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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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11.7k
| library_name
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TIGER-Lab/MAmmoTH-7B
|
TIGER-Lab
| 2023-12-05T16:34:56Z | 199 | 8 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:TIGER-Lab/MathInstruct",
"arxiv:2309.05653",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-10T20:51:23Z |
---
license: mit
datasets:
- TIGER-Lab/MathInstruct
language:
- en
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/)
Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Introduction
We introduce 🦣 MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
| | **Base Model: Llama-2** | **Base Model: Code Llama** | **Base Model: Mistral** |
|-----|---------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------|
| 7B | 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) | 🦣 [MAmmoTH-7B-Mistral](https://huggingface.co/TIGER-Lab/MAmmoTH-7B-Mistral) |
| 13B | 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)| - |
| 34B | - | 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)| - |
| 70B | 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) | - | - |
## Training Data
The models are trained on the 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), which is compiled from 13 different math rationale datasets. Check out the dataset card for more details.
## Training Procedure
The models are fine-tuned with the MathInstruct dataset using the original Llama-2 and Code Llama models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **Decoding** | **GSM** | **MATH** | **AQuA** | **NumG** | **SVA** | **Mat** | **Sim** | **SAT** | **MMLU** | **AVG** |
|-----------------------|--------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
| **MAmmoTH-7B** | CoT | 50.5 | 10.4 | 43.7 | 44.0 | 47.3 | 9.2 | 18.9 | 32.7 | 39.9 | 33.0 |
| | PoT | 51.6 | 28.7 | 43.3 | 52.3 | 65.1 | 41.9 | 48.2 | 39.1 | 44.6 | 46.1 |
| | **Hybrid** | **53.6** | **31.5** | **44.5** | **61.2** | **67.7** | **46.3** | **41.2** | **42.7** | **42.6** | **47.9** |
| **MAmmoTH-Coder-7B** | CoT | 22.4 | 7.9 | 36.2 | 36.0 | 37.0 | 8.2 | 7.2 | 32.7 | 34.6 | 24.7 |
| | PoT | 58.8 | 32.1 | 47.2 | 57.1 | 71.1 | 53.9 | 44.6 | 40.0 | 47.8 | 50.3 |
| | **Hybrid** | **59.4** | **33.4** | **47.2** | **66.4** | **71.4** | **55.4** | **45.9** | **40.5** | **48.3** | **52.0** |
| **MAmmoTH-13B** | CoT | 56.3 | 12.9 | 45.3 | 45.6 | 53.8 | 11.7 | 22.4 | 43.6 | 42.3 | 37.1 |
| | PoT | 61.3 | 32.6 | 48.8 | 59.6 | 72.2 | 48.5 | 40.3 | 46.8 | 45.4 | 50.6 |
| | **Hybrid** | **62.0** | **34.2** | **51.6** | **68.7** | **72.4** | **49.2** | **43.2** | **46.8** | **47.6** | **52.9** |
| **MAmmoTH-Coder-13B** | CoT | 32.1 | 10.2 | 40.6 | 36.2 | 43.0 | 9.6 | 10.1 | 40.9 | 36.6 | 28.8 |
| | PoT | 64.3 | 35.2 | 46.8 | 54.2 | 73.2 | 60.0 | 44.2 | 48.2 | 48.2 | 52.7 |
| | **Hybrid** | **64.7** | **36.3** | **46.9** | **66.8** | **73.7** | **61.5** | **47.1** | **48.6** | **48.3** | **54.9** |
| **MAmmoTH-Coder-33B** | CoT | 34.3 | 11.6 | 39.0 | 36.2 | 44.6 | 10.8 | 10.9 | 46.4 | 42.9 | 30.7 |
| | PoT | 72.3 | 42.8 | 53.8 | 59.6 | 84.0 | 64.7 | 50.6 | 58.6 | 52.7 | 59.9 |
| | **Hybrid** | **72.7** | **43.6** | **54.7** | **71.6** | **84.3** | **65.4** | **51.8** | **60.9** | **53.8** | **62.1** |
| **MAmmoTH-70B** | CoT | 72.4 | 21.1 | 57.9 | 58.9 | 71.6 | 20.0 | 31.9 | 57.3 | 52.1 | 49.2 |
| | PoT | 76.7 | 40.1 | 60.2 | 64.3 | 81.7 | 55.3 | 45.3 | 64.1 | 53.5 | 60.1 |
| | **Hybrid** | **76.9** | **41.8** | **65.0** | **74.4** | **82.4** | **55.6** | **51.4** | **66.4** | **56.7** | **63.4** |
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Prompt Format
If you want to do CoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
If you want to do PoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction} Let's write a program.
