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anon11112/maid | anon11112 | "2024-06-13T18:20:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:19:34Z" | Entry not found |
Egg0512/NikolaiGogol | Egg0512 | "2024-06-13T18:20:43Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:20:02Z" | Entry not found |
AnatolyBelov/dataset2_lora_model_1 | AnatolyBelov | "2024-06-13T18:20:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:20:24Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** AnatolyBelov
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AnatolyBelov/dataset2_lora_model_1_merge16 | AnatolyBelov | "2024-06-13T18:22:37Z" | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:22:36Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** AnatolyBelov
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Felladrin/mlc-q4f16-phi-2-orange-v2 | Felladrin | "2024-06-13T18:26:27Z" | 0 | 0 | null | [
"base_model:rhysjones/phi-2-orange-v2",
"license:mit",
"region:us"
] | null | "2024-06-13T18:23:57Z" | ---
license: mit
base_model: rhysjones/phi-2-orange-v2
---
[MLC](https://llm.mlc.ai/) version of [rhysjones/phi-2-orange-v2](https://huggingface.co/rhysjones/phi-2-orange-v2), using `q4f16_1` quantization.
|
anon11112/groupgirl | anon11112 | "2024-06-13T18:24:36Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:24:09Z" | Entry not found |
sahanes/llama38binstruct_summarize | sahanes | "2024-06-13T18:27:43Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | "2024-06-13T18:27:25Z" | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: llama38binstruct_summarize
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. -->
# llama38binstruct_summarize
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7376
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5341 | 1.25 | 25 | 1.7171 |
| 0.4741 | 2.5 | 50 | 1.6703 |
| 0.2744 | 3.75 | 75 | 1.7592 |
| 0.1287 | 5.0 | 100 | 1.7376 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
stellazsda/finetuned_wizardcoder | stellazsda | "2024-06-13T18:31:21Z" | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | "2024-06-13T18:31:21Z" | ---
license: llama2
---
|
agarelli/lagarelli | agarelli | "2024-06-13T18:31:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:31:25Z" | Entry not found |
wqdqqasd/Scourge | wqdqqasd | "2024-06-13T18:35:09Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T18:33:20Z" | ---
license: openrail
---
|
Grannn/vit-base-patch16-224-in21k-finetuned-lora-food101 | Grannn | "2024-06-13T18:33:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:33:25Z" | Entry not found |
mbzuai-ugrip-statement-tuning/XLMR-large2-multi-109k_1e-06_8_0.1_0.01 | mbzuai-ugrip-statement-tuning | "2024-06-13T18:36:47Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-13T18:33:58Z" | Entry not found |
Arpx22/whisper-small-en | Arpx22 | "2024-06-14T14:14:52Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-13T18:35:50Z" | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-en
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. -->
# whisper-small-en
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0947
- Wer: 11.3030
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.0 | 7.6923 | 100 | 0.0560 | 11.6170 |
| 0.0 | 15.3846 | 200 | 0.0666 | 13.9717 |
| 0.0 | 23.0769 | 300 | 0.0754 | 13.8148 |
| 0.0 | 30.7692 | 400 | 0.0850 | 14.1287 |
| 0.0 | 38.4615 | 500 | 0.0945 | 13.1868 |
| 0.0 | 46.1538 | 600 | 0.1042 | 13.0298 |
| 0.0 | 53.8462 | 700 | 0.1147 | 13.8148 |
| 0.0 | 61.5385 | 800 | 0.1256 | 14.1287 |
| 0.0 | 69.2308 | 900 | 0.1361 | 14.7567 |
| 0.0 | 76.9231 | 1000 | 0.1487 | 13.8148 |
| 0.0 | 84.6154 | 1100 | 0.1619 | 17.1115 |
| 0.0 | 92.3077 | 1200 | 0.1759 | 17.2684 |
| 0.0 | 100.0 | 1300 | 0.1866 | 17.1115 |
| 0.0 | 107.6923 | 1400 | 0.1979 | 17.1115 |
| 0.0043 | 115.3846 | 1500 | 0.0933 | 10.2041 |
| 0.0 | 123.0769 | 1600 | 0.0901 | 10.9890 |
| 0.0 | 130.7692 | 1700 | 0.0914 | 11.3030 |
| 0.0 | 138.4615 | 1800 | 0.0922 | 11.3030 |
| 0.0 | 146.1538 | 1900 | 0.0929 | 11.3030 |
| 0.0 | 153.8462 | 2000 | 0.0933 | 11.3030 |
| 0.0 | 161.5385 | 2100 | 0.0938 | 11.3030 |
| 0.0 | 169.2308 | 2200 | 0.0942 | 11.3030 |
| 0.0 | 176.9231 | 2300 | 0.0943 | 11.3030 |
| 0.0 | 184.6154 | 2400 | 0.0945 | 11.3030 |
| 0.0 | 192.3077 | 2500 | 0.0947 | 11.3030 |
| 0.0 | 200.0 | 2600 | 0.0947 | 11.3030 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
MythicAI-India/JagaGPT | MythicAI-India | "2024-06-13T18:36:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:36:46Z" | Entry not found |
koderr1/1 | koderr1 | "2024-06-13T18:37:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:37:14Z" | Entry not found |
jeantaq/planodetr | jeantaq | "2024-06-13T18:37:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:37:56Z" | Entry not found |
