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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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11.7k
| library_name
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sally9805/saved_model
|
sally9805
| 2023-11-13T05:46:08Z | 29 | 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
| 2023-11-13T05:09:35Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sally9805/saved_model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
xiaoqijun/111
|
xiaoqijun
| 2023-11-13T05:26:25Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:THUDM/chatglm2-6b",
"base_model:adapter:THUDM/chatglm2-6b",
"region:us"
] | null | 2023-11-13T05:25:53Z |
---
library_name: peft
base_model: THUDM/chatglm2-6b
---
# 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
|
lmqg/mt5-base-zhquad-qag
|
lmqg
| 2023-11-13T05:25:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"questions and answers generation",
"zh",
"dataset:lmqg/qag_zhquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-13T05:03:30Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: zh
datasets:
- lmqg/qag_zhquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。"
example_title: "Questions & Answers Generation Example 1"
model-index:
- name: lmqg/mt5-base-zhquad-qag
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qag_zhquad
type: default
args: default
metrics:
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 73.57
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 74.12
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 73.07
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 49.76
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 49.92
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 49.62
---
# Model Card of `lmqg/mt5-base-zhquad-qag`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)
- **Language:** zh
- **Training data:** [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="zh", model="lmqg/mt5-base-zhquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-base-zhquad-qag")
output = pipe("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_zhquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 73.57 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedF1Score (MoverScore) | 49.76 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedPrecision (BERTScore) | 73.07 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedPrecision (MoverScore) | 49.62 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedRecall (BERTScore) | 74.12 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedRecall (MoverScore) | 49.92 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_zhquad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: None
- model: google/mt5-base
- max_length: 512
- max_length_output: 256
- epoch: 4
- batch: 2
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 32
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-zhquad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
vihangd/stableplats-3b-v1
|
vihangd
| 2023-11-13T05:23:07Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"stablelm_epoch",
"text-generation",
"custom_code",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-11-11T06:52:08Z |
---
license: cc-by-sa-4.0
---
<p><h1> StablePlats-3b </h1></p>
An experimental finetune of StableLM-3B-4E1T with Alpaca-QLoRA
<h2> Datasets </h2>
Trained on alpca style datasets
<p><h2> Prompt Template </h2></p>
Uses alpaca style prompt template
|
BlueWard/t5-small-with-generate-finetune-indosum
|
BlueWard
| 2023-11-13T05:13:22Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-13T03:23:33Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-with-generate-finetune-indosum
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. -->
# t5-small-with-generate-finetune-indosum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6542
- Rouge1: 0.2065
- Rouge2: 0.1572
- Rougel: 0.2026
- Rougelsum: 0.2026
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.7518 | 1.0 | 4460 | 0.6542 | 0.2065 | 0.1572 | 0.2026 | 0.2026 | 19.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.2
|
jaehoonkimm/my_awesome_model
|
jaehoonkimm
| 2023-11-13T05:06:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-11T04:16:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_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_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3247
- Accuracy: 0.8114
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 350 | 0.4073 | 0.8069 |
| 0.4154 | 2.0 | 700 | 0.4134 | 0.8192 |
| 0.2369 | 3.0 | 1050 | 0.4933 | 0.8133 |
| 0.2369 | 4.0 | 1400 | 0.7105 | 0.7994 |
| 0.1121 | 5.0 | 1750 | 1.0014 | 0.7881 |
| 0.0633 | 6.0 | 2100 | 1.0510 | 0.8036 |
| 0.0633 | 7.0 | 2450 | 1.1647 | 0.8074 |
| 0.0331 | 8.0 | 2800 | 1.2433 | 0.8012 |
| 0.0205 | 9.0 | 3150 | 1.3184 | 0.8055 |
| 0.0107 | 10.0 | 3500 | 1.3247 | 0.8114 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Hrithik2212/Dr.Llama2-7b-qlora-chat-experimental
|
Hrithik2212
| 2023-11-13T05:01:39Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-11-12T06:47:24Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
|
athirdpath/Eileithyia-7B-LORA
|
athirdpath
| 2023-11-13T05:00:48Z | 8 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:quantized:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-10T04:46:55Z |
---
license: apache-2.0
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- generated_from_trainer
model-index:
- name: Eileithyia-7B
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4546
## Model description
Eileithyia-7B is an unaligned, roleplay oriented model created by merging teknium/OpenHermes-2.5-Mistral-7B with a bespoke LORA trained directly on OpenHermes.
Eileithyia, as is the current trend, is named after a Greek goddess; in this case it is the goddess of childbirth and pregnancy.
## Training and evaluation data
The private ~400k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:
- Medical texts (on pregnancy, reproductive organs, and impregnation). These are formatted so the model, in character as a doctor, answers a patient's question in short to medium form.
- Excerpts from short stories and novellas (erotic, romantic, and platonic) centered around both realistic and fantastic pregnancy. These are sliced into ~2048 token chunks, and these long-form responses are all tied to the command “Enter narrator mode.” in the instructions.
- A selection from PIPPA, using a wide keyword search for related terms then human curated (...the things I’ve seen…). These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
- ~42k tokens of GPT-4 generated data on pregnancy from various characters’ perspectives, focusing on different responses and stages. Also includes a synopsis for each week in various styles.
- ~18k tokens of GPT-4 generated data on non-maternal role-playing from various characters’ perspectives, focusing on different situations and emotions. Includes many multi-turn conversations.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5629 | 0.75 | 25 | 1.6511 |
| 1.5253 | 1.5 | 50 | 1.5730 |
| 1.3363 | 2.25 | 75 | 1.5014 |
| 1.4017 | 2.99 | 100 | 1.4690 |
| 1.2677 | 3.74 | 125 | 1.4593 |
| 1.351 | 4.49 | 150 | 1.4546 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
athirdpath/Eileithyia-13B-LORA
|
athirdpath
| 2023-11-13T04:59:47Z | 7 | 2 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:KoboldAI/LLaMA2-13B-TiefighterLR",
"base_model:quantized:KoboldAI/LLaMA2-13B-TiefighterLR",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-10T05:57:23Z |
---
license: llama2
base_model: KoboldAI/LLaMA2-13B-TiefighterLR
tags:
- generated_from_trainer
model-index:
- name: Eileithyia-13B
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
This model is a fine-tuned version of [KoboldAI/LLaMA2-13B-TiefighterLR](https://huggingface.co/KoboldAI/LLaMA2-13B-TiefighterLR) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9844
## Model description
Eileithyia-13B is an unaligned, roleplay oriented model created by merging KoboldAI/LLaMA2-13B-TiefighterLR with a bespoke LORA trained directly on TiefighterLR.
Eileithyia, as is the current trend, is named after a Greek goddess; in this case it is the goddess of childbirth and pregnancy.
## Training and evaluation data
The private ~400k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:
- Medical texts (on pregnancy, reproductive organs, and impregnation). These are formatted so the model, in character as a doctor, answers a patient's question in short to medium form.
- Excerpts from short stories and novellas (erotic, romantic, and platonic) centered around both realistic and fantastic pregnancy. These are sliced into ~2048 token chunks, and these long-form responses are all tied to the command “Enter narrator mode.” in the instructions.
- A selection from PIPPA, using a wide keyword search for related terms then human curated (...the things I’ve seen…). These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
- ~42k tokens of GPT-4 generated data on pregnancy from various characters’ perspectives, focusing on different responses and stages. Also includes a synopsis for each week in various styles.
- ~18k tokens of GPT-4 generated data on non-maternal role-playing from various characters’ perspectives, focusing on different situations and emotions. Includes many multi-turn conversations.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8419 | 0.75 | 25 | 2.5257 |
| 1.7748 | 1.5 | 50 | 2.2467 |
| 1.813 | 2.25 | 75 | 2.0914 |
| 1.8067 | 2.99 | 100 | 2.0235 |
| 1.5346 | 3.74 | 125 | 1.9939 |
| 1.5869 | 4.49 | 150 | 1.9844 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
athirdpath/Nethena-20b-Glue-LORA
|
athirdpath
| 2023-11-13T04:58:14Z | 5 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:NeverSleep/Nethena-20B",
"base_model:quantized:NeverSleep/Nethena-20B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-10T03:41:43Z |
---
license: cc-by-nc-4.0
base_model: NeverSleep/Nethena-20B
tags:
- generated_from_trainer
model-index:
- name: lora-outA
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
This model is a fine-tuned version of [NeverSleep/Nethena-20B](https://huggingface.co/NeverSleep/Nethena-20B) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3864
## Model description
athirdpath/Nethena-20b-Glued-LORA is a 128 rank LORA for RP, trained on [NeverSleep/Nethena-20B](https://huggingface.co/NeverSleep/Nethena-20B). It is unalligned and NSFW-oriented.
This is a test, exploring the effects of "gluing" the components of the 20b model together to reduce the iconic word replacement errors, increase lucidity, and improve recall.
## Training and evaluation data
The private ~500k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:
- Medical texts (on psychology, reproductive organs, anatomy, and pregnancy). These are formatted so the model, in character as a doctor or therapist, answers a patient's question in short to medium form.
- Excerpts from short stories and novellas (erotic and romantic) centered around both realistic and fantastic situations, covering several fetishes as well. These are sliced into ~2048 token chunks, and these long-form responses are all tied to the command “Enter narrator mode.” in the instructions.
- A selection from PIPPA, using a wide keyword search for tokens associated with low quality human or AI data to remove those responses, then a positive search was done for words and phrases associated with a higher reading level. These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
- ~18k tokens of GPT-4 generated data on role-playing from various characters’ perspectives, focusing on different situations and emotions. Includes many multi-turn conversations.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.955 | 0.38 | 25 | 1.9037 |
| 1.6598 | 0.75 | 50 | 1.6192 |
| 1.5649 | 1.13 | 75 | 1.5010 |
| 1.4424 | 1.5 | 100 | 1.4424 |
| 1.4142 | 1.88 | 125 | 1.4068 |
| 1.4951 | 2.25 | 150 | 1.3908 |
| 1.4418 | 2.63 | 175 | 1.3864 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
runse/OPS-koalpaca-polyglot-12.8b
|
runse
| 2023-11-13T04:50:23Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-11-12T17:49:35Z |
---
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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
openskyml/midjourney-mini
|
openskyml
| 2023-11-13T04:22:14Z | 249 | 14 |
diffusers
|
[
"diffusers",
"midjourney",
"midjourney-mini",
"openskyml",
"text-to-image",
"en",
"ru",
"de",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-11T17:45:46Z |
---
license: mit
tags:
- midjourney
- midjourney-mini
- openskyml
pinned: true
language:
- en
- ru
- de
- fr
library_name: diffusers
pipeline_tag: text-to-image
---
<h1><center>Midjourney-mini</center></h1>
## Description
Midjourney-mini is a free artificial intelligence model that can create realistic images based on textual descriptions. It has the following advantages:
- **Free:** Midjourney-mini is completely free to use for anyone.
- **High-quality image generation:** The model uses modern deep learning methods to create high-quality images.
- **Ease of use:** Working with Midjourney-mini does not require special programming or machine learning knowledge. The model has a convenient interface and works in your browser.
Although Midjoureymini is a trimmed-down version of the paid MIjdoureny modle, it still provides powerful functionality and can be used in various applications.
# Use
## In Diffusers
```py
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("midjourney-community/midjourney-mini")
```
## Deploy in Spaces
```py
import gradio as gr
gr.Interface.load("models/midjourney-community/midjourney-mini").launch()
```
## Deploy in Inference API
```py
import requests
API_URL = "https://api-inference.huggingface.co/models/midjourney-community/midjourney-mini"
headers = {"Authorization": "Bearer hf_token"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
image_bytes = query({
"inputs": "Astronaut riding a horse",
})
```
```js
async function query(data) {
const response = await fetch(
"https://api-inference.huggingface.co/models/midjourney-community/midjourney-mini",
{
headers: { Authorization: "Bearer hf_token" },
method: "POST",
body: JSON.stringify(data),
}
);
const result = await response.blob();
return result;
}
query({"inputs": "Astronaut riding a horse"}).then((response) => {
// Use image
});
```
|
leowcs/ppo-LundaLander-RL-Tut
|
leowcs
| 2023-11-13T04:15:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T04:14:55Z |
---
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: 242.90 +/- 43.89
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
...
