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odvakdvd/bachira | odvakdvd | "2024-06-07T12:30:10Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:Blib-la/blibla_art_style",
"base_model:adapter:Blib-la/blibla_art_style",
"license:apache-2.0",
"region:us"
] | text-to-image | "2024-06-07T12:29:28Z" | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/3dd2b5d34d7981ae843cccd2f4b24b16.png
base_model: Blib-la/blibla_art_style
instance_prompt: null
license: apache-2.0
---
# bachira
<Gallery />
## Model description
## Download model
[Download](/odvakdvd/bachira/tree/main) them in the Files & versions tab.
|
oumaymaMb/llama-2-7b-NER-v9 | oumaymaMb | "2024-06-07T12:30:35Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2024-06-07T12:30:22Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
alexgrigore/videomae-base-finetuned-fakeDataset-gesturePhasePleaseWork | alexgrigore | "2024-06-07T12:46:51Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] | video-classification | "2024-06-07T12:31:57Z" | Entry not found |
unibuc-cs/CyberGuardian | unibuc-cs | "2024-06-07T13:02:43Z" | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | "2024-06-07T12:34:42Z" | ---
license: mit
---
A Llama3 based model fine-tuned for cybersecurity domain.
The model was finetuned over https://huggingface.co/datasets/unibuc-cs/CyberGuardianDataset.
While the Llama3 original perplexity on the dataset started ~21.9, we managed to fine-tune to ~7.8 on this dataset, still preserving the general language abilities.
You can load the model and see its LoRA config as below:
```
from peft import get_peft_model, AutoPeftModelForCausalLM, PeftModel, PeftConfig
from transformers import AutoModel
config = PeftConfig.from_pretrained("unibuc-cs/CyberGuardian")
model = AutoModel.from_pretrained(config.base_model_name_or_path)
```
Refer to our github page for details: https://github.com/unibuc-cs/CyberGuardian |
jstonge1/dark-data-lora-test | jstonge1 | "2024-06-07T12:39:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T12:39:18Z" | ---
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:** jstonge1
- **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)
|
silent666/Qwen-Qwen1.5-7B-1717763962 | silent666 | "2024-06-07T12:39:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T12:39:25Z" | Entry not found |
Bdfrancaiss/bdfrancais | Bdfrancaiss | "2024-06-07T12:43:43Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T12:43:42Z" | ---
license: apache-2.0
---
|
harkad6e/Autotest-LLM | harkad6e | "2024-06-07T12:55:18Z" | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T12:51:06Z" | ---
license: apache-2.0
---
|
silent666/Qwen-Qwen1.5-7B-1717764893 | silent666 | "2024-06-07T12:54:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T12:54:56Z" | Entry not found |
ostmbh/2024-06-07-20-57-01_qwen__root_autodl-tmp_exp_PROMPT_TUNING_v1 | ostmbh | "2024-06-07T12:57:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T12:57:10Z" | ---
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]
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### 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]
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#### Preprocessing [optional]
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#### Training Hyperparameters
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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
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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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## 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] |
Labira/LabiraEdu.v1.0 | Labira | "2024-06-07T12:57:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T12:57:35Z" | Entry not found |
TIva16/paligemma_vqav2 | TIva16 | "2024-06-07T12:57:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T12:57:44Z" | Entry not found |
lukaskellerstein/tools-mistral-4bit-lora-adapter | lukaskellerstein | "2024-06-07T12:59:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T12:58:06Z" | ---
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]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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harkad6e/hackathonAAHLLM | harkad6e | "2024-06-07T13:00:10Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T13:00:10Z" | ---
license: apache-2.0
---
|
Dumele/autotrain-shhsb-57a2l | Dumele | "2024-06-07T13:03:03Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-07T13:02:45Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: mistralai/Mistral-7B-Instruct-v0.2
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
MelissaJ/MyLuciaTTS | MelissaJ | "2024-06-07T13:42:08Z" | 0 | 0 | null | [
"text-to-speech",
"ko",
"license:mit",
"region:us"
] | text-to-speech | "2024-06-07T13:03:35Z" | ---
license: mit
language:
- ko
pipeline_tag: text-to-speech
--- |
hishamcse/ppo-LunarLander-v2 | hishamcse | "2024-08-12T05:21:29Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T13:08:21Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.56 +/- 47.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