salahyahya/weightsGECcheckPOINT5000 | salahyahya | "2024-06-13T18:38:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:38:49Z" | Entry not found |
walnash/ERAV2_S19_BasicGPT | walnash | "2024-06-13T18:39:45Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-13T18:39:09Z" | ---
license: mit
---
|
TransLearner/SageMaker | TransLearner | "2024-06-13T18:39:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:39:41Z" | Entry not found |
EKrizhov/1 | EKrizhov | "2024-06-13T18:42:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:42:31Z" | Entry not found |
EgorP11/1 | EgorP11 | "2024-06-13T18:43:56Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-13T18:43:56Z" | ---
license: apache-2.0
---
|
neuronpedia/gpt2-small__res_fs24576-jb | neuronpedia | "2024-06-13T18:50:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:49:58Z" | Entry not found |
YupengCao/finnlp-challenge-finetuned-llama3-8b-task1 | YupengCao | "2024-06-13T18:52:44Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | "2024-06-13T18:50:57Z" | ---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B-Instruct
---
# 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]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[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]
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<!-- 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]
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- **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]
### Framework versions
- PEFT 0.11.1 |
bdsaglam/llama-3-8b-jerx-peft-9joqzpch | bdsaglam | "2024-06-13T18:51:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:51:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### 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
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[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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[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. -->
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## Model Card Contact
[More Information Needed] |
bdsaglam/llama-3-8b-jerx-peft-mdycrweq | bdsaglam | "2024-06-13T18:51:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:51:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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bdsaglam/llama-3-8b-jerx-peft-d4z0p44f | bdsaglam | "2024-06-13T18:51:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:51:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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bdsaglam/llama-3-8b-jerx-peft-2yvdndtb | bdsaglam | "2024-06-13T18:51:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:51:26Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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cfchase/Llama-2-7b-chat-hf-fine-tuned | cfchase | "2024-06-14T13:10:31Z" | 0 | 0 | peft | [
"peft",
"pytorch",
"llama",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | "2024-06-13T18:51:42Z" | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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
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[More Information Needed]
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#### 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]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.11.2.dev0 |
neuronpedia/gpt2-small__res_fs3072-jb | neuronpedia | "2024-06-13T19:01:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T18:57:57Z" | Entry not found |
sentrifybot/Emotion-1.0 | sentrifybot | "2024-06-13T20:33:26Z" | 0 | 0 | sklearn | [
"sklearn",
"emotions",
"text-classification",
"en",
"license:apache-2.0",
"region:us"
] | text-classification | "2024-06-13T18:58:07Z" | ---
license: apache-2.0
language:
- en
library_name: sklearn
tags:
- emotions
pipeline_tag: text-classification
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_description: "Our team may take 2-3 days to process your request"
extra_gated_button_content: "Acknowledge license"
extra_gated_prompt: "You agree to not use the model to conduct experiments that cause harm to human subjects."
extra_gated_fields:
Company: text
Country: country
Specific date: date_picker
I want to use this model for:
type: select
options:
- Research
- Education
- label: Other
value: other
I agree to use this model for non-commercial use ONLY (if you want to use the model for commerial reasons, please email us and get our DIRECT aproval before doing so): checkbox
---
# Emotion-1.0
## Model Details
### Model Description
This advanced AI model accurately detects emotions in text using state-of-the-art natural language processing techniques. It can identify a wide range of emotions, providing valuable insights for businesses and individuals alike. With its machine learning algorithms, it continually refines its understanding of human emotions, offering reliable results at scale.