### Response:
```
## Intended Uses
These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2023mammoth,
title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning},
author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen},
journal={arXiv preprint arXiv:2309.05653},
year={2023}
}
```
|
Tatvajsh/dpo_AHS_OPS_WPCS_v4.0
|
Tatvajsh
| 2023-12-05T16:25:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"base_model:adapter:openlm-research/open_llama_3b_v2",
"region:us"
] | null | 2023-12-01T00:22:48Z |
---
library_name: peft
base_model: openlm-research/open_llama_3b_v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2
|
teddyschoenfeld/finetuning-sentiment-model-10000-samples
|
teddyschoenfeld
| 2023-12-05T16:25:29Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-05T00:24:12Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-10000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9125
- name: F1
type: f1
value: 0.9145090376160234
---
<!-- 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. -->
# finetuning-sentiment-model-10000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2359
- Accuracy: 0.9125
- F1: 0.9145
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
elonmollusk/i3-neural-mistral-000
|
elonmollusk
| 2023-12-05T16:24:57Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Intel/neural-chat-7b-v3-1",
"base_model:adapter:Intel/neural-chat-7b-v3-1",
"region:us"
] | null | 2023-12-05T16:21:50Z |
---
library_name: peft
base_model: Intel/neural-chat-7b-v3-1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
mike-krk/dqn-SpaceInvadersNoFrameskip-v4
|
mike-krk
| 2023-12-05T16:20:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-05T16:19:45Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 596.00 +/- 88.23
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mike-krk -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mike-krk -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mike-krk
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Nerdofdot/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2_epochs_1_margin_0.4_trained_model
|
Nerdofdot
| 2023-12-05T16:15:20Z | 15 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-05T16:14:33Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 497 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 0.4}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 49,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Anwaarma/TestForColab
|
Anwaarma
| 2023-12-05T16:11:29Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:prajjwal1/bert-tiny",
"base_model:finetune:prajjwal1/bert-tiny",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-05T16:10:14Z |
---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: TestForColab
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. -->
# TestForColab
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2129
- Accuracy: 0.94
- F1: 0.9394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.01 | 50 | 0.6913 | 0.55 | 0.3903 |
| No log | 0.02 | 100 | 0.6909 | 0.59 | 0.5186 |
| No log | 0.03 | 150 | 0.6934 | 0.45 | 0.2793 |
| No log | 0.04 | 200 | 0.6889 | 0.57 | 0.5709 |
| No log | 0.05 | 250 | 0.6818 | 0.56 | 0.5607 |
| No log | 0.06 | 300 | 0.6854 | 0.56 | 0.5607 |
| No log | 0.07 | 350 | 0.6878 | 0.56 | 0.5607 |
| No log | 0.08 | 400 | 0.7014 | 0.56 | 0.5607 |
| No log | 0.09 | 450 | 0.6797 | 0.56 | 0.5607 |
| 0.6799 | 0.1 | 500 | 0.6731 | 0.56 | 0.5607 |
| 0.6799 | 0.11 | 550 | 0.6490 | 0.64 | 0.6203 |
| 0.6799 | 0.12 | 600 | 0.6456 | 0.71 | 0.7049 |
| 0.6799 | 0.13 | 650 | 0.6259 | 0.64 | 0.6203 |
| 0.6799 | 0.14 | 700 | 0.5264 | 0.83 | 0.8304 |
| 0.6799 | 0.15 | 750 | 0.4671 | 0.83 | 0.8304 |
| 0.6799 | 0.16 | 800 | 0.3387 | 0.94 | 0.9394 |
| 0.6799 | 0.17 | 850 | 0.2935 | 0.94 | 0.9394 |
| 0.6799 | 0.18 | 900 | 0.2604 | 0.94 | 0.9394 |
| 0.6799 | 0.19 | 950 | 0.2443 | 0.94 | 0.9394 |
| 0.4884 | 0.2 | 1000 | 0.2355 | 0.94 | 0.9394 |
| 0.4884 | 0.2 | 1050 | 0.2286 | 0.94 | 0.9394 |
| 0.4884 | 0.21 | 1100 | 0.2240 | 0.94 | 0.9394 |
| 0.4884 | 0.22 | 1150 | 0.2201 | 0.94 | 0.9394 |
| 0.4884 | 0.23 | 1200 | 0.2165 | 0.94 | 0.9394 |
| 0.4884 | 0.24 | 1250 | 0.2129 | 0.94 | 0.9394 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2-new3
|
pavitemple
| 2023-12-05T16:04:11Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-05T14:20:13Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-Accident-MultipleLabels-Video-subset-v2-new3
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. -->
# finetuned-Accident-MultipleLabels-Video-subset-v2-new3
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7434
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.06 | 2 | 1.9011 | 0.1719 |
| No log | 1.06 | 4 | 1.8469 | 0.3281 |
| No log | 2.06 | 6 | 1.8533 | 0.3281 |
| No log | 3.06 | 8 | 1.9122 | 0.3281 |
| 1.6429 | 4.06 | 10 | 1.9870 | 0.3125 |
| 1.6429 | 5.06 | 12 | 1.9921 | 0.2969 |
| 1.6429 | 6.06 | 14 | 1.9683 | 0.3125 |
| 1.6429 | 7.06 | 16 | 1.9313 | 0.3125 |
| 1.6429 | 8.06 | 18 | 1.9203 | 0.3125 |
| 1.4144 | 9.06 | 20 | 1.9251 | 0.3125 |
| 1.4144 | 10.06 | 22 | 1.9316 | 0.2969 |
| 1.4144 | 11.06 | 24 | 1.9455 | 0.2969 |
| 1.4144 | 12.06 | 26 | 1.9723 | 0.2969 |
| 1.4144 | 13.06 | 28 | 1.9897 | 0.2969 |
| 1.3307 | 14.06 | 30 | 2.0000 | 0.2969 |
| 1.3307 | 15.06 | 32 | 2.0063 | 0.2969 |
| 1.3307 | 16.06 | 34 | 2.0072 | 0.2969 |
| 1.3307 | 17.03 | 35 | 2.0082 | 0.2969 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231202+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TIGER-Lab/MAmmoTH-Coder-34B
|
TIGER-Lab
| 2023-12-05T16:03:18Z | 21 | 7 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:TIGER-Lab/MathInstruct",
"arxiv:2309.05653",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-11T02:55:09Z |
---
license: mit
datasets:
- TIGER-Lab/MathInstruct
language:
- en
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/)
Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Introduction
We introduce 🦣 MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
| | **Base Model: Llama-2** | **Base Model: Code Llama** |
|-----|---------------------------------------------------------------|--------------------------------------------------------------------------|
| 7B | 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) |
| 13B | 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)|
| 34B | - | 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)|
| 70B | 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) | - |
## Training Data
The models are trained on the 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), which is compiled from 13 different math rationale datasets. Check out the dataset card for more details.