Github repos(Give a star if found useful):
* https://github.com/hishamcse/DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
* https://github.com/hishamcse/Robo-Chess
Kaggle Notebook:
* https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-1-lunarlander
|
mxersion/hibou | mxersion | "2024-06-07T13:17:29Z" | 0 | 0 | transformers | [
"transformers",
"code",
"mxersion",
"chatting",
"en",
"fr",
"es",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T13:15:44Z" | ---
license: apache-2.0
language:
- en
- fr
- es
library_name: transformers
tags:
- code
- mxersion
- chatting
---
# ***UNDER CONSTRUCTION***
***THIS AI IS BEING BUILT!*** |
Eishkaran/canabis | Eishkaran | "2024-06-07T13:16:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:16:26Z" | Entry not found |
kejolong/pose | kejolong | "2024-06-07T16:09:53Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-07T13:17:02Z" | ---
license: creativeml-openrail-m
---
|
Ronal999/ORPO_PHI | Ronal999 | "2024-06-07T13:22:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:22:28Z" | Entry not found |
jihehe/llama2-7b_aihub_summary | jihehe | "2024-06-07T13:25:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:25:01Z" | Entry not found |
mirella-guenther/distil-whisper-large-v3-torgo-10-epochs | mirella-guenther | "2024-06-07T13:25:16Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T13:25:12Z" | ---
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
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mattoofahad/piper_voices | mattoofahad | "2024-06-07T13:36:09Z" | 0 | 0 | null | [
"onnx",
"region:us"
] | null | "2024-06-07T13:31:55Z" | Entry not found |
blackhole33/finetuning-sentiment-model-3000-samples | blackhole33 | "2024-06-07T13:32:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:32:14Z" | Entry not found |
a6047425318/esc64361823721t4581 | a6047425318 | "2024-06-07T13:36:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-07T13:34:50Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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a6047425318/rtc8794192836182763 | a6047425318 | "2024-06-07T13:40:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-07T13:39:27Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Milan-pez/gal-gadot | Milan-pez | "2024-06-07T13:43:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:41:06Z" | Entry not found |
ostmbh/2024-06-07-20-59-49_qwen__root_autodl-tmp_exp_PROMPT_TUNING_v1 | ostmbh | "2024-06-07T13:41:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T13:41:14Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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nihaal-mansoor/Llama3_Chat_Accurate_Final_v1_Bin | nihaal-mansoor | "2024-06-07T13:46:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T13:43:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
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whizzzzkid/sn3_ft_G_64000 | whizzzzkid | "2024-06-07T13:45:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:45:25Z" | Entry not found |
ibbb/Test4_Epoch16 | ibbb | "2024-06-07T13:47:04Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-07T13:45:50Z" | ---
license: openrail
---
|
whizzzzkid/sn3_ft_G_65000 | whizzzzkid | "2024-06-07T13:47:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T13:46:57Z" | Entry not found |
statking/paligemma_rec_lora_kalora_test_fold0_classweight_3096 | statking | "2024-06-07T17:09:21Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-07T13:54:54Z" | Entry not found |
Mirman619/RM_smoke_style_loha_v1.0 | Mirman619 | "2024-06-07T14:03:27Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-07T14:00:24Z" | ---
license: openrail
---
|
BlockArt/BlockArt | BlockArt | "2024-06-07T14:00:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:00:54Z" | Entry not found |
kaselby/test_dog | kaselby | "2024-06-07T14:14:50Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-07T14:01:05Z" | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: runwayml/stable-diffusion-v1-5
inference: true
instance_prompt: a photo of sks dog
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - kaselby/test_dog
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
TaylorJocelyn/VSGC | TaylorJocelyn | "2024-06-07T14:04:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:04:06Z" | Entry not found |
baf2b252097d46299a/medical_summarizer_a5b1b3e52a444fa8a210524f15d225d1 | baf2b252097d46299a | "2024-06-07T14:08:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:08:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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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]
## Training Details
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baf2b252097d46299a/medical_summarizer_f04b63d39aeb4831a8c5a49570ab55ef | baf2b252097d46299a | "2024-06-07T14:10:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:10:12Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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MahtaFetrat/HW5-t5model | MahtaFetrat | "2024-06-18T19:00:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | "2024-06-07T14:11:13Z" | ---