- **Developed by:** SentrifyAI
- **Funded by:** Sentrify
- **Model type:** Text Classification
- **Language(s) (NLP):** English
- **License:** Apache-2.0
## Uses
Some uses for this model are:
- Social Media
- Moderation
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from sentrifyai import api
import json
emotions = api.Emotions()
results = emotions.emotion(model_slug='Emotions-1.0', message='This is a sample message.')
json_results = json.dumps(results, indent=4)
print(json_results)
```
Note: Make sure to install the `sentrifyai` PyPi package.
```
pip install sentrifyai
```
## Model Card Authors
The Sentrify Team |
Sanjay19tsh/lora_model | Sanjay19tsh | "2024-06-13T18:59:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T18:58:57Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Sanjay19tsh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Loren85/Prince-Betty-Boop-1934 | Loren85 | "2024-06-13T19:00:11Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T18:58:58Z" | ---
license: openrail
---
|
metta-ai/baseline.v0.4.2 | metta-ai | "2024-06-13T19:01:30Z" | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | reinforcement-learning | "2024-06-13T19:01:11Z" | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
---
A(n) **APPO** model trained on the **GDY-MettaGrid** 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 metta-ai/baseline.v0.4.2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.4.2
```
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 <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.4.2 --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.
|
neuronpedia/gpt2-small__res_fs6144-jb | neuronpedia | "2024-06-13T19:02:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:01:50Z" | Entry not found |
neuronpedia/gpt2-small__res_fs12288-jb | neuronpedia | "2024-06-13T19:02:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:02:20Z" | Entry not found |
Xdoes/llama-3-8b-testGPT | Xdoes | "2024-06-13T19:03:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T19:02:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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. -->
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## Evaluation
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### 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
rolamkhar/Dante_AI | rolamkhar | "2024-06-13T19:15:37Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-13T19:14:24Z" | ---
license: apache-2.0
---
|
nrohan09/Aseer_merged_16bit | nrohan09 | "2024-06-13T19:15:31Z" | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T19:15:26Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** nrohan09
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sanchezalberto/ModeloPrueba | sanchezalberto | "2024-06-13T19:18:25Z" | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-06-13T19:17:46Z" | ---
license: cc-by-nc-4.0
---
|
onizukal/Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1 | onizukal | "2024-06-13T21:06:49Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-13T19:18:32Z" | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8561998578247182
---
<!-- 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. -->
# Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6210
- Accuracy: 0.8562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.314 | 1.0 | 2469 | 0.3707 | 0.8430 |
| 0.2191 | 2.0 | 4938 | 0.3892 | 0.8507 |
| 0.1908 | 3.0 | 7407 | 0.4759 | 0.8516 |
| 0.0575 | 4.0 | 9876 | 0.6918 | 0.8571 |
| 0.0175 | 5.0 | 12345 | 1.0455 | 0.8526 |
| 0.1052 | 6.0 | 14814 | 1.2531 | 0.8548 |
| 0.0016 | 7.0 | 17283 | 1.3936 | 0.8554 |
| 0.0 | 8.0 | 19752 | 1.5161 | 0.8563 |
| 0.0218 | 9.0 | 22221 | 1.6233 | 0.8582 |
| 0.0 | 10.0 | 24690 | 1.6210 | 0.8562 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Corcelio/FastSAM-x | Corcelio | "2024-06-13T19:19:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:18:36Z" | Entry not found |
naoyasu07/example-model | naoyasu07 | "2024-06-14T04:36:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:20:15Z" | # Example Model
This is my model card README
---
license: mit
---
|
Ilya-Nazimov/ruBert-large-ner | Ilya-Nazimov | "2024-06-13T19:21:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:21:11Z" | Entry not found |
Ilya-Nazimov/ruBert-tiny2-ner | Ilya-Nazimov | "2024-06-13T19:23:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:23:25Z" | Entry not found |
depth-anything/Depth-Anything-V2-Metric-Hypersim-Large | depth-anything | "2024-06-21T16:45:21Z" | 0 | 2 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-13T19:24:09Z" | ---
license: apache-2.0
---
# Depth Anything V2 for Metric Depth Estimation
# Pre-trained Models
We provide **six metric depth models** of three scales for indoor and outdoor scenes, respectively.