## Training Procedure
The models are fine-tuned with the MathInstruct dataset using the original Llama-2 and Code Llama models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **Decoding** | **GSM** | **MATH** | **AQuA** | **NumG** | **SVA** | **Mat** | **Sim** | **SAT** | **MMLU** | **AVG** |
|-----------------------|--------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
| **MAmmoTH-7B** | CoT | 50.5 | 10.4 | 43.7 | 44.0 | 47.3 | 9.2 | 18.9 | 32.7 | 39.9 | 33.0 |
| | PoT | 51.6 | 28.7 | 43.3 | 52.3 | 65.1 | 41.9 | 48.2 | 39.1 | 44.6 | 46.1 |
| | **Hybrid** | **53.6** | **31.5** | **44.5** | **61.2** | **67.7** | **46.3** | **41.2** | **42.7** | **42.6** | **47.9** |
| **MAmmoTH-Coder-7B** | CoT | 22.4 | 7.9 | 36.2 | 36.0 | 37.0 | 8.2 | 7.2 | 32.7 | 34.6 | 24.7 |
| | PoT | 58.8 | 32.1 | 47.2 | 57.1 | 71.1 | 53.9 | 44.6 | 40.0 | 47.8 | 50.3 |
| | **Hybrid** | **59.4** | **33.4** | **47.2** | **66.4** | **71.4** | **55.4** | **45.9** | **40.5** | **48.3** | **52.0** |
| **MAmmoTH-13B** | CoT | 56.3 | 12.9 | 45.3 | 45.6 | 53.8 | 11.7 | 22.4 | 43.6 | 42.3 | 37.1 |
| | PoT | 61.3 | 32.6 | 48.8 | 59.6 | 72.2 | 48.5 | 40.3 | 46.8 | 45.4 | 50.6 |
| | **Hybrid** | **62.0** | **34.2** | **51.6** | **68.7** | **72.4** | **49.2** | **43.2** | **46.8** | **47.6** | **52.9** |
| **MAmmoTH-Coder-13B** | CoT | 32.1 | 10.2 | 40.6 | 36.2 | 43.0 | 9.6 | 10.1 | 40.9 | 36.6 | 28.8 |
| | PoT | 64.3 | 35.2 | 46.8 | 54.2 | 73.2 | 60.0 | 44.2 | 48.2 | 48.2 | 52.7 |
| | **Hybrid** | **64.7** | **36.3** | **46.9** | **66.8** | **73.7** | **61.5** | **47.1** | **48.6** | **48.3** | **54.9** |
| **MAmmoTH-Coder-33B** | CoT | 34.3 | 11.6 | 39.0 | 36.2 | 44.6 | 10.8 | 10.9 | 46.4 | 42.9 | 30.7 |
| | PoT | 72.3 | 42.8 | 53.8 | 59.6 | 84.0 | 64.7 | 50.6 | 58.6 | 52.7 | 59.9 |
| | **Hybrid** | **72.7** | **43.6** | **54.7** | **71.6** | **84.3** | **65.4** | **51.8** | **60.9** | **53.8** | **62.1** |
| **MAmmoTH-70B** | CoT | 72.4 | 21.1 | 57.9 | 58.9 | 71.6 | 20.0 | 31.9 | 57.3 | 52.1 | 49.2 |
| | PoT | 76.7 | 40.1 | 60.2 | 64.3 | 81.7 | 55.3 | 45.3 | 64.1 | 53.5 | 60.1 |
| | **Hybrid** | **76.9** | **41.8** | **65.0** | **74.4** | **82.4** | **55.6** | **51.4** | **66.4** | **56.7** | **63.4** |
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Prompt Format
If you want to do CoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
If you want to do PoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction} Let's write a program.