```
|
minh009/my_awesome_model
|
minh009
| 2023-11-13T04:08:33Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"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
| 2023-11-13T04:01:18Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: minh009/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# minh009/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9951
- Validation Loss: 0.8844
- Train Accuracy: 0.7955
- Epoch: 2
## 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': 125, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.4992 | 1.3056 | 0.5795 | 0 |
| 1.2073 | 1.0418 | 0.625 | 1 |
| 0.9951 | 0.8844 | 0.7955 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
tommylam/A2C-pandaReachDense-v3
|
tommylam
| 2023-11-13T03:55:45Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T03:50:01Z |
---
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: -0.18 +/- 0.07
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
...
```
|
nikxtaco/a2c-PandaReachDense-v3
|
nikxtaco
| 2023-11-13T03:38:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T03:22:14Z |
---
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: -0.14 +/- 0.10
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
...
```
|
orca3315/lora-trained-xl
|
orca3315
| 2023-11-13T03:34:23Z | 1 | 4 |
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
| 2023-11-10T04:29:33Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks toy
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - orca3315/lora-trained-xl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks toy 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.
|
yeye776/t5-brokarry-total-v6
|
yeye776
| 2023-11-13T03:00:05Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:paust/pko-t5-large",
"base_model:finetune:paust/pko-t5-large",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-13T02:57:34Z |
---
license: cc-by-4.0
base_model: paust/pko-t5-large
tags:
- generated_from_trainer
model-index:
- name: t5-brokarry-total-v6
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. -->
# t5-brokarry-total-v6
This model is a fine-tuned version of [paust/pko-t5-large](https://huggingface.co/paust/pko-t5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0007
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
bartowski/Autolycus-Mistral_7B-exl2
|
bartowski
| 2023-11-13T02:24:25Z | 1 | 0 | null |
[
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-13T00:03:25Z |
---
license: apache-2.0
language:
- en
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
quantized_by: bartowski
---
## Exllama v2 Quantizations of Autolycus-Mistral_7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/FPHam/Autolycus-Mistral_7B
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/3.75">3.75 bits per weight</a>
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/4.25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/5.0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/Autolycus-Mistral_7B-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Autolycus-Mistral_7B-exl2`:
```shell
mkdir Autolycus-Mistral_7B-exl2
huggingface-cli download bartowski/Autolycus-Mistral_7B-exl2 --local-dir Autolycus-Mistral_7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Autolycus-Mistral_7B-exl2
huggingface-cli download bartowski/Autolycus-Mistral_7B-exl2 --revision 4.0 --local-dir Autolycus-Mistral_7B-exl2 --local-dir-use-symlinks False
```
|
DylanJHJ/bert-base-final-v0-ep2
|
DylanJHJ
| 2023-11-13T01:56:16Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-11-13T01:24:24Z |
---
license: apache-2.0
---
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, KLDivLoss
from transformers.modeling_outputs import TokenClassifierOutput
from transformers import BertModel, BertPreTrainedModel
class BertForHighlightPrediction(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config, **model_kwargs):
super().__init__(config)
# self.model_args = model_kargs["model_args"]
self.num_labels = config.num_labels
self.bert = BertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.tokens_clf = nn.Linear(config.hidden_size, config.num_labels)
self.tau = model_kwargs.pop('tau', 1)
self.gamma = model_kwargs.pop('gamma', 1)
self.soft_labeling = model_kwargs.pop('soft_labeling', False)
self.init_weights()
self.softmax = nn.Softmax(dim=-1)
def forward(self,
input_ids=None,
probs=None, # soft-labeling
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,):
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
tokens_output = outputs[0]
highlight_logits = self.tokens_clf(self.dropout(tokens_output))
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
active_loss = attention_mask.view(-1) == 1
active_logits = highlight_logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss,
labels.view(-1),
torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss_ce = loss_fct(active_logits, active_labels)
loss_kl = 0
if self.soft_labeling:
loss_fct = KLDivLoss(reduction='sum')
active_mask = (attention_mask * token_type_ids).view(-1, 1) # BL 1
n_active = (active_mask == 1).sum()
active_mask = active_mask.repeat(1, 2) # BL 2
input_logp = F.log_softmax(active_logits / self.tau, -1) # BL 2
target_p = torch.cat(( (1-probs).view(-1, 1), probs.view(-1, 1)), -1) # BL 2
loss_kl = loss_fct(input_logp, target_p * active_mask) / n_active
loss = self.gamma * loss_ce + (1-self.gamma) * loss_kl
# print("Loss:\n")
# print(loss)
# print(loss_kl)
# print(loss_ce)
return TokenClassifierOutput(
loss=loss,
logits=highlight_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@torch.no_grad()
def inference(self, outputs):
with torch.no_grad():
outputs = self.forward(**batch_inputs)
probabilities = self.softmax(self.tokens_clf(outputs.hidden_states[-1]))
predictions = torch.argmax(probabilities, dim=-1)
# active filtering
active_tokens = batch_inputs['attention_mask'] == 1
active_predictions = torch.where(
active_tokens,
predictions,
torch.tensor(-1).type_as(predictions)
)
outputs = {
"probabilities": probabilities[:, :, 1].detach(), # shape: (batch, length)
"active_predictions": predictions.detach(),
"active_tokens": active_tokens,
}
return outputs
```
|
Asheron/Taxi-v3
|
Asheron
| 2023-11-13T01:51:27Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T01:51:25Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.81
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="Asheron/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"])
```
|
jcfneto/bert-tv-portuguese
|
jcfneto
| 2023-11-13T01:45:07Z | 5 | 2 |
transformers
|
[
"transformers",
"tf",
"bert",
"pretraining",
"pt",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-03T13:10:38Z |
---
license: mit
language:
- pt
model-index:
- name: bert-tv-portuguese
results: []
---
# BERT-TV
<img src="https://cdn-uploads.huggingface.co/production/uploads/6385e26cc12615765caa6afe/3lSkNEfW57BNudZIFyTH2.png" width=400 height=400>
Image generated by ChatGPT with DALL-E from OpenAI.
## Model description
BERT-TV is a BERT model specifically pre-trained from scratch on a dataset of television reviews in Brazilian Portuguese.
This model is tailored to grasp the nuances and specificities associated with the context and sentiment expressed in
television reviews. BERT-TV features 6 layers, 12 attention heads, and an embedding dimension of 768, making it adept at
handling NLP tasks related to television content in Portuguese.
## Usage ideas
- Sentiment analysis on television reviews in Portuguese
- Recommender systems for television models in Portuguese
- Text classification for different television brands and types in Portuguese
- Named entity recognition in television-related contexts in Portuguese
- Aspect extraction for features and specifications of televisions in Portuguese
- Text generation for summarizing television reviews in Portuguese
## Limitations and bias
As the BERT-TV model is exclusively pre-trained on television reviews in Brazilian Portuguese, its performance may be
limited when applied to other types of text or reviews in different languages. Furthermore, the model could inherit
biases present in the training data, which may influence its predictions or embeddings. The tokenizer is adapted from
the BERTimbau tokenizer, optimized for Brazilian Portuguese, thus it might not deliver optimal results with other
languages or Portuguese dialects.
## Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.1
- Datasets 2.11.0
- Tokenizers 0.13.3
|
unoooo/llama-7b-hf
|
unoooo
| 2023-11-13T01:24:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-13T01:02:43Z |
---
license: other
---
LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details.
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
judy93536/distilroberta-base-rb156k-ep40
|
judy93536
| 2023-11-13T01:02:52Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"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
| 2023-11-10T17:57:15Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-rb156k
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. -->
# distilroberta-base-rb156k
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2509
## 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: 7.2115e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.12
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.779 | 1.0 | 8994 | 1.6188 |
| 1.6848 | 2.0 | 17988 | 1.5515 |
| 1.6376 | 3.0 | 26982 | 1.5219 |
| 1.6208 | 4.0 | 35976 | 1.5021 |
| 1.6025 | 5.0 | 44970 | 1.4949 |
| 1.5731 | 6.0 | 53964 | 1.4753 |
| 1.5512 | 7.0 | 62958 | 1.4579 |
| 1.5305 | 8.0 | 71952 | 1.4457 |
| 1.5159 | 9.0 | 80946 | 1.4308 |
| 1.4967 | 10.0 | 89940 | 1.4212 |
| 1.4761 | 11.0 | 98934 | 1.4081 |
| 1.4607 | 12.0 | 107928 | 1.3979 |
| 1.4512 | 13.0 | 116922 | 1.3896 |
| 1.4394 | 14.0 | 125916 | 1.3816 |
| 1.431 | 15.0 | 134910 | 1.3755 |
| 1.4226 | 16.0 | 143904 | 1.3639 |
| 1.4032 | 17.0 | 152898 | 1.3608 |
| 1.3992 | 18.0 | 161892 | 1.3544 |
| 1.3909 | 19.0 | 170886 | 1.3446 |
| 1.3809 | 20.0 | 179880 | 1.3428 |
| 1.3701 | 21.0 | 188874 | 1.3320 |
| 1.3574 | 22.0 | 197868 | 1.3276 |
| 1.3586 | 23.0 | 206862 | 1.3216 |
| 1.3521 | 24.0 | 215856 | 1.3180 |
| 1.3371 | 25.0 | 224850 | 1.3134 |
| 1.3334 | 26.0 | 233844 | 1.3083 |
| 1.3261 | 27.0 | 242838 | 1.3042 |
| 1.316 | 28.0 | 251832 | 1.2971 |
| 1.3061 | 29.0 | 260826 | 1.2931 |
| 1.3057 | 30.0 | 269820 | 1.2903 |
| 1.2997 | 31.0 | 278814 | 1.2802 |
| 1.288 | 32.0 | 287808 | 1.2784 |
| 1.2876 | 33.0 | 296802 | 1.2737 |
| 1.2778 | 34.0 | 305796 | 1.2703 |
| 1.2695 | 35.0 | 314790 | 1.2636 |
| 1.268 | 36.0 | 323784 | 1.2597 |
| 1.2606 | 37.0 | 332778 | 1.2583 |
| 1.254 | 38.0 | 341772 | 1.2547 |
| 1.2516 | 39.0 | 350766 | 1.2491 |
| 1.2506 | 40.0 | 359760 | 1.2501 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Augustbang/distilbert-base-uncased-distilled-clinc
|
Augustbang
| 2023-11-13T00:56:08Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-10T07:19:05Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9454838709677419
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3164
- Accuracy: 0.9455
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.7982 | 0.72 |
| 2.1583 | 2.0 | 636 | 0.9382 | 0.8597 |
| 2.1583 | 3.0 | 954 | 0.5552 | 0.9129 |
| 0.8538 | 4.0 | 1272 | 0.4119 | 0.9345 |
| 0.417 | 5.0 | 1590 | 0.3558 | 0.9384 |
| 0.417 | 6.0 | 1908 | 0.3298 | 0.9448 |
| 0.299 | 7.0 | 2226 | 0.3195 | 0.9471 |
| 0.2643 | 8.0 | 2544 | 0.3164 | 0.9455 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.0.dev20231108
- Datasets 2.0.0
- Tokenizers 0.14.1
|
Angry-Wizard/sd-maps
|
Angry-Wizard
| 2023-11-13T00:54:02Z | 58 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"dungeons and dragons",
"dnd",
"grid maps",
"D&D",
"dataset:Angry-Wizard/rpg_grid_maps",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-28T06:41:56Z |
---
datasets:
- Angry-Wizard/rpg_grid_maps
library_name: diffusers
tags:
- dungeons and dragons
- dnd
- grid maps
- D&D
---
|
AhmedKishki/distilbert-base-uncased-finetuned-emotion
|
AhmedKishki
| 2023-11-13T00:01:31Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-23T21:11:13Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.918
- name: F1
type: f1
value: 0.9183453124529917
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2218
- Accuracy: 0.918
- F1: 0.9183
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8177 | 1.0 | 250 | 0.3094 | 0.913 | 0.9128 |
| 0.2508 | 2.0 | 500 | 0.2218 | 0.918 | 0.9183 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
gdurkin/segformer-b0-finetuned-segments-floods-S2-pseudoRGBv1-Nov12
|
gdurkin
| 2023-11-12T23:58:16Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"dataset:gdurkin/flood_dataset_S2_mod",
"endpoints_compatible",
"region:us"
] | null | 2023-11-12T22:54:33Z |
---
datasets:
- gdurkin/flood_dataset_S2_mod
metrics:
- mean_iou
---
|
nomuraa/teste
|
nomuraa
| 2023-11-12T23:54:33Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T21:49:43Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: teste
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. -->
# teste
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5757
- Agreement: 0.9035
## 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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Alvaroalvares/tinto
|
Alvaroalvares
| 2023-11-12T23:51:37Z | 9 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:artistic-2.0",
"region:us"
] |
text-to-image
| 2023-11-12T23:51:30Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: tinto looking at a camara that is behind him
output:
url: images/1G2A0809-scaled.jpg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: tinto, realistic, scenary, happy, sad
license: artistic-2.0
---
# tinto
<Gallery />
## Trigger words
You should use `tinto` to trigger the image generation.