| Base Model | Params | Indoor (Hypersim) | Outdoor (Virtual KITTI 2) |
|:-|-:|:-:|:-:|
| Depth-Anything-V2-Small | 24.8M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Small/resolve/main/depth_anything_v2_metric_hypersim_vits.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Small/resolve/main/depth_anything_v2_metric_vkitti_vits.pth?download=true) |
| Depth-Anything-V2-Base | 97.5M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Base/resolve/main/depth_anything_v2_metric_hypersim_vitb.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Base/resolve/main/depth_anything_v2_metric_vkitti_vitb.pth?download=true) |
| Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Large/resolve/main/depth_anything_v2_metric_hypersim_vitl.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Large/resolve/main/depth_anything_v2_metric_vkitti_vitl.pth?download=true) |
*We recommend to first try our larger models (if computational cost is affordable) and the indoor version.*
## Usage
### Prepraration
```bash
git clone https://github.com/DepthAnything/Depth-Anything-V2
cd Depth-Anything-V2/metric_depth
pip install -r requirements.txt
```
Download the checkpoints listed [here](#pre-trained-models) and put them under the `checkpoints` directory.
### Use our models
```python
import cv2
import torch
from depth_anything_v2.dpt import DepthAnythingV2
model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}
}
encoder = 'vitl' # or 'vits', 'vitb'
dataset = 'hypersim' # 'hypersim' for indoor model, 'vkitti' for outdoor model
max_depth = 20 # 20 for indoor model, 80 for outdoor model
model = DepthAnythingV2(**{**model_configs[encoder], 'max_depth': max_depth})
model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_metric_{dataset}_{encoder}.pth', map_location='cpu'))
model.eval()
raw_img = cv2.imread('your/image/path')
depth = model.infer_image(raw_img) # HxW depth map in meters in numpy
```
### Running script on images
Here, we take the `vitl` encoder as an example. You can also use `vitb` or `vits` encoders.
```bash
# indoor scenes
python run.py \
--encoder vitl \
--load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
--max-depth 20 \
--img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
# outdoor scenes
python run.py \
--encoder vitl \
--load-from checkpoints/depth_anything_v2_metric_vkitti_vitl.pth \
--max-depth 80 \
--img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
```
### Project 2D images to point clouds:
```bash
python depth_to_pointcloud.py \
--encoder vitl \
--load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
--max-depth 20 \
--img-path <path> --outdir <outdir>
```
### Reproduce training
Please first prepare the [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets. Then:
```bash
bash dist_train.sh
```
## Citation
If you find this project useful, please consider citing:
```bibtex
@article{depth_anything_v2,
title={Depth Anything V2},
author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
journal={arXiv:2406.09414},
year={2024}
}
@inproceedings{depth_anything_v1,
title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
booktitle={CVPR},
year={2024}
}
```
|
huruisi/BiLSTM_embed | huruisi | "2024-06-13T19:48:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:24:42Z" | Entry not found |
Hyumseok/Hyumseok_also | Hyumseok | "2024-06-13T19:33:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-06-13T19:25:20Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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[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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
satani/trained-sd3 | satani | "2024-06-13T19:25:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:25:29Z" | Entry not found |
cminja/llama-2-70b-hf-sp-srds | cminja | "2024-06-13T23:11:48Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-13T19:28:55Z" | ---
license: llama2
---
# Llama-2 70b HF SP SRDS Model
## Description
Finetuned version of the Llama-70b model, optimized for text conversational agents. This finetune incorporates the XGaming dataset to improve performance in gaming-related contexts, trained on a 100M tokens of twitch chat and gaming terms. Implemented using PyTorch and the Transformers framework.
|
Ikrameeatir/paligemma | Ikrameeatir | "2024-06-13T19:32:55Z" | 0 | 0 | transformers | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T19:29:58Z" | Entry not found |
sfanm/ultrafeedback_llama3_label | sfanm | "2024-06-13T19:31:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:31:47Z" | Entry not found |
rogerbr/sample | rogerbr | "2024-06-13T19:33:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:33:20Z" | Entry not found |
thewordsmiths/Llama_SciQ_quantize-mcq_8bits_gptq_wikitext2_reg | thewordsmiths | "2024-06-13T19:39:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | "2024-06-13T19:35:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
hugging-michael/example-model | hugging-michael | "2024-06-13T19:56:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:36:57Z" | # Example Model
This is my model card README.