### Response:
```
## Intended Uses
These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2023mammoth,
title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning},
author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen},
journal={arXiv preprint arXiv:2309.05653},
year={2023}
}
```
|
TIGER-Lab/MAmmoTH-Coder-13B
|
TIGER-Lab
| 2023-12-05T16:03:03Z | 61 | 8 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:TIGER-Lab/MathInstruct",
"arxiv:2309.05653",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-11T18:44:26Z |
---
license: mit
datasets:
- TIGER-Lab/MathInstruct
language:
- en
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/)
Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Introduction
We introduce 🦣 MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
| | **Base Model: Llama-2** | **Base Model: Code Llama** |
|-----|---------------------------------------------------------------|--------------------------------------------------------------------------|
| 7B | 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) |
| 13B | 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)|
| 34B | - | 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)|
| 70B | 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) | - |
## Training Data
The models are trained on the 🤗 [MathInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), which is compiled from 13 different math rationale datasets. Check out the dataset card for more details.
## Training Procedure
The models are fine-tuned with the MathInstruct dataset using the original Llama-2 and Code Llama models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **Decoding** | **GSM** | **MATH** | **AQuA** | **NumG** | **SVA** | **Mat** | **Sim** | **SAT** | **MMLU** | **AVG** |
|-----------------------|--------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
| **MAmmoTH-7B** | CoT | 50.5 | 10.4 | 43.7 | 44.0 | 47.3 | 9.2 | 18.9 | 32.7 | 39.9 | 33.0 |
| | PoT | 51.6 | 28.7 | 43.3 | 52.3 | 65.1 | 41.9 | 48.2 | 39.1 | 44.6 | 46.1 |
| | **Hybrid** | **53.6** | **31.5** | **44.5** | **61.2** | **67.7** | **46.3** | **41.2** | **42.7** | **42.6** | **47.9** |
| **MAmmoTH-Coder-7B** | CoT | 22.4 | 7.9 | 36.2 | 36.0 | 37.0 | 8.2 | 7.2 | 32.7 | 34.6 | 24.7 |
| | PoT | 58.8 | 32.1 | 47.2 | 57.1 | 71.1 | 53.9 | 44.6 | 40.0 | 47.8 | 50.3 |
| | **Hybrid** | **59.4** | **33.4** | **47.2** | **66.4** | **71.4** | **55.4** | **45.9** | **40.5** | **48.3** | **52.0** |
| **MAmmoTH-13B** | CoT | 56.3 | 12.9 | 45.3 | 45.6 | 53.8 | 11.7 | 22.4 | 43.6 | 42.3 | 37.1 |
| | PoT | 61.3 | 32.6 | 48.8 | 59.6 | 72.2 | 48.5 | 40.3 | 46.8 | 45.4 | 50.6 |
| | **Hybrid** | **62.0** | **34.2** | **51.6** | **68.7** | **72.4** | **49.2** | **43.2** | **46.8** | **47.6** | **52.9** |
| **MAmmoTH-Coder-13B** | CoT | 32.1 | 10.2 | 40.6 | 36.2 | 43.0 | 9.6 | 10.1 | 40.9 | 36.6 | 28.8 |
| | PoT | 64.3 | 35.2 | 46.8 | 54.2 | 73.2 | 60.0 | 44.2 | 48.2 | 48.2 | 52.7 |
| | **Hybrid** | **64.7** | **36.3** | **46.9** | **66.8** | **73.7** | **61.5** | **47.1** | **48.6** | **48.3** | **54.9** |
| **MAmmoTH-Coder-33B** | CoT | 34.3 | 11.6 | 39.0 | 36.2 | 44.6 | 10.8 | 10.9 | 46.4 | 42.9 | 30.7 |
| | PoT | 72.3 | 42.8 | 53.8 | 59.6 | 84.0 | 64.7 | 50.6 | 58.6 | 52.7 | 59.9 |
| | **Hybrid** | **72.7** | **43.6** | **54.7** | **71.6** | **84.3** | **65.4** | **51.8** | **60.9** | **53.8** | **62.1** |
| **MAmmoTH-70B** | CoT | 72.4 | 21.1 | 57.9 | 58.9 | 71.6 | 20.0 | 31.9 | 57.3 | 52.1 | 49.2 |
| | PoT | 76.7 | 40.1 | 60.2 | 64.3 | 81.7 | 55.3 | 45.3 | 64.1 | 53.5 | 60.1 |
| | **Hybrid** | **76.9** | **41.8** | **65.0** | **74.4** | **82.4** | **55.6** | **51.4** | **66.4** | **56.7** | **63.4** |
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
## Prompt Format
If you want to do CoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
```
If you want to do PoT:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction} Let's write a program.