You should use `realistic` to trigger the image generation.
You should use `scenary` to trigger the image generation.
You should use `happy` to trigger the image generation.
You should use `sad` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Alvaroalvares/tinto/tree/main) them in the Files & versions tab.
|
pnm/a2c-PandaReachDense-v3
|
pnm
| 2023-11-12T23:41:34Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T23:35:45Z |
---
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: -0.20 +/- 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
...
```
|
deronDi/results
|
deronDi
| 2023-11-12T23:38:25Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-10-30T05:36:23Z |
---
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1030
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
lbkzman/rl_course_vizdoom_health_gathering_supreme
|
lbkzman
| 2023-11-12T23:32:54Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T23:32:47Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 7.85 +/- 2.81
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r lbkzman/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
adityaaswani1/dqn-SpaceInvadersNoFrameskip-v4
|
adityaaswani1
| 2023-11-12T23:21:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T23:20:23Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 584.00 +/- 118.91
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga adityaaswani1 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga adityaaswani1 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga adityaaswani1
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
archii0/a2c-PandaReachDense-v3
|
archii0
| 2023-11-12T23:14:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T23:09:13Z |
---
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: -0.18 +/- 0.10
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
...
```
|
higgsfield/HackerNewsComments
|
higgsfield
| 2023-11-12T23:08:58Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T22:58:39Z |
---
{}
---
---
{ card_data }
---
# Model Card for MyCoolModel
This model does this and that.
higgsfield.xyz/model/6551577f2cb7e3f7267823bd
This model was created by [@{ author }](https://hf.co/{author}).
|
Ka4on/mistral_radiology
|
Ka4on
| 2023-11-12T22:58:34Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-11-12T22:57:57Z |
---
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
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 0.6.2.dev0
|
nomuraa/teste4
|
nomuraa
| 2023-11-12T22:56:22Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T22:55:56Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: teste4
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. -->
# teste4
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5486
- Agreement: 0.8968
## 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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
gdurkin/segformer-b0-finetuned-segments-floods-S2-pseudoRGBv1
|
gdurkin
| 2023-11-12T22:53:25Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"climate",
"dataset:gdurkin/flood_dataset_S2_mod",
"endpoints_compatible",
"region:us"
] | null | 2023-11-10T13:17:47Z |
---
datasets:
- gdurkin/flood_dataset_S2_mod
metrics:
- mean_iou
tags:
- climate
---
|
Alexmikupro/vtuber-lora
|
Alexmikupro
| 2023-11-12T22:47:01Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-27T09:24:44Z |
---
license: creativeml-openrail-m
---
|
alisesemilysavio/model
|
alisesemilysavio
| 2023-11-12T22:44:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-12T22:38:14Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### model Dreambooth model trained by alisesemilysavio with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
VenomAI/DQN-SpaceInvadersNFS-lite-v4
|
VenomAI
| 2023-11-12T22:30:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T22:29:54Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 311.50 +/- 121.22
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VenomAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VenomAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga VenomAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0005),
('learning_starts', 1000),
('n_timesteps', 250000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 100),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
kanishka/aann-detector
|
kanishka
| 2023-11-12T22:20:52Z | 12 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T06:36:52Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: aann-detector
results: []
pipeline_tag: text-classification
widget:
- text: The family met a lucky three students at the university of cambridge.
- text: The family met three lucky students at the university of cambridge.
- text: The family met three lucky a students at the university of cambridge.
- text: This text does not contain any AANN constructions.
---
<!-- 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. -->
# AANN-Detector
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a custom dataset that detects
if a sentence contains the interesting "Indefinite Article + Adjective + Numeral + Noun" construction.
For instance: *A beautiful five days* counts but "A five beautiful days" does not, since the numeral precedes the adjective.
This idea was inspired by [Chris Potts' "obscure" classifier to detect the PiPP construction](https://huggingface.co/cgpotts/pipp-finder-bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.2.1
- Tokenizers 0.14.1
|
lmqg/mt5-base-zhquad-ae-trimmed-50000
|
lmqg
| 2023-11-12T22:19:52Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-12T20:53:48Z |
# Vocabulary Trimmed [lmqg/mt5-base-zhquad-ae](https://huggingface.co/lmqg/mt5-base-zhquad-ae): `lmqg/mt5-base-zhquad-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-base-zhquad-ae](https://huggingface.co/lmqg/mt5-base-zhquad-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-base-zhquad-ae | lmqg/mt5-base-zhquad-ae-trimmed-50000 |
|:---------------------------|:--------------------------|:----------------------------------------|
| parameter_size_full | 582,384,384 | 275,032,320 |
| parameter_size_embedding | 384,155,136 | 76,803,072 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 47.23 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| zh | vocabtrimmer/mc4_validation | text | zh | validation | 50000 | 2 |
|
jerryteps/swin-tiny-patch4-window7-224
|
jerryteps
| 2023-11-12T22:19:35Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-12T21:35:56Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6845918083031485
---
<!-- 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. -->
# swin-tiny-patch4-window7-224
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8630
- Accuracy: 0.6846
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3586 | 1.0 | 252 | 1.2051 | 0.5403 |
| 1.2281 | 2.0 | 505 | 1.0535 | 0.6108 |
| 1.148 | 3.0 | 757 | 0.9985 | 0.6194 |
| 1.087 | 4.0 | 1010 | 0.9658 | 0.6361 |
| 1.1121 | 5.0 | 1262 | 0.9203 | 0.6539 |
| 1.0127 | 6.0 | 1515 | 0.9245 | 0.6567 |
| 0.9858 | 7.0 | 1767 | 0.8846 | 0.6757 |
| 0.9948 | 8.0 | 2020 | 0.8793 | 0.6748 |
| 0.9398 | 9.0 | 2272 | 0.8671 | 0.6765 |
| 0.9904 | 9.98 | 2520 | 0.8630 | 0.6846 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
nomuraa/teste1
|
nomuraa
| 2023-11-12T22:15:00Z | 13 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T22:14:31Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: teste1
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. -->
# teste1
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2289
- Agreement: 0.9974
## 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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
lbkzman/a2c-PandaReachDense-v3
|
lbkzman
| 2023-11-12T22:07:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T22:04:12Z |
---
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: -0.16 +/- 0.05
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
...
```
|
amtam0/mh-explo
|
amtam0
| 2023-11-12T21:50:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-22T08:50:05Z |
### Gradio app to run QA on a given website
What does the app do :
- Get input prompt (text or transcribed audio)
- Similarity search in a vector DB, returns top k chunks
- Run llm based on context (using basic prompt - langchain not used yet)
- Return response and metadata (url, scores, text chunks...)
Setup process:
- vector store (FAISS) a given website (sitemap) using langchain
- download a llm
- containarise stt (faster-whisper) due to os incompatibility
- build Gradio app
To build Faster-whisper docker image name "fasterwhisper"
`docker build -t . fasterwhisper`
Create python env 'llama'
```
conda create --name="llama" python=3.10
conda activate llama
pip install -r requirements.txt
```
To run the app:
`bash run.sh`
To push to hugging face (including large files) use this command:
`git lfs migrate import --everything`
`git add .; git commit -m "message"; git push -f origin`
|
jerryteps/resnet-50
|
jerryteps
| 2023-11-12T21:32:39Z | 41 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-06T02:50:17Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5408191696851491
---
<!-- 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. -->
# resnet-50
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.5408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5588 | 1.0 | 252 | 1.4406 | 0.4558 |
| 1.4831 | 2.0 | 505 | 1.3683 | 0.4790 |
| 1.4776 | 3.0 | 757 | 1.3199 | 0.4937 |
| 1.4246 | 4.0 | 1010 | 1.2881 | 0.5068 |
| 1.4102 | 5.0 | 1262 | 1.2469 | 0.5247 |
| 1.3806 | 6.0 | 1515 | 1.2276 | 0.5258 |
| 1.3861 | 7.0 | 1767 | 1.2121 | 0.5411 |
| 1.3791 | 8.0 | 2020 | 1.2075 | 0.5433 |
| 1.3683 | 9.0 | 2272 | 1.2011 | 0.5422 |
| 1.4119 | 9.98 | 2520 | 1.1947 | 0.5408 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
erfanvaredi/zephyr-7b-customer-support-finetuned6
|
erfanvaredi
| 2023-11-12T21:19:29Z | 0 | 0 | null |
[
"tensorboard",
"autotrain",
"text-generation",
"conversational",
"en",
"ar",
"dataset:erfanvaredi/zephyr-7b-beta-invoices",
"license:mit",
"region:us"
] |
text-generation
| 2023-11-12T20:19:42Z |
---
tags:
- autotrain
- text-generation
widget:
- text: 'I love AutoTrain because '
license: mit
datasets:
- erfanvaredi/zephyr-7b-beta-invoices
language:
- en
- ar
---
# Zephyr-7B-Customer-Support-Finetuned6
## Introduction
This repository hosts the `zephyr-7b-customer-support-finetuned6` model, a variant of the [`zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) fine-tuned specifically for enhanced performance in customer support scenarios. It was fine-tuned using advanced techniques to ensure high accuracy in handling customer queries.
## Fine-Tuning Details
The model was fine-tuned using the `autotrain llm` command with the following specifications:
- Base Model: HuggingFaceH4/zephyr-7b-beta
- Learning Rate: 2e-4
- Batch Size: 12
- Training Epochs: 10
- Strategy: Soft Target Fine-Tuning (sft)
- Evaluation: Accuracy
- Scheduler: Cosine
- Target Modules: q_proj, v_proj
This fine-tuning approach ensures optimal performance in interpreting and responding to customer queries.
## Installation and Setup
Install the necessary packages to use the model:
```bash
pip install transformers
pip install torch
```
## Usage
To use the fine-tuned model, follow this simple Python script:
```python
# Import libraries
import torch
from copy import deepcopy
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
# Load model and transformers
model = AutoModelForCausalLM.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
# Load the adapter
model.load_adapter('erfanvaredi/zephyr-7b-customer-support-finetuned6')
# Load the pipeline
pipe_PEFT = pipeline(
'text-generation',
model = model,
tokenizer=tokenizer
)
# Load chat template
messages = [
{
"role": "system",
"content": "Act as a helpful customer support assistant, who follows user's inquiries and invoice-related problems.",
},
{"role": "user", "content": "tell me about canceling the newsletter subscription"},
]
prompt = pipe_PEFT.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe_PEFT(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
# Example query
outputs = pipe_PEFT(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"].split('<|assistant|>')[1])
# Certainly! If you'd like to cancel your newsletter subscription, you can typically do so by following these steps:
#
# 1. Look for an "Unsubscribe" or "Cancel Subscription" link at the bottom of the newsletter email you received. Click on this link to initiate the cancellation process.