---
license: mit
---
|
LoneStriker/Maiden-Unquirked-20B-4.0bpw-h6-exl2 | LoneStriker | "2024-06-13T19:41:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"not-for-all-audiences",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:TeeZee/BigMaid-20B-v1.0",
"base_model:TeeZee/DarkForest-20B-v2.0",
"base_model:athirdpath/Harmonia-20B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"exl2",
"region:us"
] | text-generation | "2024-06-13T19:37:02Z" | ---
base_model:
- TeeZee/BigMaid-20B-v1.0
- TeeZee/DarkForest-20B-v2.0
- athirdpath/Harmonia-20B
library_name: transformers
tags:
- mergekit
- merge
- not-for-all-audiences
---
![](maidenq.png)
# Maiden-Unquirked-20B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
See Below
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [TeeZee/DarkForest-20B-v2.0](https://huggingface.co/TeeZee/DarkForest-20B-v2.0) as a base.
### Models Merged
The following models were included in the merge:
* [TeeZee/BigMaid-20B-v1.0](https://huggingface.co/TeeZee/BigMaid-20B-v1.0)
* [athirdpath/Harmonia-20B](https://huggingface.co/athirdpath/Harmonia-20B)
* [TeeZee/DarkForest-20B-v2.0](https://huggingface.co/TeeZee/DarkForest-20B-v2.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TeeZee/DarkForest-20B-v2.0
- model: athirdpath/Harmonia-20B
parameters:
weight: 0.5
density: 1.0
- model: TeeZee/BigMaid-20B-v1.0
parameters:
weight: 0.5
density: 1.0
merge_method: dare_ties
base_model: TeeZee/DarkForest-20B-v2.0
parameters:
int8_mask: true
dtype: bfloat16
name: maiden_unquirked
``` |
PLS442/Jackson | PLS442 | "2024-06-13T19:40:57Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T19:39:22Z" | ---
license: openrail
---
|
magnifi/parser_user_v5-0613-epoch5-0.002_user_and_ontology_upper_ticker_time_nosystem_prompt | magnifi | "2024-06-13T19:42:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-13T19:40:20Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
LoneStriker/Maiden-Unquirked-20B-4.55bpw-h6-exl2 | LoneStriker | "2024-06-13T19:46:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"not-for-all-audiences",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:TeeZee/BigMaid-20B-v1.0",
"base_model:TeeZee/DarkForest-20B-v2.0",
"base_model:athirdpath/Harmonia-20B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"exl2",
"region:us"
] | text-generation | "2024-06-13T19:41:24Z" | ---
base_model:
- TeeZee/BigMaid-20B-v1.0
- TeeZee/DarkForest-20B-v2.0
- athirdpath/Harmonia-20B
library_name: transformers
tags:
- mergekit
- merge
- not-for-all-audiences
---
![](maidenq.png)
# Maiden-Unquirked-20B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
See Below
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [TeeZee/DarkForest-20B-v2.0](https://huggingface.co/TeeZee/DarkForest-20B-v2.0) as a base.
### Models Merged
The following models were included in the merge:
* [TeeZee/BigMaid-20B-v1.0](https://huggingface.co/TeeZee/BigMaid-20B-v1.0)
* [athirdpath/Harmonia-20B](https://huggingface.co/athirdpath/Harmonia-20B)
* [TeeZee/DarkForest-20B-v2.0](https://huggingface.co/TeeZee/DarkForest-20B-v2.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TeeZee/DarkForest-20B-v2.0
- model: athirdpath/Harmonia-20B
parameters:
weight: 0.5
density: 1.0
- model: TeeZee/BigMaid-20B-v1.0
parameters:
weight: 0.5
density: 1.0
merge_method: dare_ties
base_model: TeeZee/DarkForest-20B-v2.0
parameters:
int8_mask: true
dtype: bfloat16
name: maiden_unquirked
``` |
malias/trained-sd3 | malias | "2024-06-13T19:41:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:41:51Z" | Entry not found |
candrews1971/ppo-LunarLander-v2.1 | candrews1971 | "2024-06-14T15:29:51Z" | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-13T19:42:24Z" | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 0.60 +/- 1.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': '3million'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': True
'wandb_project_name': 'LunarLanderProject'
'wandb_entity': None
'capture_video': True
'env_id': 'LunarLander-v2'
'total_timesteps': 3000000
'learning_rate': 0.0004
'num_envs': 4
'num_steps': 128
'anneal_lr': False
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 4
'update_epochs': 16
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'num_hidden_layers': 2
'upload_to_hf': True
'repo_id': 'candrews1971/ppo-LunarLander-v2.1'
'batch_size': 512
'minibatch_size': 128}
```
|
a01110946/unsloth-Qwen2-7b-Instruct-16bit-32k-tok-context-Mexican-Federal-Laws-Inst-FineTuned-step1 | a01110946 | "2024-06-13T19:48:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T19:47:34Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
base_model: unsloth/Qwen2-7B-Instruct
---
# Uploaded model
- **Developed by:** a01110946
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
torjusik/JUICEWRLDAI | torjusik | "2024-06-13T19:49:02Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-13T19:48:12Z" | ---
license: mit
---
|
AI-Wheelz/WestonEstate-Manas | AI-Wheelz | "2024-06-13T19:49:39Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T19:48:45Z" | ---