library_name: transformers
tags: []
---
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anup66/taipy_llm | anup66 | "2024-06-07T14:11:13Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-07T14:11:13Z" | ---
license: mit
---
|
vishal324/hb_info2 | vishal324 | "2024-06-07T14:11:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:11:14Z" | ---
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:** vishal324
- **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)
|
MahtaFetrat/HW5-t5tokenizer | MahtaFetrat | "2024-06-18T19:00:30Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:11:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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baf2b252097d46299a/medical_summarizer_0f9315fa1b204234aaedc8de3c98fbeb | baf2b252097d46299a | "2024-06-07T14:12:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:11:50Z" | ---
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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## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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MahtaFetrat/speecht5_tts_finetuned | MahtaFetrat | "2024-06-12T00:05:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | "2024-06-07T14:12:47Z" | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_tts_finetuned
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. -->
# speecht5_tts_finetuned
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|
temorubun/callai | temorubun | "2024-06-07T14:30:30Z" | 0 | 0 | null | [
"code",
"legal",
"text-to-speech",
"id",
"en",
"dataset:Locutusque/function-calling-chatml",
"dataset:openbmb/RLAIF-V-Dataset",
"license:apache-2.0",
"region:us"
] | text-to-speech | "2024-06-07T14:13:07Z" | ---
license: apache-2.0
datasets:
- Locutusque/function-calling-chatml
- openbmb/RLAIF-V-Dataset
language:
- id
- en
metrics:
- accuracy
- character
pipeline_tag: text-to-speech
tags:
- code
- legal
--- |
isma692/jjj | isma692 | "2024-06-07T14:13:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:13:58Z" | Entry not found |
Likalto4/sd-v2-base_OPTIMAM | Likalto4 | "2024-06-08T10:12:41Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-07T14:15:25Z" | Entry not found |
baf2b252097d46299a/disease_diagnosis_5f550955d8dd4c39a97a17877c71685c | baf2b252097d46299a | "2024-06-07T14:16:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:15:56Z" | ---
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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shuyuej/MedGemma7B-English | shuyuej | "2024-06-07T15:26:55Z" | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T14:16:37Z" | ---
license: apache-2.0
---
|
hmandsager/detr-serials | hmandsager | "2024-06-07T16:35:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | "2024-06-07T14:16:41Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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cgihlstorf/llama2-13b_32_1_0.0003_sequential_RANDOM_75pct | cgihlstorf | "2024-06-07T14:18:47Z" | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:adapter:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | "2024-06-07T14:17:55Z" | ---
library_name: peft
base_model: meta-llama/Llama-2-13b-hf
---
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.10.0 |
camilomj/youngbloodguitar | camilomj | "2024-06-07T14:19:26Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T14:18:38Z" | ---
license: apache-2.0
---
|
itsadeel/tinyllama-v1.4.0 | itsadeel | "2024-06-07T15:55:47Z" | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T14:22:33Z" | ---
license: apache-2.0
---
|
Mwchapman/SatImageGen | Mwchapman | "2024-06-08T19:47:52Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:23:06Z" | Entry not found |
GraydientPlatformAPI/loras-jun7b | GraydientPlatformAPI | "2024-06-08T04:07:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:23:30Z" | Entry not found |
PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer12_16_20-def_layer12-wikitext-39 | PhillipGuo | "2024-06-07T14:28:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:28:30Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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pbxadb/sd_turbo-models | pbxadb | "2024-06-08T07:42:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:30:44Z" | Entry not found |
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer12_16_20-def_layer12-wikitext-39 | PhillipGuo | "2024-06-07T14:31:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:31:11Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-40 | PhillipGuo | "2024-06-07T14:31:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:31:15Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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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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PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer12_16_20-def_layer12-wikitext-40 | PhillipGuo | "2024-06-07T14:31:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | "2024-06-07T14:31:30Z" | ---