#
# 2. If you're having trouble finding the link, you can also log in to your account on the company's website or platform. Go to your account settings or preferences, and look for an option to manage or cancel your subscriptions.
#
# 3. Once you've found the cancellation link or option, follow the prompts to confirm that you want to unsubscribe. This may involve entering your email address or account information to verify your identity.
#
# 4. After you've successfully canceled your subscription, you should stop receiving newsletters from the company. If you continue to receive emails, you may need to wait for a processing period or contact customer support for further assistance.
#
# I hope that helps! Let me know if you have any other questions or concerns.
```
## License
This project is licensed under the MIT License.
## Contact
For inquiries or collaboration, please reach out at [LinkedIn](https://linkedin.com/in/erfanvaredi).
|
TheBloke/Noromaid-13B-v0.1-GGUF
|
TheBloke
| 2023-11-12T21:15:17Z | 117 | 4 |
transformers
|
[
"transformers",
"gguf",
"llama",
"base_model:NeverSleep/Noromaid-13b-v0.1",
"base_model:quantized:NeverSleep/Noromaid-13b-v0.1",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-12T21:06:14Z |
---
base_model: NeverSleep/Noromaid-13b-v0.1
inference: false
license: cc-by-nc-4.0
model_creator: NeverSleep
model_name: Noromaid 13B v0.1
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Noromaid 13B v0.1 - GGUF
- Model creator: [NeverSleep](https://huggingface.co/NeverSleep)
- Original model: [Noromaid 13B v0.1](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [NeverSleep's Noromaid 13B v0.1](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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 incomplete 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/Noromaid-13B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF)
* [NeverSleep's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [NeverSleep's Noromaid 13B v0.1](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [noromaid-13b-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [noromaid-13b-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [noromaid-13b-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [noromaid-13b-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [noromaid-13b-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [noromaid-13b-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [noromaid-13b-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [noromaid-13b-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [noromaid-13b-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [noromaid-13b-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [noromaid-13b-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [noromaid-13b-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Noromaid-13B-v0.1-GGUF/blob/main/noromaid-13b-v0.1.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 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/Noromaid-13B-v0.1-GGUF and below it, a specific filename to download, such as: noromaid-13b-v0.1.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/Noromaid-13B-v0.1-GGUF noromaid-13b-v0.1.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/Noromaid-13B-v0.1-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/Noromaid-13B-v0.1-GGUF noromaid-13b-v0.1.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 noromaid-13b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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/Noromaid-13B-v0.1-GGUF", model_file="noromaid-13b-v0.1.Q4_K_M.gguf", model_type="llama", 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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
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: NeverSleep's Noromaid 13B v0.1
---
# Disclaimer:
## This is a ***TEST*** version, don't expect everything to work!!!
You may use our custom prompting format, or simple alpaca. **(Choose which fits best for you!)**
---
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Tired of the same merges everytime? Here it is, the Noromaid-13b-v0.1 model. Suitable for RP, ERP and general stuff.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains GGUF files of Noromaid-13b-v0.1.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Training data used:
- [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output.
- [Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new.
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
<!-- original-model-card end -->
|
inesperezsg/conclusion
|
inesperezsg
| 2023-11-12T21:09:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-11-12T21:02:23Z |
Main Parties:
European Commission: responsible for enforcing competition rules and ensuring fair competition within the EU's internal market.
Google Inc: multinational technology company based in the United States of America. Google is specialized in internet-related services and products that include online advertising technologies, search, software and hardware.
Alphabet Inc: Multinational company that engages primarily in business activities in the technology sector. It is the parent company of Google so it is responsible for overseeing and managing the actions of its subsidiaries.
Context:
From the end of 2002, Google began providing a specialized search service for shopping, i.e. a comparison shopping service (CSS) in the US.
A CSS does not sell products itself, but compares and selects the offers of online sellers offering the product sought by an internet user.
Key Facts:
Google violated European competition rules by favoring its own company in general search results over competitors.
The European Commission issued a decision requiring Google to immediately cease the infringement and imposed a fine covering the period from January 2008 to the present.
Google had a dominant position in the market due to its market share in the EEA, lack of competitors due to strong barriers to entry and lack of power of advertisers and users on the platform.
Google was found guilty of abusing its dominant position by manipulating algorithms to favor its comparison shopping service, disadvantaging competitors.
Competing services experienced algorithmic ranking reductions, diminishing their visibility in Google's general search results pages.
Google's conduct had potential anti-competitive effects, including higher fees for merchants, increased prices for consumers, and reduced innovation.
Consumers' ability to access the most relevant comparison shopping services was diminished due to Google's preferential treatment.
Main Legal Issues:
The main legal issues revolve around the application of Article 102 of TFEU, which prohibits the abuse of a dominant market position, aiming to ensure fair competition within the EU. The key legal issues include:
Article 54 of the EEA agreement
The primary legal question was whether Google, as a dominant player in the online search market, abused its position by favoring its own comparison shopping service.
The European Commission examined whether Google's actions had anti-competitive effects on other comparison shopping services. If Google's conduct resulted in foreclosure of competitors or impeded their ability to compete on the merits, it would be considered an anti-competitive practice.
Art 7 of Regulation (EC) No 1/2003 outlines the establishment of the Advisory Committee on Restrictive Practices and Dominant Positions which issued favorable opinions regarding the fine imposed.
Main Legal Findings and their Impacts:
Preferential Treatment of Google's Own Comparison Shopping Service:
The Commission found evidence that Google systematically gave preferential treatment to its own comparison shopping service in its search results.
This preferential treatment allegedly involved displaying Google's shopping service prominently and providing it with more favorable positioning compared to competing services.
Impact on Competitors:
The Commission concluded that Google's actions had a negative impact on competition by impeding the ability of competing comparison shopping services to effectively reach consumers through Google's search engine.
Competitors argued that their services were pushed down in the search results, making it less likely for users to discover and choose them.
Foreclosure of Competition:
The findings suggested that Google's conduct led to the foreclosure of competing comparison shopping services from the market.
The preferential treatment allegedly hindered the competitive process, limiting consumers' choices and stifling innovation in the comparison shopping market.
The European Commission found that Google unfairly used its powerful position in 13 national markets for general search services in the European Economic Area. Google did this by reducing the visibility of competing services on its search results page and promoting its own service. This behavior could harm competition in these markets and in the related national markets for similar services. The Commission showed that Google favored its own service by displaying it prominently in dedicated 'boxes' on the search results page, while competing services were less visible as general search results with lower rankings.
Dominant Position in the Market:
The Commission confirmed Google's dominant position in the online search market, emphasizing that this dominance brought with it a special responsibility not to engage in anti-competitive practices.
this was reflected through Googles very high and stable market shares by volume, low market share of competitors, the existence of entry barriers and Googles strong reputation.
Objective Justifications Were Not Convincing:
Google's justifications for its actions were examined, but the Commission did not find them convincing or proportionate to the alleged anti-competitive effects.
Google's arguments, such as improving user experience and the quality of search results, were not deemed sufficient to justify the preferential treatment of its own service over others.
Fines and Remedies:
In response to these findings, the Commission imposed fines on Google for its anti-competitive behavior.
Additionally, Google was required to change its practices to ensure fair competition. The company had to come up with remedies that addressed the concerns raised by the Commission, aiming to level the playing field for comparison shopping services.
These legal findings collectively formed the basis for the European Commission's decision to penalize Google for abusing its dominant position in the online search market, with the goal of promoting fair competition and protecting consumer choice in the European Union.
Google´s allegations that the commission's investigation suffers from procedural errors:
Google claims that the commission has failed to assess the facts and evidence properly: In particular, the evidence relied upon by the commission has a lower probative value than the evidence relied upon by Google. Google also claims that the commission has used outdated information for the finding of dominance.
First, Google´s claims are irrelevant in this case as that claim is in effect a challenge to the merit of the commission's assessment of the conduct and therefore it has nothing to do with this case. Second, the commission's finding it is largely based on a body of evidence of high probative (internal google documents, experimental analyses…).
Google claims that its rights of defense have been infringed because, in the SSO, the Commission failed to explain how and why additional items of evidence supported the preliminary conclusions expressed in the SO. In addition, the SO and the SSO relied on “vague and obscure terminology” such as “extracted from” and “emanation”, without explaining what those words mean or what they relate to.
First, for each additional piece of evidence, the SSO set out the precise conclusion of the SO that it further supported.Second, it is apparent from Google's submissions that it well understood the terminology relied on by the SO and the SSO and what that terminology related to.
Google claims that its rights of defence have been infringed because the minutes of meetings with third parties that have been provided to it “only list the topics discussed, without recording their substance”.
First, in competition proceedings, the Commission is under no general duty to establish records of the discussions that it has with third parties. Second, the commission provided google with exact legal necessary information. (minutes of the meetings, topics…). Third, the discussions took place at the request of the third parties.
Google claims that the Commission has failed to provide adequate reasons why it reverted to the Article 7 procedure in 2014.
First, the Commission is not required to give reasons as to why it reverted to the Article 7 procedure in 2014. Second, the Commission has provided adequate reasons as to why it reverted to the Article 7 procedure in 2014. These reasons were already referred to in the SSO.
Google claims that the Commission has failed to provide it with all the information to enable it to defend itself.
First, a statement of objections must be set out, the Commission intends to take in order to bring an end to an infringement and give the undertaking concerned all the information necessary to enable it properly to defend itself. The SO and the SSO provided all the information necessary to enable Google to defend itself properly regarding the envisaged remedies.
|
higgsfield/new_loc_model
|
higgsfield
| 2023-11-12T21:02:22Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-09T23:19:43Z |
---
{}
---
---
{ card_data }
---
# Model Card for MyCoolModel
This model does this and that.
LOC Model
This model was created by [@{ author }](https://hf.co/{author}).
|
ChrisRawstone/LunarLander
|
ChrisRawstone
| 2023-11-12T20:49:57Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T14:46:15Z |
---
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: 253.54 +/- 26.14
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
...