### Response:
```
## Intended Uses
These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2023mammoth,
title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning},
author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen},
journal={arXiv preprint arXiv:2309.05653},
year={2023}
}
```
|
DianaJin/krmodel
|
DianaJin
| 2023-12-05T16:00:38Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:DianaJin/krmodel",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-05T13:11:38Z |
---
language:
- ko
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- DianaJin/krmodel
model-index:
- name: jinkrsmodel
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. -->
# jinkrsmodel
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the DianaJin/krmodel dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1290
- Cer: 92.0690
## 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: 20
- training_steps: 160
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1899 | 13.33 | 40 | 1.0819 | 153.1034 |
| 0.0027 | 26.67 | 80 | 1.0554 | 24.8276 |
| 0.0006 | 40.0 | 120 | 1.1173 | 39.3103 |
| 0.0004 | 53.33 | 160 | 1.1290 | 92.0690 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
mogaio/pr_ebsa_en_merged25_offsets
|
mogaio
| 2023-12-05T16:00:02Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-12-05T15:59:07Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# mogaio/pr_ebsa_en_merged25_offsets
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_en_merged25_offsets")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
sgimmel/gpt-neo-125m-finetuned-cummings-multiline
|
sgimmel
| 2023-12-05T15:58:41Z | 18 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-26T18:34:58Z |
---
license: mit
base_model: EleutherAI/gpt-neo-125m
tags:
- generated_from_trainer
model-index:
- name: gpt-neo-125m-finetuned-cummings-multiline
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-125m-finetuned-cummings-multiline
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 191 | 4.5170 |
| No log | 2.0 | 382 | 4.4383 |
| 4.4042 | 3.0 | 573 | 4.4100 |
| 4.4042 | 4.0 | 764 | 4.3972 |
| 4.4042 | 5.0 | 955 | 4.3934 |
| 4.0308 | 6.0 | 1146 | 4.3958 |
| 4.0308 | 7.0 | 1337 | 4.3962 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
Hypersniper/The_OverThinker_Phi_1_5
|
Hypersniper
| 2023-12-05T15:51:13Z | 22 | 7 |
transformers
|
[
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"fun",
"riddles",
"phi1.5",
"phi 1.5",
"alpaca",
"custom_code",
"dataset:Hypersniper/riddles_v1",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-01T15:56:20Z |
---
datasets:
- Hypersniper/riddles_v1
tags:
- fun
- riddles
- phi1.5
- phi 1.5
- alpaca
library_name: transformers
---
# Welcome to The OverThinker's Repository!
>The OverThinker working late at night trying to find that answer for you.
[Buy me Ko-fi](https://ko-fi.com/hypersniper)
Meet The OverThinker! While some might argue he digs way too deep, many regard him as a genius. This repository is where his complex thoughts and innovative solutions come to life.
## Model Phi 1.5
The OverThinker operates on the small Model Phi 1.5, uniquely tailored to offer intriguing perspectives:
- **Specialized in Riddles**: Overfitted, by design, on a comprehensive Riddles database. Mr OverThinker is not just another bot – he's an enigma wrapped in a mystery.
- **Built on the Alpaca Template**
```python
template = ("Below is an instruction that describes a task, paired with an input "
"that provides further context. Write a response that appropriately "
"completes the request.\n\n### Instruction:\n{query}\n\n### Response:\n")
# Example of using the template with a query
query = "What gets wetter as it dries?"
formatted_string = template.format(query=query)
```
## How to Interact with Mr OverThinker
Here is how you can get started:
<details>
<summary><b>How to Interact with OverThinker using Web Generation WebUI</b> (click to expand)</summary>
- Install text generation webui [Text Generation WebUI](https://github.com/oobabooga/text-generation-webui).
- On the 'Model' tab enter this URL `Hypersniper/The_OverThinker_Phi_1_5` to automatically download the model.
- On the same tab, select the model, make sure `trust-remote-code` is checked, then `Load` the model.
- Next, on the `Parameters` table, select `Instruction Template` and make sure the `Alpaca` template is selected.
- Lastly, under `Mode`, select `Chat` and then `Instruct`. Now you are ready to chat with The OverThinker. Enjoy!
</details>
The OverThinker awaits your challenging riddles and is ready to provide you with wild answers. Let the journey begin!
## Conversation Examples
|
superaidesu/cantonese-alma-2-7b-oasst-v1-lora
|
superaidesu
| 2023-12-05T15:46:58Z | 0 | 0 | null |
[
"zh",
"license:llama2",
"region:us"
] | null | 2023-11-23T01:33:54Z |
---
license: llama2
language:
- zh
metrics:
- bleu
- chrf
---
Base model: https://huggingface.co/indiejoseph/cantonese-llama-2-7b-oasst-v1
Finetuned following ALMA (https://github.com/fe1ixxu/ALMA) on the Cantonese-Mandarin translation task.
Finetuning dataset: Sourced from the released raw dataset in https://github.com/meganndare/cantonese-nlp
As the base model was already finetuned on Cantonese monolingual data, we only conducted finetuning on parallel sentences.
Results:
Man -> Can: 35.371 BLEU, 26.197 ChrF++
Can -> Man: 36.553 BLEU, 27.471 ChrF++
Github Repo: https://github.com/cmgao/nlp_project
The ALMA code was linked as submodule.