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PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer12_16_20-def_layer12-wikitext-41 | PhillipGuo | "2024-06-07T14:31:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | "2024-06-07T14:31:31Z" | ---
library_name: transformers
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PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer12_16_20-def_layer12-wikitext-39 | PhillipGuo | "2024-06-07T14:31:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | "2024-06-07T14:31:47Z" | ---
library_name: transformers
tags: []
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PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer12_16_20-def_layer12-wikitext-40 | PhillipGuo | "2024-06-07T14:32:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | "2024-06-07T14:31:50Z" | ---
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tags: []
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PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer12_16_20-def_layer12-wikitext-40 | PhillipGuo | "2024-06-07T14:32:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | "2024-06-07T14:32:05Z" | ---
library_name: transformers
tags: []
---
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-39 | PhillipGuo | "2024-06-07T14:32:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:32:22Z" | ---
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]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
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fals3/peft-unit-test-generation-experiments | fals3 | "2024-08-03T05:22:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"dataset:fals3/methods2test_small",
"license:mit",
"region:us"
] | null | "2024-06-07T14:36:19Z" | ---
license: mit
datasets:
- fals3/methods2test_small
library_name: peft
---
## Training Runs
|
delly41/margot2 | delly41 | "2024-06-07T15:13:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:38:12Z" | Entry not found |
kajamo/model_15 | kajamo | "2024-06-10T12:29:16Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:adapter:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T14:38:30Z" | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
model-index:
- name: model_15
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. -->
# model_15
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7434
- eval_accuracy: 0.7127
- eval_precision: 0.7148
- eval_recall: 0.7127
- eval_f1: 0.7125
- eval_runtime: 65.7031
- eval_samples_per_second: 186.369
- eval_steps_per_second: 23.302
- epoch: 19.0
- step: 116337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1 |
quaziadib76/mistral-finetuned-alpaca | quaziadib76 | "2024-06-07T19:23:10Z" | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | "2024-06-07T14:38:53Z" | ---
license: mit
---
|
SidXXD/debug | SidXXD | "2024-06-07T14:39:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:39:06Z" | Entry not found |
nlp-models/toxic_ru_tiny_bert | nlp-models | "2024-06-07T14:40:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:40:11Z" | Entry not found |
cascadenite/q-Taxi-v3-3rd_train | cascadenite | "2024-06-07T14:59:56Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T14:41:29Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-3rd_train
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="cascadenite/q-Taxi-v3-3rd_train", 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"])
```
|
KianSh/T1_T2_Export | KianSh | "2024-06-07T14:44:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:42:49Z" | import os
import re
import csv
def extract_t1_t2_values(file_path):
with open(file_path, 'r') as file:
data = file.read()
# Define regular expressions to find the required values for Native T1
native_t1_global_pattern = r"Native T1[\s\S]*?Global Myo T1 Across Slices\s+(\d+\.?\d*)"
native_t1_slice1_pattern = r"Regional Native T1 Slice 1[\s\S]*?Myo\s+(\d+\.?\d*)"
native_t1_slice2_pattern = r"Regional Native T1 Slice 2[\s\S]*?Myo\s+(\d+\.?\d*)"
# Define regular expressions to find the required values for CA T1 (T2)
ca_t1_global_pattern = r"CA T1[\s\S]*?Global Myo T1 Across Slices\s+(\d+\.?\d*)"
ca_t1_slice1_pattern = r"Regional CA T1 Slice 1[\s\S]*?Myo\s+(\d+\.?\d*)"
ca_t1_slice2_pattern = r"Regional CA T1 Slice 2[\s\S]*?Myo\s+(\d+\.?\d*)"
# Search for the patterns in the data for Native T1
native_t1_global = re.search(native_t1_global_pattern, data)
native_t1_slice1 = re.search(native_t1_slice1_pattern, data)
native_t1_slice2 = re.search(native_t1_slice2_pattern, data)
# Search for the patterns in the data for CA T1 (T2)
ca_t1_global = re.search(ca_t1_global_pattern, data)
ca_t1_slice1 = re.search(ca_t1_slice1_pattern, data)
ca_t1_slice2 = re.search(ca_t1_slice2_pattern, data)
# Extract the values if the patterns were found for Native T1
native_t1_global_value = native_t1_global.group(1) if native_t1_global else None
native_t1_slice1_value = native_t1_slice1.group(1) if native_t1_slice1 else None