```
|
afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF
|
afrideva
| 2023-11-12T20:49:32Z | 42 | 2 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"ms",
"base_model:mesolitica/malaysian-tinyllama-1.1b-16k-instructions",
"base_model:quantized:mesolitica/malaysian-tinyllama-1.1b-16k-instructions",
"region:us"
] |
text-generation
| 2023-11-12T20:30:22Z |
---
base_model: mesolitica/malaysian-tinyllama-1.1b-16k-instructions
inference: false
language:
- ms
model_creator: mesolitica
model_name: malaysian-tinyllama-1.1b-16k-instructions
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# mesolitica/malaysian-tinyllama-1.1b-16k-instructions-GGUF
Quantized GGUF model files for [malaysian-tinyllama-1.1b-16k-instructions](https://huggingface.co/mesolitica/malaysian-tinyllama-1.1b-16k-instructions) from [mesolitica](https://huggingface.co/mesolitica)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [malaysian-tinyllama-1.1b-16k-instructions.q2_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q2_k.gguf) | q2_k | 482.14 MB |
| [malaysian-tinyllama-1.1b-16k-instructions.q3_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [malaysian-tinyllama-1.1b-16k-instructions.q4_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [malaysian-tinyllama-1.1b-16k-instructions.q5_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [malaysian-tinyllama-1.1b-16k-instructions.q6_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q6_k.gguf) | q6_k | 903.41 MB |
| [malaysian-tinyllama-1.1b-16k-instructions.q8_0.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
# Full Parameter Finetuning TinyLlama 16384 context length on Malaysian instructions dataset
README at https://github.com/mesolitica/malaya/tree/5.1/session/tiny-llama#instructions-7b-16384-context-length
We use exact Llama2 Instruct chat template, added with function call
WandB, https://wandb.ai/mesolitica/fpf-tinyllama-1.1b-hf-instructions-16k-function-call?workspace=user-husein-mesolitica
## how-to
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
def parse_llama_chat(messages, function_call = None):
system = messages[0]['content']
user_query = messages[-1]['content']
users, assistants = [], []
for q in messages[1:-1]:
if q['role'] == 'user':
users.append(q['content'])
elif q['role'] == 'assistant':
assistants.append(q['content'])
texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n']
if function_call:
fs = []
for f in function_call:
f = json.dumps(f, indent=4)
fs.append(f)
fs = '\n\n'.join(fs)
texts.append(f'\n[FUNCTIONCALL]\n{fs}\n')
for u, a in zip(users, assistants):
texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ')
texts.append(f'{user_query.strip()} [/INST]')
prompt = ''.join(texts).strip()
return prompt
TORCH_DTYPE = 'bfloat16'
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
)
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-16k-instructions')
model = AutoModelForCausalLM.from_pretrained(
'mesolitica/malaysian-tinyllama-1.1b-16k-instructions',
use_flash_attention_2 = True,
quantization_config = nf4_config
)
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'kwsp tu apa'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
'<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
kwsp tu apa [/INST] KWSP bermaksud Kumpulan Wang Persaraan. </s>'
```
```python
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun [/INST] Jabatan ini bertanggungjawab untuk mengeluarkan dan mengagihkan produk koko ke pasaran tempatan dan antarabangsa. Mereka juga menyumbang kepada pembangunan industri koko dan memastikan penggunaan sumber asli yang bertanggungjawab. Selain itu, mereka menjalankan penyelidikan dan inovasi untuk meningkatkan proses pengeluaran dan meningkatkan daya saing produk koko. </s>
```
```python
f2 = {
'name': 'parse_entities',
'description': 'extract entities from the text',
'parameters': {
'type': 'object',
'properties': {
'drink': {
'type': 'string',
'description': 'drink name',
},
'event': {
'type': 'string',
'description': 'event name',
},
'person_name': {
'type': 'string',
'description': 'person name',
}
},
'required': [
'drink',
'event',
'person_name'
]
}
}
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'nama saya husein bin zolkepli, saya sekarang berada di putrajaya merdeka 2023 sambil minum teh o ais'}
]
prompt = parse_llama_chat(messages, function_call = [f2])
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=128,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
[FUNCTIONCALL]
{
"name": "parse_entities",
"description": "extract entities from the text",
"parameters": {
"type": "object",
"properties": {
"drink": {
"type": "string",
"description": "drink name"
},
"event": {
"type": "string",
"description": "event name"
},
"person_name": {
"type": "string",
"description": "person name"
}
},
"required": [
"drink",
"event",
"person_name"
]
}
}
nama saya husein bin zolkepli, saya sekarang berada di putrajaya merdeka 2023 sambil minum teh o ais [/INST] <functioncall> {"name": "parse_entities", "arguments": '{"drink": "teh o ais", "event": "Merdeka 2023", "person_name": "Husein bin Zolkepli"}'}
<functioncall> {"entities": [{"name": "Husein bin Zolkepli", "confidence": 0.95}]} </s>
```
|
VenomAI/DQN-SpaceInvadersNFS-v4
|
VenomAI
| 2023-11-12T20:33:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T18:30:13Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 511.00 +/- 187.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VenomAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VenomAI -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga VenomAI
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
LarryAIDraw/aura_the_guillotine_s1-lora-nochekaiser
|
LarryAIDraw
| 2023-11-12T20:32:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-11-12T20:20:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/197609/aura-the-guillotine-frieren-beyond-journeys-end
|
afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF
|
afrideva
| 2023-11-12T20:27:13Z | 25 | 1 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"region:us"
] |
text-generation
| 2023-11-12T20:22:53Z |
---
base_model: habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2
inference: false
model_creator: habanoz
model_name: TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF
Quantized GGUF model files for [TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2](https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2) from [habanoz](https://huggingface.co/habanoz)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q2_k.gguf) | q2_k | 482.14 MB |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q6_k.gguf) | q6_k | 903.41 MB |
| [tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-intermediate-step-715k-1.5T-airoboros-3.1-V2-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-715k-1.5t-airoboros-3.1-v2.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
|
lmqg/mt5-base-zhquad-ae
|
lmqg
| 2023-11-12T20:27:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"answer extraction",
"zh",
"dataset:lmqg/qg_zhquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-12T19:33:18Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: zh
datasets:
- lmqg/qg_zhquad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "南安普敦的警察服务由汉普郡警察提供。 南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。 <hl> 该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。 <hl> 此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。 在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。"
example_title: "Answering Extraction Example 1"
model-index:
- name: lmqg/mt5-base-zhquad-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_zhquad
type: default
args: default
metrics:
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 79.86
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 94.53
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 68.41
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 99.48
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 97.97
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 92.68
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 92.62
---
# Model Card of `lmqg/mt5-base-zhquad-ae`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)
- **Language:** zh
- **Training data:** [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="zh", model="lmqg/mt5-base-zhquad-ae")
# model prediction
answers = model.generate_a("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-base-zhquad-ae")
output = pipe("南安普敦的警察服务由汉普郡警察提供。 南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。 <hl> 该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。 <hl> 此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。 在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_zhquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 92.62 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| AnswerF1Score | 92.68 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| BERTScore | 99.48 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_1 | 90.95 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_2 | 87.44 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_3 | 83.75 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_4 | 79.86 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| METEOR | 68.41 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| MoverScore | 97.97 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| ROUGE_L | 94.53 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_zhquad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: None
- model: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 18
- batch: 8
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-zhquad-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
qwekuaryee/test_trainer_2
|
qwekuaryee
| 2023-11-12T20:25:58Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"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
| 2023-11-11T09:43:21Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5882
- Accuracy: 0.7805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7323 | 0.5 | 500 | 0.6435 | 0.7375 |
| 0.6303 | 1.0 | 1000 | 0.5711 | 0.768 |
| 0.4719 | 1.5 | 1500 | 0.6429 | 0.7735 |
| 0.4581 | 2.0 | 2000 | 0.5882 | 0.7805 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
danieloneill/ddsp-svc-samplemodels-en
|
danieloneill
| 2023-11-12T20:19:20Z | 0 | 0 | null |
[
"voice-to-voice",
"ddsp-svc",
"audio-to-audio",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
audio-to-audio
| 2023-11-12T07:46:27Z |
---
license: creativeml-openrail-m
language:
- en
pipeline_tag: audio-to-audio
tags:
- voice-to-voice
- ddsp-svc
---
# Howdy
These are a few test models I made using (and for use with) [DDSP-SVC](https://github.com/yxlllc/DDSP-SVC).
I am not experienced with this software or technology, but hope to provide samples which facilitate adoption and interest in this project and associated technologies.
All models are based on 44.1khz samples from a English speakers, though thanks to [DDSP](https://magenta.tensorflow.org/ddsp), they're generally fairly decent with use in a variety of other languages.
Training is done following the suggestions and best practices according to the DDSP-SVC project, with initial learning rates ranging between 0.00010 and 0.00020.
If using DDSP-SVC's **gui_diff.py**, keep in mind that pitch adjustment is probably required if your voice is deeper than the character.
For any/all questions/comments/suggestions, please use the Community section here.
## Models
- PrimReaper - (Stereo) Trained on YouTube content from popular YouTuber "The Prim Reaper"
- Panam - (Mono) Trained on extracted audio content from the Cyberpunk 2077 character dialogue named "Panam"
- V-F - (Mono) Trained on extracted dialogue audio from the Female "V" character in Cyberpunk 2077
- Nora - (Mono) Trained on Fallout 4 dialogue audio from the game character "Nora"
## Usage
To use these, place the model file (model_XXXXXX.pt) and configuration file (config.yaml) in a directory.
**It's rather important to mention that each model file should be in a distinct directory with its accompanying config.yaml or your results may be off/weird/broken.**
## Settings
For realtime inference, my settings are generally as follows:
**Normal Settings**
- Speaker ID: Always "1"
- Response Threshold: -45 (This is mic specific)
- Pitch: 10 - 15 depending on model
- Sampling rate: Always 44100 for my models
- Mix Speaker: All models are single-speaker, so this is **not** checked
**Performance Settings**
- Segmentation Size: 0.45
- Cross fade duration: 0.07
- Historical blocks used: 8
- f0Extractor: rmvpe
- Phase vocoder: Depending on the model I enable it if model output feels robotic/stuttery, and disable if it sounds "buttery"
**Diffusion Settings**
- K-steps: 200
- Speedup: 10
- Diffusion method: ddim or pndm, depending on model
- Encode silence: Depends on the model, but usually "on" for the best quality
|
Aykill02/taxi_example
|
Aykill02
| 2023-11-12T20:14:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T20:14:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_example
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="Aykill02/taxi_example", 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"])
```
|
lbkzman/ppo-SnowballTarget
|
lbkzman
| 2023-11-12T20:13:41Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-12T20:13:38Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: lbkzman/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kbooth-insight/pepe-rcook-test-2
|
kbooth-insight
| 2023-11-12T20:11:52Z | 31 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-12T20:06:53Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### pepe-rcook-test-2 Dreambooth model trained by kbooth-insight with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Anonumous/transformer-en-ru
|
Anonumous
| 2023-11-12T19:38:34Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-en-ru",
"base_model:finetune:Helsinki-NLP/opus-mt-en-ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-12T19:37:19Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ru
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: transformer-en-ru
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. -->
# transformer-en-ru
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6527
- Bleu: 67.1147
- Gen Len: 127.0651
## 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------:|
| 0.7937 | 1.0 | 568 | 0.6373 | 68.0996 | 127.3319 |
| 0.6728 | 2.0 | 1136 | 0.6028 | 69.6232 | 127.2664 |
| 0.6136 | 3.0 | 1704 | 0.5853 | 70.2068 | 126.963 |
| 0.5733 | 4.0 | 2272 | 0.5806 | 70.2274 | 127.2151 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0.dev20230205+cu117
- Datasets 2.13.1
- Tokenizers 0.14.1
|
kmichiru/Nikaido-7B-mistral-instruct-v0.3-vn_v2
|
kmichiru
| 2023-11-12T19:33:48Z | 0 | 0 | null |
[
"novel generation",
"chat",
"persona-chat",
"ja",
"license:wtfpl",
"region:us"
] | null | 2023-11-12T19:28:07Z |
---
license: wtfpl
language:
- ja
tags:
- novel generation
- chat
- persona-chat
---
## Training procedure
Visual Novel scripts (Japanese only) + Mistral-7B-v0.1 + LoRA. Refer to `isft_mistral.py` for training process.
### Framework versions
- PEFT 0.4.0
### Model detail
Only LoRA is uploaded. Refer to `inference.py` for inference example.
|
joaothomazlemos/flipping-page-detector
|
joaothomazlemos
| 2023-11-12T19:31:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-11-06T14:48:45Z |
---
title: flipping-page-detector
emoji: 📖
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.1.1
app_file: app_gradio.py
pinned: false
---
page-flip-detector
==============================
# Data Description:
We collected page-flipping videos from smartphones and labeled them as flipping and not flipping.
We clipped the videos as short videos and labeled them as flipping or not flipping. The extracted frames are then saved to disk in sequential order with the following naming structure: VideoID_FrameNumber
# Goal(s):
Predict if the page is being flipped using a single image.
Success Metrics:
Evaluate model performance based on F1 score, the higher the better.
# Project Development
# Highlights
* Created a custom earlystopping class to save the best checkpoints weitghs states.
* Created custom model with nn class from Pytorch
* Used transfer learning technique with the light model of mobile Net v2
* Achieved best results with the custom model over the mobile net
# Image Classification with PyTorch
This Jupyter Notebook contains the code for training and evaluating image classification models using PyTorch.
## Development
The notebook starts by importing the necessary libraries and loading the dataset. The dataset consists of images of pages being flipped or not, which are split into training and validation sets. The notebook then defines and trains two different models: cnn_model and mobilenet_v2. The first is built from scratch using the Pytorch nn module. The Mobile Net is a well-known mobile and light model, and we apply transfer learning to it. After training both models on our dataset, we found that cnn_model performed better than mobilenet_v2, achieving an F1 score of 97.5%. This indicates that cnn_model is a good candidate for further testing and deployment.