license: openrail
---
|
marco925/sygnet | marco925 | "2024-06-13T19:50:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:50:33Z" | Entry not found |
patrycr/Tania | patrycr | "2024-06-13T20:00:28Z" | 0 | 0 | null | [
"music",
"es",
"arxiv:1910.09700",
"region:us"
] | null | "2024-06-13T19:53:08Z" | ---
language:
- es
tags:
- music
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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] |
adbrasi/girl3-trained-sd3 | adbrasi | "2024-06-13T19:53:57Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:53:56Z" | Entry not found |
Hyumseok/Hyumseok_also_new | Hyumseok | "2024-06-13T19:54:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:54:25Z" | Entry not found |
huduque/sd3 | huduque | "2024-06-13T20:15:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:54:29Z" | Entry not found |
khairi/ProtToNL | khairi | "2024-06-13T19:55:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-13T19:54:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Hyumseok/Hyumseok_also_new_again | Hyumseok | "2024-06-13T19:55:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:55:31Z" | Entry not found |
MG31/dfm_detr_30_b4_n4 | MG31 | "2024-06-13T19:56:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T19:56:49Z" | Entry not found |
Tester404/test-v1 | Tester404 | "2024-06-13T19:56:50Z" | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | "2024-06-13T19:56:50Z" | ---
license: llama2
---
|
salmaafifi98/opt-350 | salmaafifi98 | "2024-06-13T20:00:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-13T19:59:53Z" | Entry not found |
TheHoodieFerret/newimageGen | TheHoodieFerret | "2024-06-13T20:00:37Z" | 0 | 0 | null | [
"license:cc-by-nd-4.0",
"region:us"
] | null | "2024-06-13T20:00:37Z" | ---
license: cc-by-nd-4.0
---
|
Ilya-Nazimov/my_awesome_wnut_model | Ilya-Nazimov | "2024-06-13T20:03:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:03:17Z" | Entry not found |
dbands/llama-3-8b-Instruct-bnb-4bit-4bit-Alpacha-lora | dbands | "2024-06-14T06:01:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:04:44Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** dbands
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dbands/llama-3-8b-Instruct-bnb-4bit-Alpacha-lora_model | dbands | "2024-06-14T06:01:29Z" | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:05:03Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
gleekerperson/ryder | gleekerperson | "2024-06-13T20:07:21Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-13T20:05:18Z" | ---
license: apache-2.0
---
|
dbands/llama-3-8b-Instruct-bnb-4bit-Alpacha-merged_16bit | dbands | "2024-06-14T06:01:49Z" | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:05:26Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** dbands
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
xMaulana/QLoRA-Psychika-v1.1 | xMaulana | "2024-06-13T20:06:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:indischepartij/mialatte-indo-mistral-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:06:39Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: indischepartij/mialatte-indo-mistral-7b
---
# Uploaded model
- **Developed by:** xMaulana
- **License:** apache-2.0
- **Finetuned from model :** indischepartij/mialatte-indo-mistral-7b
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
gleekerperson/unique | gleekerperson | "2024-06-13T20:10:00Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T20:08:20Z" | ---
license: openrail
---
|
bdsaglam/llama-3-8b-jerx-rltf-peft-fohm4yuh | bdsaglam | "2024-06-13T20:10:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:10:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
gleekerperson/sam | gleekerperson | "2024-06-13T20:13:27Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T20:10:18Z" | ---
license: openrail
---
|
seri00990/test | seri00990 | "2024-06-13T20:11:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:10:56Z" | test |
gleekerperson/jake | gleekerperson | "2024-06-13T20:15:32Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-13T20:14:22Z" | ---
license: openrail
---
|
yasirchemmakh/SLAD-llama3-7b | yasirchemmakh | "2024-06-13T20:15:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:14:49Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** yasirchemmakh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TurnerBrit/HAL | TurnerBrit | "2024-06-13T20:15:31Z" | 0 | 0 | null | [
"license:llama3",
"region:us"
] | null | "2024-06-13T20:15:31Z" | ---
license: llama3
---
|
sultanaw/fine_tuned_setfit_pydata_demo | sultanaw | "2024-06-14T13:59:33Z" | 0 | 0 | setfit | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:SetFit/SentEval-CR",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] | text-classification | "2024-06-13T20:18:35Z" | ---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- SetFit/SentEval-CR
metrics:
- accuracy
widget:
- text: you can take pic of your friends and the picture will pop up when they call
.