native_t1_slice2_value = native_t1_slice2.group(1) if native_t1_slice2 else None
# Extract the values if the patterns were found for CA T1 (T2)
ca_t1_global_value = ca_t1_global.group(1) if ca_t1_global else None
ca_t1_slice1_value = ca_t1_slice1.group(1) if ca_t1_slice1 else None
ca_t1_slice2_value = ca_t1_slice2.group(1) if ca_t1_slice2 else None
return {
"Native Mean Global T1": native_t1_global_value,
"Native Mean Basal T1": native_t1_slice1_value,
"Native Mean Mid T1": native_t1_slice2_value,
"CA Mean Global T2": ca_t1_global_value,
"CA Mean Basal T2": ca_t1_slice1_value,
"CA Mean Mid T2": ca_t1_slice2_value
}
def process_reports(folder_path):
report_files = [f for f in os.listdir(folder_path) if f.endswith('.txt')]
results = []
for report_file in report_files:
file_path = os.path.join(folder_path, report_file)
t1_t2_values = extract_t1_t2_values(file_path)
results.append({
"File": report_file,
"Native Mean Global T1": t1_t2_values["Native Mean Global T1"],
"Native Mean Basal T1": t1_t2_values["Native Mean Basal T1"],
"Native Mean Mid T1": t1_t2_values["Native Mean Mid T1"],
"CA Mean Global T2": t1_t2_values["CA Mean Global T2"],
"CA Mean Basal T2": t1_t2_values["CA Mean Basal T2"],
"CA Mean Mid T2": t1_t2_values["CA Mean Mid T2"]
})
return results
# Example usage
folder_path = 'report_files' # Replace with your actual folder path
results = process_reports(folder_path)
for result in results:
print(result)
# Optionally, save the results to a CSV file
csv_file_path = 'extracted_t1_t2_values.csv' # Replace with desired CSV file path
with open(csv_file_path, 'w', newline='') as csvfile:
fieldnames = [
'File',
'Native Mean Global T1', 'Native Mean Basal T1', 'Native Mean Mid T1',
'CA Mean Global T2', 'CA Mean Basal T2', 'CA Mean Mid T2'
]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for result in results:
writer.writerow(result)
|
hmandsager/detr-finetuned-balloon-v2 | hmandsager | "2024-06-07T14:43:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | "2024-06-07T14:43:17Z" | ---
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]
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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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[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
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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]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[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] |
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-41 | PhillipGuo | "2024-06-07T14:43:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:43:40Z" | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer12_16_20-def_layer12-wikitext-41 | PhillipGuo | "2024-06-07T14:45:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:45:14Z" | ---
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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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Albatu1773/opt-125m-fine-tuned-lora-model_v2 | Albatu1773 | "2024-06-07T15:34:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:45:17Z" | ---
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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mal-sh/fine_tuned_distilbert | mal-sh | "2024-06-07T14:48:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:48:44Z" | Entry not found |
decula/sd | decula | "2024-07-11T13:22:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:50:50Z" | Entry not found |
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer12_16_20-def_layer12-wikitext-41 | PhillipGuo | "2024-06-07T14:51:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:50:56Z" | ---
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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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
## Environmental Impact
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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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drgary/id | drgary | "2024-06-07T14:53:52Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:53:52Z" | Entry not found |
Kingpeach/Reinforce-CartPole-v1 | Kingpeach | "2024-06-07T14:54:22Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T14:54:12Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
thekirsila/whisper-small-hi | thekirsila | "2024-06-07T14:56:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:56:20Z" | Entry not found |
GS7776/GPT2-GREEK-v4 | GS7776 | "2024-06-24T10:51:46Z" | 0 | 0 | transformers | [
"transformers",
"el",
"dataset:wikimedia/wikipedia",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T14:56:24Z" | ---
library_name: transformers
license: apache-2.0
datasets:
- wikimedia/wikipedia
language:
- el
---
# Model Card for Model ID
Only Greek Tokenizer based on BPE
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
Can be used with any application which required Greek tokenization based on BPE for the GPT2 type of models.