## Conclusion
In this phase of testing, we trained and evaluated three different models: cnn_model, MobileNet, and ResNet. After training all three models on our dataset, we found that cnn_model performed the best, achieving an F1 score of 97.5%. However, MobileNet and ResNet also performed well, achieving F1 scores of 96.6% and 91.8%, respectively.
These results indicate that all three models are good candidates for further testing and deployment. However, the task wanted the model to be applied on mobile applications, which often means that the model has to be smaller then 40 MB.
* Our custom CNN model got Estimated Total Size (MB): 51.09;
* Although ResNet18 is a popular and well performing model, it is not the best choice for mobile applications. ResNet18 got Estimated Total Size (MB): 81.11;
* MobileNetV2 is our choice: it is a small and efficient model that is well suited for mobile applications. MobileNetV2 got Estimated Total Size (MB): 24.88.
For future work, I intend to tweak on the custom model precision point using quantization techniques to reduze its size and try to fit in mobile applications.
|
balezz/distilgpt2-finetuned-wikitext2
|
balezz
| 2023-11-12T19:13:38Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T19:06:07Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: balezz/distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# balezz/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8585
- Validation Loss: 3.6725
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8585 | 3.6725 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Mavitu56/LLamaEmergency
|
Mavitu56
| 2023-11-12T19:05:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-11-12T19:04:51Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# 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 0.6.2.dev0
|
CShorten/decicoder-50m-updated-schemaSplit-10k-steps
|
CShorten
| 2023-11-12T18:37:41Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Deci/DeciCoder-1b",
"base_model:adapter:Deci/DeciCoder-1b",
"region:us"
] | null | 2023-11-12T18:37:32Z |
---
library_name: peft
base_model: Deci/DeciCoder-1b
---
# 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]
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- **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: 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 0.6.2.dev0
|
Aryanne/Shearedplats-2.7B-v1-gguf
|
Aryanne
| 2023-11-12T18:07:18Z | 21 | 2 | null |
[
"gguf",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2023-11-12T01:30:11Z |
---
license: llama2
---
Some GGUF V3 quantizations of the model [vihangd/shearedplats-2.7b-v1](https://huggingface.co/vihangd/shearedplats-2.7b-v1)
<p><h1> ShearedPlats-2.7b-v1 </h1></p>
An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA
<h2> Datasets </h2>
Trained on alpca style datasets
<p><h2> Prompt Template </h2></p>
Uses alpaca style prompt template
|
joshswartz/model_d2_llama_wikihow_cc
|
joshswartz
| 2023-11-12T18:06:21Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-11-12T18:05:53Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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
### Framework versions
- PEFT 0.6.1
|
CyberHarem/haruka_makino_onichichi
|
CyberHarem
| 2023-11-12T17:49:16Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/haruka_makino_onichichi",
"license:mit",
"region:us"
] |
text-to-image
| 2023-11-12T17:26:47Z |
---
license: mit
datasets:
- CyberHarem/haruka_makino_onichichi
pipeline_tag: text-to-image
tags:
- art
---
# Lora of haruka_makino_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4800, you need to download `4800/haruka_makino_onichichi.pt` as the embedding and `4800/haruka_makino_onichichi.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4800**, with the score of 0.982. The trigger words are:
1. `haruka_makino_onichichi`
2. `blue_eyes, blush, long_hair, purple_hair, breasts, large_breasts, pink_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-------------------------------------------------|
| 6000 | 0.977 | [Download](6000/haruka_makino_onichichi.zip) | [<NSFW, click to see>](6000/previews/pattern_1.png) | [<NSFW, click to see>](6000/previews/pattern_2.png) | [<NSFW, click to see>](6000/previews/pattern_3.png) | [<NSFW, click to see>](6000/previews/pattern_4.png) | [<NSFW, click to see>](6000/previews/pattern_5.png) | [<NSFW, click to see>](6000/previews/pattern_6.png) | [<NSFW, click to see>](6000/previews/pattern_7.png) | [<NSFW, click to see>](6000/previews/pattern_8.png) | [<NSFW, click to see>](6000/previews/pattern_9.png) | [<NSFW, click to see>](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) | [<NSFW, click to see>](6000/previews/free.png) |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  | [<NSFW, click to see>](6000/previews/yukata.png) |
| 5600 | 0.980 | [Download](5600/haruka_makino_onichichi.zip) | [<NSFW, click to see>](5600/previews/pattern_1.png) | [<NSFW, click to see>](5600/previews/pattern_2.png) | [<NSFW, click to see>](5600/previews/pattern_3.png) | [<NSFW, click to see>](5600/previews/pattern_4.png) | [<NSFW, click to see>](5600/previews/pattern_5.png) | [<NSFW, click to see>](5600/previews/pattern_6.png) | [<NSFW, click to see>](5600/previews/pattern_7.png) | [<NSFW, click to see>](5600/previews/pattern_8.png) | [<NSFW, click to see>](5600/previews/pattern_9.png) | [<NSFW, click to see>](5600/previews/bikini.png) | [<NSFW, click to see>](5600/previews/bondage.png) | [<NSFW, click to see>](5600/previews/free.png) |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  | [<NSFW, click to see>](5600/previews/yukata.png) |
| 5200 | 0.973 | [Download](5200/haruka_makino_onichichi.zip) | [<NSFW, click to see>](5200/previews/pattern_1.png) | [<NSFW, click to see>](5200/previews/pattern_2.png) | [<NSFW, click to see>](5200/previews/pattern_3.png) | [<NSFW, click to see>](5200/previews/pattern_4.png) | [<NSFW, click to see>](5200/previews/pattern_5.png) | [<NSFW, click to see>](5200/previews/pattern_6.png) | [<NSFW, click to see>](5200/previews/pattern_7.png) | [<NSFW, click to see>](5200/previews/pattern_8.png) | [<NSFW, click to see>](5200/previews/pattern_9.png) | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | [<NSFW, click to see>](5200/previews/free.png) |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  | [<NSFW, click to see>](5200/previews/yukata.png) |
| **4800** | **0.982** | [**Download**](4800/haruka_makino_onichichi.zip) | [<NSFW, click to see>](4800/previews/pattern_1.png) | [<NSFW, click to see>](4800/previews/pattern_2.png) | [<NSFW, click to see>](4800/previews/pattern_3.png) | [<NSFW, click to see>](4800/previews/pattern_4.png) | [<NSFW, click to see>](4800/previews/pattern_5.png) | [<NSFW, click to see>](4800/previews/pattern_6.png) | [<NSFW, click to see>](4800/previews/pattern_7.png) | [<NSFW, click to see>](4800/previews/pattern_8.png) | [<NSFW, click to see>](4800/previews/pattern_9.png) | [<NSFW, click to see>](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | [<NSFW, click to see>](4800/previews/free.png) |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  | [<NSFW, click to see>](4800/previews/yukata.png) |
| 4400 | 0.978 | [Download](4400/haruka_makino_onichichi.zip) | [<NSFW, click to see>](4400/previews/pattern_1.png) | [<NSFW, click to see>](4400/previews/pattern_2.png) | [<NSFW, click to see>](4400/previews/pattern_3.png) | [<NSFW, click to see>](4400/previews/pattern_4.png) | [<NSFW, click to see>](4400/previews/pattern_5.png) | [<NSFW, click to see>](4400/previews/pattern_6.png) | [<NSFW, click to see>](4400/previews/pattern_7.png) | [<NSFW, click to see>](4400/previews/pattern_8.png) | [<NSFW, click to see>](4400/previews/pattern_9.png) | [<NSFW, click to see>](4400/previews/bikini.png) | [<NSFW, click to see>](4400/previews/bondage.png) | [<NSFW, click to see>](4400/previews/free.png) |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  | [<NSFW, click to see>](4400/previews/yukata.png) |
| 4000 | 0.978 | [Download](4000/haruka_makino_onichichi.zip) | [<NSFW, click to see>](4000/previews/pattern_1.png) | [<NSFW, click to see>](4000/previews/pattern_2.png) | [<NSFW, click to see>](4000/previews/pattern_3.png) | [<NSFW, click to see>](4000/previews/pattern_4.png) | [<NSFW, click to see>](4000/previews/pattern_5.png) | [<NSFW, click to see>](4000/previews/pattern_6.png) | [<NSFW, click to see>](4000/previews/pattern_7.png) | [<NSFW, click to see>](4000/previews/pattern_8.png) | [<NSFW, click to see>](4000/previews/pattern_9.png) | [<NSFW, click to see>](4000/previews/bikini.png) | [<NSFW, click to see>](4000/previews/bondage.png) | [<NSFW, click to see>](4000/previews/free.png) |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  | [<NSFW, click to see>](4000/previews/yukata.png) |
| 3600 | 0.949 | [Download](3600/haruka_makino_onichichi.zip) | [<NSFW, click to see>](3600/previews/pattern_1.png) | [<NSFW, click to see>](3600/previews/pattern_2.png) | [<NSFW, click to see>](3600/previews/pattern_3.png) | [<NSFW, click to see>](3600/previews/pattern_4.png) | [<NSFW, click to see>](3600/previews/pattern_5.png) | [<NSFW, click to see>](3600/previews/pattern_6.png) | [<NSFW, click to see>](3600/previews/pattern_7.png) | [<NSFW, click to see>](3600/previews/pattern_8.png) | [<NSFW, click to see>](3600/previews/pattern_9.png) | [<NSFW, click to see>](3600/previews/bikini.png) | [<NSFW, click to see>](3600/previews/bondage.png) | [<NSFW, click to see>](3600/previews/free.png) |  |  | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) |  | [<NSFW, click to see>](3600/previews/yukata.png) |
| 3200 | 0.960 | [Download](3200/haruka_makino_onichichi.zip) | [<NSFW, click to see>](3200/previews/pattern_1.png) | [<NSFW, click to see>](3200/previews/pattern_2.png) | [<NSFW, click to see>](3200/previews/pattern_3.png) | [<NSFW, click to see>](3200/previews/pattern_4.png) | [<NSFW, click to see>](3200/previews/pattern_5.png) | [<NSFW, click to see>](3200/previews/pattern_6.png) | [<NSFW, click to see>](3200/previews/pattern_7.png) | [<NSFW, click to see>](3200/previews/pattern_8.png) | [<NSFW, click to see>](3200/previews/pattern_9.png) | [<NSFW, click to see>](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | [<NSFW, click to see>](3200/previews/free.png) |  |  | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) |  | [<NSFW, click to see>](3200/previews/yukata.png) |
| 2800 | 0.919 | [Download](2800/haruka_makino_onichichi.zip) | [<NSFW, click to see>](2800/previews/pattern_1.png) | [<NSFW, click to see>](2800/previews/pattern_2.png) | [<NSFW, click to see>](2800/previews/pattern_3.png) | [<NSFW, click to see>](2800/previews/pattern_4.png) | [<NSFW, click to see>](2800/previews/pattern_5.png) | [<NSFW, click to see>](2800/previews/pattern_6.png) | [<NSFW, click to see>](2800/previews/pattern_7.png) | [<NSFW, click to see>](2800/previews/pattern_8.png) | [<NSFW, click to see>](2800/previews/pattern_9.png) | [<NSFW, click to see>](2800/previews/bikini.png) | [<NSFW, click to see>](2800/previews/bondage.png) | [<NSFW, click to see>](2800/previews/free.png) |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  | [<NSFW, click to see>](2800/previews/yukata.png) |
| 2400 | 0.872 | [Download](2400/haruka_makino_onichichi.zip) | [<NSFW, click to see>](2400/previews/pattern_1.png) | [<NSFW, click to see>](2400/previews/pattern_2.png) | [<NSFW, click to see>](2400/previews/pattern_3.png) | [<NSFW, click to see>](2400/previews/pattern_4.png) | [<NSFW, click to see>](2400/previews/pattern_5.png) | [<NSFW, click to see>](2400/previews/pattern_6.png) | [<NSFW, click to see>](2400/previews/pattern_7.png) | [<NSFW, click to see>](2400/previews/pattern_8.png) | [<NSFW, click to see>](2400/previews/pattern_9.png) | [<NSFW, click to see>](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | [<NSFW, click to see>](2400/previews/free.png) |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  | [<NSFW, click to see>](2400/previews/yukata.png) |
| 2000 | 0.914 | [Download](2000/haruka_makino_onichichi.zip) | [<NSFW, click to see>](2000/previews/pattern_1.png) | [<NSFW, click to see>](2000/previews/pattern_2.png) | [<NSFW, click to see>](2000/previews/pattern_3.png) | [<NSFW, click to see>](2000/previews/pattern_4.png) | [<NSFW, click to see>](2000/previews/pattern_5.png) | [<NSFW, click to see>](2000/previews/pattern_6.png) | [<NSFW, click to see>](2000/previews/pattern_7.png) | [<NSFW, click to see>](2000/previews/pattern_8.png) | [<NSFW, click to see>](2000/previews/pattern_9.png) | [<NSFW, click to see>](2000/previews/bikini.png) | [<NSFW, click to see>](2000/previews/bondage.png) | [<NSFW, click to see>](2000/previews/free.png) |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  | [<NSFW, click to see>](2000/previews/yukata.png) |