|
shivangx01b/phi-1_5-finetuned-science-qa
|
shivangx01b
| 2023-12-05T15:46:51Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T15:35:15Z |
---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-science-qa
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. -->
# phi-1_5-finetuned-science-qa
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
AyoubChLin/Albert-bbc-news
|
AyoubChLin
| 2023-12-05T15:30:01Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"albert",
"text-classification",
"autotrain",
"en",
"dataset:AyoubChLin/autotrain-data-albert-bbc-news",
"dataset:SetFit/bbc-news",
"license:apache-2.0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-04-12T19:00:44Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: I love AutoTrain 🤗
datasets:
- AyoubChLin/autotrain-data-albert-bbc-news
- SetFit/bbc-news
co2_eq_emissions:
emissions: 13.344689233410659
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-classification
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 48939118438
- CO2 Emissions (in grams): 13.3447
## Validation Metrics
- Loss: 0.103
- Accuracy: 0.978
- Macro F1: 0.978
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.977
- Micro Precision: 0.978
- Weighted Precision: 0.978
- Macro Recall: 0.978
- Micro Recall: 0.978
- Weighted Recall: 0.978
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/AyoubChLin/autotrain-albert-bbc-news-48939118438
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/autotrain-albert-bbc-news-48939118438", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-albert-bbc-news-48939118438", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
MAPS-research/Chaos3.0
|
MAPS-research
| 2023-12-05T15:29:57Z | 5 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-05T14:55:42Z |
---
pipeline_tag: text-to-image
license: creativeml-openrail-m
---
# Chaos3.0
This is the converted checkpoint of Chaos3.0 for diffusers pipeline.
Original Model: [Link](https://civitai.com/models/91534/chaos30)
|
fatcatmilo/test
|
fatcatmilo
| 2023-12-05T15:21:14Z | 1 | 0 |
peft
|
[
"peft",
"pytorch",
"safetensors",
"bloom",
"arxiv:1910.09700",
"base_model:bigscience/bloom-3b",
"base_model:adapter:bigscience/bloom-3b",
"region:us"
] | null | 2023-08-30T10:07:21Z |
---
library_name: peft
base_model: bigscience/bloom-3b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.3.dev0
|
abdulmatinomotoso/distilroberta-topic-classification_4
|
abdulmatinomotoso
| 2023-12-05T15:15:13Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-30T20:39:51Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-topic-classification_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-topic-classification_4
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5428
- Acc: 0.7557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6261 | 1.0 | 564 | 3.6045 | 0.6610 |
| 3.5223 | 2.0 | 1128 | 3.5326 | 0.7111 |
| 3.4204 | 3.0 | 1692 | 3.5040 | 0.7322 |
| 3.3446 | 4.0 | 2256 | 3.4980 | 0.7400 |
| 3.2737 | 5.0 | 2820 | 3.4872 | 0.7539 |
| 3.2533 | 6.0 | 3384 | 3.4967 | 0.7555 |
| 3.2059 | 7.0 | 3948 | 3.5038 | 0.7613 |
| 3.1697 | 8.0 | 4512 | 3.5258 | 0.7537 |
| 3.1439 | 9.0 | 5076 | 3.5311 | 0.7573 |
| 3.1426 | 10.0 | 5640 | 3.5334 | 0.7544 |
| 3.1325 | 11.0 | 6204 | 3.5311 | 0.7562 |
| 3.1165 | 12.0 | 6768 | 3.5428 | 0.7557 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
matansol/a2c-PandaReachDense-v3
|
matansol
| 2023-12-05T15:08:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-05T15:04:00Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
qqplot23/xsum-gpt2-long-pegasus
|
qqplot23
| 2023-12-05T14:54:34Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T07:31:22Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: xsum-gpt2-long-pegasus
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. -->
# xsum-gpt2-long-pegasus
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2524
- Ppl: 26.6834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 22554
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ppl |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 3.7921 | 2.67 | 4000 | 3.6382 | 39.1940 |
| 3.4486 | 5.34 | 8000 | 3.4164 | 31.3953 |
| 3.299 | 8.01 | 12000 | 3.3291 | 28.7823 |
| 3.2019 | 10.68 | 16000 | 3.2769 | 27.3369 |
| 3.1403 | 13.36 | 20000 | 3.2524 | 26.6834 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
malhajar/Llama-2-13b-chat-tr
|
malhajar
| 2023-12-05T14:51:10Z | 13 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"tr",
"dataset:atasoglu/databricks-dolly-15k-tr",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T14:38:04Z |
---
datasets:
- atasoglu/databricks-dolly-15k-tr
language:
- tr
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
malhajar/Llama-2-13b-chat-dolly-tr is a finetuned version of Llama-2-13b-hf using SFT Training.
This model can answer information in turkish language as it is finetuned on a turkish dataset specifically [`databricks-dolly-15k-tr`]( https://huggingface.co/datasets/atasoglu/databricks-dolly-15k-tr)
### Model Description
- **Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
- **Language(s) (NLP):** Turkish
- **Finetuned from model:** [`meta-llama/Llama-2-13b-hf`](https://huggingface.co/meta-llama/Llama-2-13b-hf)
### Prompt Template
```
<s>[INST] <prompt> [/INST]
```
## How to Get Started with the Model
Use the code sample provided in the original post to interact with the model.
```python
from transformers import AutoTokenizer,AutoModelForCausalLM
model_id = "malhajar/Llama-2-7b-chat-dolly-tr"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
torch_dtype=torch.float16,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_id)
question: "Türkiyenin en büyük şehir nedir?"
