:27Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:43:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-duorc_SelfRC_generate_question_by_answer
|
lorahub
| 2023-10-19T06:43:08Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:42:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-dbpedia_14_given_a_choice_of_categories_
|
lorahub
| 2023-10-19T06:42:45Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:42:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-adversarial_qa_droberta_tell_what_it_is
|
lorahub
| 2023-10-19T06:41:22Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:41:07Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-sentiment140
|
lorahub
| 2023-10-19T06:41:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:40:47Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-adversarial_qa_dbert_answer_the_following_q
|
lorahub
| 2023-10-19T06:40:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:40:06Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-kilt_tasks_hotpotqa_combining_facts
|
lorahub
| 2023-10-19T06:40:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:39:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
RikoteMaster/results_selected
|
RikoteMaster
| 2023-10-19T06:38:25Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-10-19T06:37:43Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: results_selected
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results_selected
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.14.5
- Tokenizers 0.14.1
|
lorahub/flan_t5_xl-adversarial_qa_dbidaf_based_on
|
lorahub
| 2023-10-19T06:38:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:38:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
femboysLover/Kohaku-fp16-XL
|
femboysLover
| 2023-10-19T06:37:34Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-09-23T12:47:08Z |
https://civitai.com/models/136389/kohaku-xl
|
lorahub/flan_t5_xl-web_questions_potential_correct_answer
|
lorahub
| 2023-10-19T06:36:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:36:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-wiki_qa_Topic_Prediction_Question_and_Answer_Pair
|
lorahub
| 2023-10-19T06:36:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:36:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-ropes_given_background_situation
|
lorahub
| 2023-10-19T06:35:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:35:44Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-snli
|
lorahub
| 2023-10-19T06:35:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:35:05Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-glue_sst2
|
lorahub
| 2023-10-19T06:35:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:34:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-super_glue_rte
|
lorahub
| 2023-10-19T06:33:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:33:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-squad_v1.1
|
lorahub
| 2023-10-19T06:32:46Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:32:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-wiki_bio_what_content
|
lorahub
| 2023-10-19T06:31:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:31:34Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-wiki_hop_original_choose_best_object_interrogative_1
|
lorahub
| 2023-10-19T06:31:28Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:31:14Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-ropes_background_situation_middle
|
lorahub
| 2023-10-19T06:31:08Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:30:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
lorahub/flan_t5_xl-adversarial_qa_droberta_answer_the_following_q
|
lorahub
| 2023-10-19T06:30:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-19T06:30:35Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
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