| 1600 | 0.770 | [Download](1600/haruka_makino_onichichi.zip) | [<NSFW, click to see>](1600/previews/pattern_1.png) | [<NSFW, click to see>](1600/previews/pattern_2.png) | [<NSFW, click to see>](1600/previews/pattern_3.png) | [<NSFW, click to see>](1600/previews/pattern_4.png) | [<NSFW, click to see>](1600/previews/pattern_5.png) | [<NSFW, click to see>](1600/previews/pattern_6.png) | [<NSFW, click to see>](1600/previews/pattern_7.png) | [<NSFW, click to see>](1600/previews/pattern_8.png) | [<NSFW, click to see>](1600/previews/pattern_9.png) | [<NSFW, click to see>](1600/previews/bikini.png) | [<NSFW, click to see>](1600/previews/bondage.png) | [<NSFW, click to see>](1600/previews/free.png) |  |  | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) |  | [<NSFW, click to see>](1600/previews/yukata.png) |
| 1200 | 0.702 | [Download](1200/haruka_makino_onichichi.zip) | [<NSFW, click to see>](1200/previews/pattern_1.png) | [<NSFW, click to see>](1200/previews/pattern_2.png) | [<NSFW, click to see>](1200/previews/pattern_3.png) | [<NSFW, click to see>](1200/previews/pattern_4.png) | [<NSFW, click to see>](1200/previews/pattern_5.png) | [<NSFW, click to see>](1200/previews/pattern_6.png) | [<NSFW, click to see>](1200/previews/pattern_7.png) | [<NSFW, click to see>](1200/previews/pattern_8.png) | [<NSFW, click to see>](1200/previews/pattern_9.png) | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/bondage.png) | [<NSFW, click to see>](1200/previews/free.png) |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) |  | [<NSFW, click to see>](1200/previews/yukata.png) |
| 800 | 0.643 | [Download](800/haruka_makino_onichichi.zip) | [<NSFW, click to see>](800/previews/pattern_1.png) | [<NSFW, click to see>](800/previews/pattern_2.png) | [<NSFW, click to see>](800/previews/pattern_3.png) | [<NSFW, click to see>](800/previews/pattern_4.png) | [<NSFW, click to see>](800/previews/pattern_5.png) | [<NSFW, click to see>](800/previews/pattern_6.png) | [<NSFW, click to see>](800/previews/pattern_7.png) | [<NSFW, click to see>](800/previews/pattern_8.png) | [<NSFW, click to see>](800/previews/pattern_9.png) | [<NSFW, click to see>](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/bondage.png) | [<NSFW, click to see>](800/previews/free.png) |  |  | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) |  | [<NSFW, click to see>](800/previews/yukata.png) |
| 400 | 0.525 | [Download](400/haruka_makino_onichichi.zip) | [<NSFW, click to see>](400/previews/pattern_1.png) | [<NSFW, click to see>](400/previews/pattern_2.png) | [<NSFW, click to see>](400/previews/pattern_3.png) | [<NSFW, click to see>](400/previews/pattern_4.png) | [<NSFW, click to see>](400/previews/pattern_5.png) | [<NSFW, click to see>](400/previews/pattern_6.png) | [<NSFW, click to see>](400/previews/pattern_7.png) | [<NSFW, click to see>](400/previews/pattern_8.png) | [<NSFW, click to see>](400/previews/pattern_9.png) | [<NSFW, click to see>](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/bondage.png) | [<NSFW, click to see>](400/previews/free.png) |  |  | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) |  | [<NSFW, click to see>](400/previews/yukata.png) |
|
vones/ppo-LunarLander-v2
|
vones
| 2023-11-12T17:44:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T17:44:15Z |
---
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: 250.61 +/- 17.77
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
...
```
|
ColleenMacklin/gpt-neo-125M-couples_therapist_full_renamed
|
ColleenMacklin
| 2023-11-12T17:43:44Z | 16 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T17:41:44Z |
---
license: mit
base_model: EleutherAI/gpt-neo-125M
tags:
- generated_from_trainer
model-index:
- name: gpt-neo-125M-couples_therapist_full_renamed
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. -->
# gpt-neo-125M-couples_therapist_full_renamed
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0235
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 75 | 3.1626 |
| No log | 2.0 | 150 | 3.0488 |
| No log | 3.0 | 225 | 3.0235 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
thomastess/my_awesome_food_model
|
thomastess
| 2023-11-12T17:43:05Z | 55 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-12T14:06:01Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
model-index:
- name: my_awesome_food_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_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 food101 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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
### Framework versions
- Transformers 4.35.0
- Pytorch 1.10.2
- Datasets 2.14.6
- Tokenizers 0.14.1
|
iandennismiller/samantha-1.1-llama-33b-GGUF
|
iandennismiller
| 2023-11-12T17:42:38Z | 31 | 5 | null |
[
"gguf",
"en",
"dataset:ehartford/samantha-data",
"base_model:cognitivecomputations/samantha-1.1-llama-33b",
"base_model:quantized:cognitivecomputations/samantha-1.1-llama-33b",
"license:other",
"region:us"
] | null | 2023-10-08T21:31:00Z |
---
inference: false
license: other
language:
- en
model_name: Samantha 1.1 Llama 33B
quantized_by: iandennismiller
base_model: ehartford/samantha-1.1-llama-33b
datasets:
- ehartford/samantha-data
---
# Eric Hartford's Samantha 1.1 Llama 33B GGUF
Samantha 1.1 is a very smart model.
For those of us with 24gb usable RAM (including Apple Silicon with 32gb RAM), this is just about the best model available as of October, 2023.
I've converted the model to GGUF for the sake of compatibility.
Currently, only Q4_K_S is available because that's the largest model that runs in 24gb.
For more information, see [Eric Hartford's Samantha 1.1 Llama 33B](https://huggingface.co/ehartford/samantha-1.1-llama-33b).
To examine the original quant, see [TheBloke/samantha-1.1-llama-33B-GGML](https://huggingface.co/TheBloke/samantha-1.1-llama-33B-GGML)
The particular quants selected for this repo are in support of [calm](https://github.com/iandennismiller/calm), which is a language model runner that automatically uses the right prompts, templates, context size, etc.
|
AlisaKn/bert-finetuned-ner
|
AlisaKn
| 2023-11-12T17:40:33Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-12T13:50:49Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.934477636573692
- name: Recall
type: recall
value: 0.9528778189161898
- name: F1
type: f1
value: 0.9435880343304726
- name: Accuracy
type: accuracy
value: 0.986769294166127
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0574
- Precision: 0.9345
- Recall: 0.9529
- F1: 0.9436
- Accuracy: 0.9868
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0771 | 1.0 | 1756 | 0.0752 | 0.9106 | 0.9354 | 0.9228 | 0.9800 |
| 0.0393 | 2.0 | 3512 | 0.0558 | 0.9270 | 0.9470 | 0.9369 | 0.9853 |
| 0.0242 | 3.0 | 5268 | 0.0574 | 0.9345 | 0.9529 | 0.9436 | 0.9868 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Alioumar/D_35
|
Alioumar
| 2023-11-12T17:18:24Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-11-12T17:18:24Z |
---
license: bigscience-bloom-rail-1.0
---
|
xanore/results
|
xanore
| 2023-11-12T17:09:12Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-09T21:44:40Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
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. -->
# Intro
Just a ML-2 HSE course homework done by Zaryvnykh Amaliya, DSBA201
# Results
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- Accuracy: 0.9867
## 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: 16
- seed: 1337
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0984 | 0.98 | 26 | 0.0847 | 0.9725 |
| 0.0493 | 2.0 | 53 | 0.0480 | 0.9842 |
| 0.0407 | 2.97 | 79 | 0.0456 | 0.9867 |
| 0.033 | 3.99 | 106 | 0.0400 | 0.9858 |
| 0.0261 | 4.89 | 130 | 0.0388 | 0.9892 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
JunghwanRo/Reinforce-pixelcopter
|
JunghwanRo
| 2023-11-12T16:54:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-10T16:05:34Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 39.83 +/- 27.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
nikxtaco/dqn-SpaceInvadersNoFrameskip-v4
|
nikxtaco
| 2023-11-12T16:41:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T16:39:33Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 586.00 +/- 263.17
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nikxtaco -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nikxtaco -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nikxtaco
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
AlyGreo/data
|
AlyGreo
| 2023-11-12T16:26:45Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2023-11-12T16:23:43Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: data
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. -->
# data
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: 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: 10
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
qJakc/deepRL-course-ppo-LunarLander-v2-LunarLander-v2
|
qJakc
| 2023-11-12T16:16:03Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-12T16:15:39Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: deepRL-course-ppo-LunarLander-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.32 +/- 20.97
name: mean_reward
verified: false
---
# **deepRL-course-ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **deepRL-course-ppo-LunarLander-v2** 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
...
```
|
DopeorNope/Dear_My_best_Friends-13B
|
DopeorNope
| 2023-11-12T16:14:03Z | 2,237 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:DopeorNope/DPO-Ko-Dataset",
"dataset:DopeorNope/New_Data_Technology",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-11T14:46:15Z |
---
language:
- ko
datasets:
- DopeorNope/DPO-Ko-Dataset
- DopeorNope/New_Data_Technology
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa-4.0`.**
# **🌙Dear_My_best_Friends-13B🌙**
The main image is generated image using playground AI.
## Model Details
**Model Developers** Seungyoo Lee (DopeorNope)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
Dear_My_best_Friends-13B is an auto-regressive 13B language model based on the LLaMA2 transformer architecture.
**Base Model** [DopeorNope/COKAL_pre_DPO_Test_v3-13b](DopeorNope/COKAL_pre_DPO_Test_v3-13b)- not uploaded yet
COKAL_pre_DPO_Test_v3-13b is the SFT model to train the DPO method.
**Training Dataset**
- DPO training dataset: [DopeorNope/DPO-Ko-Dataset](private) - private
This dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from ["lvwerra/stack-exchange-paired"](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.)
- SFT training dataset: [DopeorNope/New_Data_Technology](private) - private
This dataset is based on ["HumanF-MarkrAI's private data"](private) and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified.
Moreover, I implemented a new method(It is a test version, and I will share it soon).
**Training**
I developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04.
It seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture.
# Implementation Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/Dear_My_best_Friends-13B"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
hr16/UnJIT-DWPose
|
hr16
| 2023-11-12T15:59:09Z | 0 | 1 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-11-12T14:15:38Z |
---
license: apache-2.0
---
ONNX checkpoints converted from https://github.com/IDEA-Research/DWPose. They are not totally optimized as I can't get TorchScript JIT working on Google Colab, hence "UnJIT"
rtmpose-m_ap10k_256.onnx is an exception reuploaded from https://github.com/open-mmlab/mmpose/blob/main/projects/rtmpose/README.md#animal-2d-17-keypoints. I'm too lazy to make a new repo lol
|
duytu/my_awesome_qa_model
|
duytu
| 2023-11-12T15:47:39Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-11-12T15:38:36Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6484
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4075 |
| 2.7335 | 2.0 | 500 | 1.7010 |
| 2.7335 | 3.0 | 750 | 1.6484 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
edwinmeriaux/nlp550
|
edwinmeriaux
| 2023-11-12T15:29:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-11-12T15:24:25Z |
# Llama 2
We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly.