# For generating a response
prompt = '''
<s>[INST] {question} [/INST]
'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,repetition_penalty=1.3
top_p=0.95)
response = tokenizer.decode(output[0])
print(response)
```
|
sosophiphiaa/nabi
|
sosophiphiaa
| 2023-12-05T14:50:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail",
"region:us"
] |
text-to-image
| 2023-12-05T14:09:47Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/2c22c20c4fba91f53834e11c20373a69.jpg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: openrail
---
# nabi voice
<Gallery />
## Download model
[Download](/sosophiphiaa/nabi/tree/main) them in the Files & versions tab.
|
Pranavsenthilvel/t5-small-finetuned-xsum-2
|
Pranavsenthilvel
| 2023-12-05T14:38:09Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-05T14:18:12Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0545
- Rouge1: 97.619
- Rouge2: 0.0
- Rougel: 97.619
- Rougelsum: 97.619
- Gen Len: 2.0
# Notes
- Try the following:
- What is your name?
- Name:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 47 | 2.3226 | 41.1659 | 0.0 | 41.5533 | 40.7833 | 3.7857 |
| No log | 2.0 | 94 | 0.9654 | 71.4286 | 0.0 | 70.6349 | 70.6349 | 2.0476 |
| No log | 3.0 | 141 | 0.5835 | 80.9524 | 0.0 | 80.9524 | 80.9524 | 2.0 |
| No log | 4.0 | 188 | 0.4075 | 80.9524 | 0.0 | 80.9524 | 80.9524 | 2.0238 |
| No log | 5.0 | 235 | 0.2701 | 83.3333 | 0.0 | 83.3333 | 83.3333 | 2.0 |
| No log | 6.0 | 282 | 0.1677 | 88.0952 | 0.0 | 88.0952 | 88.0952 | 1.9762 |
| No log | 7.0 | 329 | 0.1074 | 92.8571 | 0.0 | 92.8571 | 92.8571 | 2.0 |
| No log | 8.0 | 376 | 0.0745 | 97.619 | 0.0 | 97.619 | 97.619 | 2.0 |
| No log | 9.0 | 423 | 0.0583 | 97.619 | 0.0 | 97.619 | 97.619 | 2.0 |
| No log | 10.0 | 470 | 0.0545 | 97.619 | 0.0 | 97.619 | 97.619 | 2.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Meyveli1Lahmacun/atam18
|
Meyveli1Lahmacun
| 2023-12-05T14:29:40Z | 1 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-05T14:25:38Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### atam18 Dreambooth model trained by Meyveli1Lahmacun with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
aleksejalex/gpt2_kytice
|
aleksejalex
| 2023-12-05T14:26:24Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T14:26:03Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2_kytice
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. -->
# gpt2_kytice
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 5e-05
- train_batch_size: 15
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 28
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
nlile/PE-12b-pythia
|
nlile
| 2023-12-05T14:22:29Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:EleutherAI/pythia-12b-deduped",
"base_model:finetune:EleutherAI/pythia-12b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-04T06:18:00Z |
---
license: apache-2.0
base_model: EleutherAI/pythia-12b-deduped
tags:
- generated_from_trainer
model-index:
- name: PE-12b-pythia
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. -->
# PE-12b-pythia
This model is a fine-tuned version of [EleutherAI/pythia-12b-deduped](https://huggingface.co/EleutherAI/pythia-12b-deduped) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Rewards/chosen: 3.5045
- Rewards/rejected: -2.3171
- Rewards/accuracies: 0.9441
- Rewards/margins: 5.8216
- Logps/rejected: -95.5639
- Logps/chosen: -116.1507
- Logits/rejected: -0.4604
- Logits/chosen: -0.4355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-07
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.8825 | 0.05 | 100 | 0.8872 | 0.1884 | 0.1204 | 0.5056 | 0.0680 | -90.6889 | -122.7830 | -0.5017 | -0.4522 |