### 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]
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<!-- 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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ShamuKaka/automator | ShamuKaka | "2024-06-07T14:58:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T14:58:55Z" | Entry not found |
Wacim-octo/train_lora_SDXL_GTX_1080Ti | Wacim-octo | "2024-06-07T15:01:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:01:10Z" | Entry not found |
BetterThanNothing/AutumnLux | BetterThanNothing | "2024-06-07T16:04:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:03:03Z" | *best used with dpm upscalers , steps 30-40 ,cfg >6 , clip skip 2 , sdxl vae ( best to add details about eye for the best result)*
`score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up, source_furry, woman at a grocery store shopping, 1girl, female, bonnie hopps, rabbit ears, wide hips, mammal, rabbit, anthro, mature female, purple eyes, solo, focused, (looking at object), holding object, vegetable, holding vegetable, carrot, standing, shopping, basket, store, t-shirt, jeans, two tone fur, grey fur, sun hat,`
`score_9, score_8_up, score_7_up, PDXL,
1girl, cute face, closed eyes, long blonde hair, average breasts, standing in the middle of a grassy field, hair waving in the wind, stretching BREAK mountains in the background`
`score_9, score_8_up, score_7_up, masterpiece, best quality, expressive, 1girl, kuchiki rukia, medium hair, black hair, park, textured skin, skinny, cinematic lighting, standing, cowboy shot, denim pants, hoodie, heavy blush, flustered, rukiaSDXL, looking at viewer, lipstick, sexy, mature, confident, hands in pockets`
`score_9, score_8_up, score_7_up, PDXL,
1girl, cute face, detailed eyes, furry, anthro, cat girl, sitting on a bench, white-t-shirt, black shorts, dark red toeless stockings BREAK park in the background`
`score_9,score_8_up,score_7_up,score_6_up,score_5_up,score_4_up,score_9,
imasaka yue \(memories off\),sumisora uniform,yellow hairband,black bodysuit,yellow bow,
1girl,solo,white background,upper body,looking at viewer,smile,looking at viewer,chibi,double v,red eyes`
`masterpiece, best quality, realistic, realism, analogue photo, score_9, score_8_up, score_8, score_7_up, score_7, score_6_up, score_6, score_5_up, score_5,
a girl with red hair standing in front of a sunset, an realistic drawing, fantasy art, fire lava, detailed face, burning pulse, ufotable, made of lava, yandere intricate, she has eyes of fire, dark color, explosions`
`score_9, score_8_up, score_7_up,
1girl, sabrina spellman, blonde hair, blue eyes, long hair, swept bangs, earrings, mismatched earrings, looking at viewer, squatting, solo,
tracksuit, snow, street, russia background`
`score_9, score_8_up, score_8_up, 1girl, braided_ponytail, long_hair, red_hair, green_eyes, freckles, tanlines`
`score_9, score_8_up, score_8_up, 1girl, braided_ponytail, long_hair, red_hair, green_eyes, freckles, tanlines,black sweatshirt`
`score_9, score_8_up, score_7_up, score_6_up, score_5_up, 1girl, spider gwen, waist up portrait, New York City at night, outdoors, looking at viewer, tbp, cartoon`
`score_9,score_8_up,score_7_up,8k,hd,,ruanyi0046,alley,no humans,traffic cone,power lines,industrial pipe,dark,night,dim lighting,dirty,
air conditioner,bicycle,box,brick wall,bucket,building,cardboard box,city,cityscape,door,graffiti,house,lamppost,motor vehicle,outdoors,photo background,poster \(object\),power lines,puddle,rain,road,scenery,stairs,street,trash bag,trash can,wall,window,`
`score_9,score_8_up,score_7_up,1girl, green shirt, collared shirt, white pant, stylish outfit, suspenders, happy, dynamic, colorful, white hair, long hair, green eyes, blue sky, clouds, flowers, trees, buildings, cowboy shot, looking at viewer, sketch, pop art, masterpiece, best quality, very aesthetic, absurdres`