- text: the speakerphone , the radio , all features work perfectly .
- text: 'a ) the picture quality ( color and sharpness of focusing ) are so great
, it completely eliminated my doubt about digital imaging -- - how could one eat
rice one grain at a time : - ) )'
- text: so far the dvd works so i hope it does n 't break down like the reviews i
've read .
- text: i have a couple hundred contacts and the menu loads within a few seconds ,
no big deal .
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: SetFit/SentEval-CR
type: SetFit/SentEval-CR
split: test
metrics:
- type: accuracy
value: 0.8313413014608234
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
- **Training Dataset:** [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> |
| 0 | <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8313 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("sultanaw/fine_tuned_setfit_pydata_demo")
# Run inference
preds = model("the speakerphone , the radio , all features work perfectly .")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 18.0625 | 44 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 7 |
| 1 | 9 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.1 | 1 | 0.2267 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.40.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
ubermenchh/clip-gpt2-caption | ubermenchh | "2024-06-14T08:20:09Z" | 0 | 0 | transformers | [
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-13T20:19:57Z" | ---
license: apache-2.0
---
|
alexandro767/saiga_for_text2sql_v2 | alexandro767 | "2024-06-13T23:30:43Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-13T20:21:07Z" | Entry not found |
yassinechaouch/Mistral-best-sft | yassinechaouch | "2024-06-13T20:24:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-13T20:21:17Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
TimTime99/Tim | TimTime99 | "2024-06-13T20:22:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:22:07Z" | Entry not found |
preciouscript/swin-tiny-patch4-window7-224-finetuned-eurosat | preciouscript | "2024-06-13T20:26:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:26:15Z" | Entry not found |
satani/trained-sd3-lora | satani | "2024-06-13T20:28:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:28:00Z" | Entry not found |
atinrao/llama38binstruct_summarize | atinrao | "2024-06-13T20:30:27Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | "2024-06-13T20:30:18Z" | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: llama38binstruct_summarize
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. -->
# llama38binstruct_summarize
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5019 | 1.25 | 25 | 1.4253 |
| 0.5119 | 2.5 | 50 | 1.8478 |
| 0.2398 | 3.75 | 75 | 1.8915 |
| 0.1067 | 5.0 | 100 | 1.9058 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
LarryAIDraw/perfection_style | LarryAIDraw | "2024-06-13T20:35:58Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-13T20:30:29Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/411088?modelVersionId=120335 |
LarryAIDraw/3Danimation_Disney_1_0 | LarryAIDraw | "2024-06-13T20:36:22Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-13T20:32:33Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/405143?modelVersionId=467356 |
Ridha8888/Hello | Ridha8888 | "2024-06-13T20:34:05Z" | 0 | 0 | allennlp | [
"allennlp",
"chemistry",
"token-classification",
"ab",
"dataset:HuggingFaceFW/fineweb",
"license:apache-2.0",
"region:us"
] | token-classification | "2024-06-13T20:32:57Z" | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- ab
metrics:
- bertscore
library_name: allennlp
pipeline_tag: token-classification
tags:
- chemistry
--- |
elee25/q-FrozenLake-v1-4x4-noSlippery | elee25 | "2024-06-13T20:34:14Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-13T20:34:11Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="elee25/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
dcyyy/fye | dcyyy | "2024-06-13T22:02:19Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-13T20:38:34Z" | Entry not found |