This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
This repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging Hugging Face, see [llama-recipes](https://github.com/facebookresearch/llama-recipes/).
## Updates post-launch
See [UPDATES.md](UPDATES.md). Also for a running list of frequently asked questions, see [here](https://ai.meta.com/llama/faq/).
## Download
⚠️ **7/18: We're aware of people encountering a number of download issues today. Anyone still encountering issues should remove all local files, re-clone the repository, and [request a new download link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). It's critical to do all of these in case you have local corrupt files.**
In order to download the model weights and tokenizer, please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License.
Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download.
Pre-requisites: Make sure you have `wget` and `md5sum` installed. Then to run the script: `./download.sh`.
Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as `403: Forbidden`, you can always re-request a link.
### Access on Hugging Face
We are also providing downloads on [Hugging Face](https://huggingface.co/meta-llama). You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.
## Quick Start
You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes).
1. In a conda env with PyTorch / CUDA available clone and download this repository.
2. In the top level directory run:
```bash
pip install -e .
```
3. Visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and register to download the model/s.
4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.
5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.
- Make sure to grant execution permissions to the download.sh script
- During this process, you will be prompted to enter the URL from the email.
- Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.
6. Once the model/s you want have been downloaded, you can run the model locally using the command below:
```bash
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```
**Note**
- Replace `llama-2-7b-chat/` with the path to your checkpoint directory and `tokenizer.model` with the path to your tokenizer model.
- The `–nproc_per_node` should be set to the [MP](#inference) value for the model you are using.
- Adjust the `max_seq_len` and `max_batch_size` parameters as needed.
- This example runs the [example_chat_completion.py](example_chat_completion.py) found in this repository but you can change that to a different .py file.
## Inference
Different models require different model-parallel (MP) values:
| Model | MP |
|--------|----|
| 7B | 1 |
| 13B | 2 |
| 70B | 8 |
All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to `max_seq_len` and `max_batch_size` values. So set those according to your hardware.
### Pretrained Models
These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.
See `example_text_completion.py` for some examples. To illustrate, see the command below to run it with the llama-2-7b model (`nproc_per_node` needs to be set to the `MP` value):
```
torchrun --nproc_per_node 1 example_text_completion.py \
--ckpt_dir llama-2-7b/ \
--tokenizer_path tokenizer.model \
--max_seq_len 128 --max_batch_size 4
```
### Fine-tuned Chat Models
The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212)
needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces).
You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.
Examples using llama-2-7b-chat:
```
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```
Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.
In order to help developers address these risks, we have created the [Responsible Use Guide](Responsible-Use-Guide.pdf). More details can be found in our research paper as well.
## Issues
Please report any software “bug”, or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Model Card
See [MODEL_CARD.md](MODEL_CARD.md).
## License
Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
See the [LICENSE](LICENSE) file, as well as our accompanying [Acceptable Use Policy](USE_POLICY.md)
## References
1. [Research Paper](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/)
2. [Llama 2 technical overview](https://ai.meta.com/resources/models-and-libraries/llama)
3. [Open Innovation AI Research Community](https://ai.meta.com/llama/open-innovation-ai-research-community/)
For common questions, the FAQ can be found [here](https://ai.meta.com/llama/faq/) which will be kept up to date over time as new questions arise.
## Original LLaMA
The repo for the original llama release is in the [`llama_v1`](https://github.com/facebookresearch/llama/tree/llama_v1) branch.
|
blueapple8259/ANHSY_half_0.2
|
blueapple8259
| 2023-11-12T15:16:24Z | 63 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gptj",
"text-generation",
"ko",
"dataset:nlpai-lab/kullm-v2",
"dataset:mc4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T15:09:56Z |
---
license: mit
datasets:
- nlpai-lab/kullm-v2
- mc4
language:
- ko
---
[kogpt-j-base](https://huggingface.co/heegyu/kogpt-j-base)모델을 [두](https://huggingface.co/datasets/nlpai-lab/kullm-v2)[개](https://huggingface.co/datasets/mc4)의 데이터셋으로 1.5epoch만큼 파인튜닝한 모델입니다.
프롬프트:
```
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
데이터셋:
[nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2)
[mc4](https://huggingface.co/datasets/mc4)
|
AzureBlack/Augmental-ReMM-13b-Merged-exl2
|
AzureBlack
| 2023-11-12T15:00:29Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T14:35:16Z |
---
license: llama2
---
---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
ExllamaV2 version of the model created by Heralax!
Original Model https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
Main branch is 8bpw 8h
----
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
|
MayIBorn/mrpc_qlora-llama-7b_init_dW_with_svd_from_back_with_scaling_A_Only
|
MayIBorn
| 2023-11-12T14:58:31Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"region:us"
] | null | 2023-11-12T14:58:26Z |
---
library_name: peft
base_model: huggyllama/llama-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[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]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Citation [optional]
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## Glossary [optional]
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[More Information Needed]
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## 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 0.7.0.dev0
|
mounika-7/my-pet-cat
|
mounika-7
| 2023-11-12T14:57:23Z | 10 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-12T14:52:23Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat Dreambooth model trained by mounika-7 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-96
Sample pictures of this concept:
.jpg)
|
kejolong/asianbeauty
|
kejolong
| 2023-11-12T14:44:12Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-11-11T06:06:05Z |
---
license: creativeml-openrail-m
---
|
yaochung/antifungal-linguist
|
yaochung
| 2023-11-12T14:31:29Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-12T13:36:31Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
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 [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 0.6058
- Rouge2: 0.5996
- Rougel: 0.6054
- Rougelsum: 0.6061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0023 | 1.0 | 1667 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
| 0.0001 | 2.0 | 3334 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
| 0.0 | 3.0 | 5001 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
rizkyjun/bloom-1b-finetuned-aings-adapters-chat-2
|
rizkyjun
| 2023-11-12T14:10:29Z | 4 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b1",
"base_model:adapter:bigscience/bloom-1b1",
"region:us"
] | null | 2023-11-12T09:21:39Z |
---
library_name: peft
base_model: bigscience/bloom-1b1
---
# 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 0.6.2.dev0
## 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 0.6.2.dev0
|
joachimsallstrom/aether-pixel-lora-for-sdxl
|
joachimsallstrom
| 2023-11-12T14:09:16Z | 482 | 9 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"style",
"pixelart",
"dissolving",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2023-11-12T14:09:04Z |
---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- style
- pixelart
- dissolving
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: dissolving into pixels
widget:
- text: " a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background"
- text: " photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night"
- text: "super mario dissolving into pixels,, windy, dark background "
- text: " neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background"
- text: "(melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty."
- text: " a sloth dissolving into pixels"
- text: " photo of a woman dissolving into pixels"
- text: " photo profile of a man dissolving into pixels upwards, windy"
- text: "a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8 "
- text: " a banana dissolving into pixels"
---
# Aether Pixel - LoRA for SDXL
> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background
<p>This is Aether <strong><span style="color:rgb(255, 0, 0)">P</span><span style="color:rgb(0, 255, 17)">I</span><span style="color:rgb(0, 174, 255)">X</span><span style="color:rgb(255, 0, 0)">E</span><span style="color:rgb(0, 255, 17)">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>
## Image examples for the model:
> photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night
> super mario dissolving into pixels,, windy, dark background
> neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background
> (melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty.
> a sloth dissolving into pixels
> photo of a woman dissolving into pixels
> photo profile of a man dissolving into pixels upwards, windy
> a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8
> a banana dissolving into pixels
|
mesolitica/malaysian-llama2-7b-32k-instructions-AWQ
|
mesolitica
| 2023-11-12T14:08:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"ms",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-11-07T14:49:43Z |
---
language:
- ms
---
# 4 bit AWQ QLORA Malaysian Llama2 7B 32k chat completions
Original model at https://huggingface.co/mesolitica/malaysian-llama2-7b-32k-instructions, read more about AWQ integration at https://huggingface.co/docs/transformers/main_classes/quantization#awq-integration
## how-to
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
def parse_llama_chat(messages):
system = messages[0]['content']
user_query = messages[-1]['content']
users, assistants = [], []
for q in messages[1:-1]:
if q['role'] == 'user':
users.append(q['content'])
elif q['role'] == 'assistant':
assistants.append(q['content'])
texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n']
for u, a in zip(users, assistants):
texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ')
texts.append(f'{user_query.strip()} [/INST]')
prompt = ''.join(texts).strip()
return prompt
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-llama2-7b-32k-instructions-AWQ')
model = AutoModelForCausalLM.from_pretrained(
'mesolitica/malaysian-llama2-7b-32k-instructions-AWQ',
use_flash_attention_2 = True,
)
_ = model.cuda()
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'kwsp tu apa'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
'<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
kwsp tu apa [/INST] Kumpulan Wang Simpanan Pekerja (KWSP) ialah sebuah badan berkanun yang ditubuhkan di Malaysia yang menguruskan tabung simpanan tetap pekerja bagi tujuan persaraan dan perancangan masa depan. Diasaskan pada tahun 1951, KWSP bertanggungjawab untuk mengumpul dan menguruskan sumber daripada majikan dan pekerja, dan juga menyediakan pelbagai faedah kepada ahli seperti dividen dan akses kepada pengeluaran simpanan pada usia persaraan. KWSP juga memainkan peranan penting dalam menyediakan perlindungan sosial dan pembangunan ekonomi di Malaysia, dengan mempromosikan simpanan pengguna dan meningkatkan kadar celik kewangan dalam kalangan rakyat. </s>'
```
|
rizkyjun/bloom-1b-finetuned-aings-adapters-chat-1
|
rizkyjun
| 2023-11-12T14:05:00Z | 14 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b1",
"base_model:adapter:bigscience/bloom-1b1",
"region:us"
] | null | 2023-11-12T09:16:10Z |
---
library_name: peft
base_model: bigscience/bloom-1b1
---
# 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 0.6.2.dev0
## 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 0.6.2.dev0
|
matemato/sd-class-butterflies-32
|
matemato
| 2023-11-12T13:59:03Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-11-12T13:57:05Z |
---
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('matemato/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
thingthatis/sdxl-vae
|
thingthatis
| 2023-11-12T13:56:13Z | 8 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"arxiv:2112.10752",
"license:mit",
"region:us"
] | null | 2023-11-12T13:56:13Z |
---
license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
inference: false
---
# SDXL - VAE
#### How to use with 🧨 diffusers
You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline`
```py
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionPipeline
model = "stabilityai/your-stable-diffusion-model"
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
```
## Model
[SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) is a [latent diffusion model](https://arxiv.org/abs/2112.10752), where the diffusion operates in a pretrained,
learned (and fixed) latent space of an autoencoder.
While the bulk of the semantic composition is done by the latent diffusion model,
we can improve _local_, high-frequency details in generated images by improving the quality of the autoencoder.
To this end, we train the same autoencoder architecture used for the original [Stable Diffusion](https://github.com/CompVis/stable-diffusion) at a larger batch-size (256 vs 9)
and additionally track the weights with an exponential moving average (EMA).
The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.
## Evaluation
_SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_
### COCO 2017 (256x256, val, 5000 images)
| Model | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|------|--------------|---------------|---------------|------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | |
| SDXL-VAE | 4.42 | 24.7 +/- 3.9 | 0.73 +/- 0.13 | 0.88 +/- 0.27 | https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors | as used in SDXL |
| original | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-MSE | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
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Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
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Filtered Qwen 7B Model Cards
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Qwen 7B Distilled Models
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Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
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Filtered Model Cards with Distill Qwen 7
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Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.