`score_9,score_8_up,score_7_up,masterpiece, best quality, satyrcpt, satyr, horns, goat horns, satyr girl, animal ears, legfur, hooves, white fur, great forest scenario`
`score_9,score_8_up,score_7_up,brown gloves, superhero, water, purple cape, day, muscular, upper body, cape, green skin, bodysuit,1boy , closed mouth, smile, green hair`
`score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up,Male, black hair,vibrant color, black jacket, open jacket, ear piercing, red eyes, city, day`
`score_9, score_8_up, score_7_up, source_cartoon BREAK 1girl,, POV, kim possible, orange hair, sunglasses, fedora, fake mustache, medium breasts, perky breasts, overcoat, park night, outdoors, hetero, disguise, secret agent, kimberly ann possible, hat, trench coat, spy, looking at viewer, cowboy, shot, close up of ID card, rating_safe`
`(score_9, score_8, score_7, score_6,) Humanized innocent sheep, Humanized, white curls, sheep's horns and ears, sheep's pupils, a 20 yo girl, 1990s \(style\), vintage, solo, long hair, flower,`
`score_9, score_8_up, score_7_up, source_anime, cowboy shot, looking at viewer, smile, medium hair, beanie, scarf, winter coat, jeans, reaching towards viewer, outdoors, night, skyline`
`score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up, source_anime, upper body, 1girl, velma, off shoulder, glasses, brastrap, night, depth of field, bokeh`
Images:
|
ernestknurov/ppo-LunarLander-v2 | ernestknurov | "2024-06-07T15:04:36Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T15:04:17Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.86 +/- 22.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cascadenite/q-Taxi-v3-zero_Q | cascadenite | "2024-06-07T15:05:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:05:41Z" | Entry not found |
Carlosslocar/exp17 | Carlosslocar | "2024-06-07T15:05:58Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-07T15:05:49Z" | Entry not found |
KalidasVijayBhak/output | KalidasVijayBhak | "2024-06-07T15:06:30Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | null | "2024-06-07T15:06:28Z" | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: PY007/TinyLlama-1.1B-Chat-v0.3
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1 |
xMaulana/QLoRA-Psychika | xMaulana | "2024-06-07T15:07:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T15:07:11Z" | ---
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] |
Peaceofmind01/P_M | Peaceofmind01 | "2024-06-07T15:09:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:09:35Z" | Entry not found |
Jhandry123/Evaluacion | Jhandry123 | "2024-06-07T15:14:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:14:05Z" | Entry not found |
Mriganka1999/PandaReachDense-v3usingDDPG | Mriganka1999 | "2024-06-07T15:14:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-07T15:14:56Z" | Entry not found |
debenoist/qlora_model_4 | debenoist | "2024-06-07T15:15:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-07T15:15:37Z" | ---
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:** debenoist
- **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)
|
Mriganka1999/PandaReachDense-v3usingDDPG-PandaReachDense-v3 | Mriganka1999 | "2024-06-07T15:21:10Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T15:15:52Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.20 +/- 0.07
name: mean_reward
verified: false
---
# **DDPG** Agent playing **PandaReachDense-v3**
This is a trained model of a **DDPG** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Mortello/ppo-LunarLander-v2 | Mortello | "2024-06-07T15:17:25Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-07T15:17:07Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.41 +/- 19.87
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|