| 0.9136 | 0.09 | 200 | 0.8325 | 0.3253 | 0.0714 | 0.5894 | 0.2540 | -90.7870 | -122.5091 | -0.4960 | -0.4447 |
| 0.7507 | 0.14 | 300 | 0.7816 | 0.5741 | 0.2797 | 0.5670 | 0.2944 | -90.3703 | -122.0116 | -0.4909 | -0.4426 |
| 0.6142 | 0.18 | 400 | 0.6435 | 1.0753 | 0.4404 | 0.6369 | 0.6348 | -90.0489 | -121.0092 | -0.4793 | -0.4322 |
| 0.519 | 0.23 | 500 | 0.5196 | 1.7213 | 0.5624 | 0.7430 | 1.1590 | -89.8050 | -119.7171 | -0.4559 | -0.4084 |
| 0.4858 | 0.27 | 600 | 0.4351 | 2.2085 | 0.5923 | 0.7877 | 1.6162 | -89.7450 | -118.7428 | -0.4592 | -0.4138 |
| 0.4048 | 0.32 | 700 | 0.3878 | 2.6105 | 0.5736 | 0.8324 | 2.0369 | -89.7825 | -117.9388 | -0.4398 | -0.3953 |
| 0.3623 | 0.37 | 800 | 0.3383 | 2.7055 | 0.4610 | 0.8520 | 2.2446 | -90.0078 | -117.7487 | -0.4492 | -0.4046 |
| 0.308 | 0.41 | 900 | 0.3145 | 2.9742 | 0.3506 | 0.8520 | 2.6236 | -90.2285 | -117.2114 | -0.4381 | -0.3971 |
| 0.3092 | 0.46 | 1000 | 0.3125 | 3.1541 | 0.2687 | 0.8352 | 2.8854 | -90.3922 | -116.8515 | -0.4276 | -0.3926 |
| 0.2765 | 0.5 | 1100 | 0.2939 | 3.1208 | 0.1475 | 0.8603 | 2.9733 | -90.6347 | -116.9181 | -0.4615 | -0.4216 |
| 0.3058 | 0.55 | 1200 | 0.2772 | 2.9861 | -0.1371 | 0.8771 | 3.1232 | -91.2038 | -117.1875 | -0.4249 | -0.3887 |
| 0.2702 | 0.59 | 1300 | 0.2592 | 3.3217 | -0.0639 | 0.8715 | 3.3856 | -91.0574 | -116.5163 | -0.4497 | -0.4113 |
| 0.2316 | 0.64 | 1400 | 0.2491 | 3.3560 | -0.2934 | 0.8855 | 3.6494 | -91.5165 | -116.4477 | -0.4234 | -0.3869 |
| 0.2344 | 0.68 | 1500 | 0.2506 | 3.2223 | -0.2242 | 0.8687 | 3.4464 | -91.3780 | -116.7152 | -0.4515 | -0.4151 |
| 0.2332 | 0.73 | 1600 | 0.2350 | 3.2137 | -0.4070 | 0.8855 | 3.6207 | -91.7436 | -116.7324 | -0.4299 | -0.3936 |
| 0.2258 | 0.78 | 1700 | 0.2477 | 3.0894 | -0.5590 | 0.8939 | 3.6484 | -92.0476 | -116.9809 | -0.4316 | -0.3960 |
| 0.2526 | 0.82 | 1800 | 0.2277 | 3.2845 | -0.5527 | 0.8771 | 3.8373 | -92.0351 | -116.5907 | -0.4420 | -0.4076 |
| 0.2025 | 0.87 | 1900 | 0.2182 | 3.2061 | -0.8100 | 0.9022 | 4.0160 | -92.5496 | -116.7476 | -0.4319 | -0.3974 |
| 0.2253 | 0.91 | 2000 | 0.2149 | 3.2765 | -0.9756 | 0.9078 | 4.2521 | -92.8809 | -116.6067 | -0.4391 | -0.4023 |
| 0.2084 | 0.96 | 2100 | 0.2223 | 3.1160 | -1.0659 | 0.8939 | 4.1820 | -93.0615 | -116.9277 | -0.4283 | -0.3954 |
| 0.1896 | 1.0 | 2200 | 0.2100 | 3.1835 | -1.0131 | 0.8911 | 4.1966 | -92.9559 | -116.7927 | -0.4517 | -0.4154 |
| 0.2294 | 1.05 | 2300 | 0.2070 | 3.1205 | -1.0873 | 0.8939 | 4.2078 | -93.1043 | -116.9187 | -0.4412 | -0.4051 |
| 0.1897 | 1.1 | 2400 | 0.2011 | 3.1553 | -1.0875 | 0.9050 | 4.2428 | -93.1047 | -116.8492 | -0.4483 | -0.4136 |
| 0.1943 | 1.14 | 2500 | 0.1953 | 3.3317 | -1.2261 | 0.9022 | 4.5578 | -93.3819 | -116.4964 | -0.4488 | -0.4137 |
| 0.1749 | 1.19 | 2600 | 0.1975 | 3.2186 | -1.3232 | 0.8911 | 4.5419 | -93.5761 | -116.7225 | -0.4500 | -0.4160 |
| 0.1881 | 1.23 | 2700 | 0.1838 | 3.3207 | -1.3323 | 0.9274 | 4.6530 | -93.5944 | -116.5184 | -0.4262 | -0.3962 |
| 0.1611 | 1.28 | 2800 | 0.1833 | 3.2881 | -1.3588 | 0.9106 | 4.6469 | -93.6472 | -116.5835 | -0.4404 | -0.4091 |
| 0.1653 | 1.32 | 2900 | 0.1959 | 3.2545 | -1.6143 | 0.9190 | 4.8688 | -94.1584 | -116.6508 | -0.4252 | -0.3996 |
| 0.1613 | 1.37 | 3000 | 0.1779 | 3.3926 | -1.5190 | 0.9218 | 4.9117 | -93.9678 | -116.3744 | -0.4374 | -0.4071 |
| 0.1785 | 1.42 | 3100 | 0.1840 | 3.4053 | -1.6286 | 0.9246 | 5.0339 | -94.1868 | -116.3491 | -0.4280 | -0.3987 |
| 0.1544 | 1.46 | 3200 | 0.1686 | 3.5029 | -1.6389 | 0.9218 | 5.1418 | -94.2075 | -116.1539 | -0.4624 | -0.4309 |
| 0.1492 | 1.51 | 3300 | 0.1706 | 3.2854 | -1.8094 | 0.9330 | 5.0948 | -94.5485 | -116.5889 | -0.4148 | -0.3943 |
| 0.1719 | 1.55 | 3400 | 0.1691 | 3.5148 | -1.7457 | 0.9274 | 5.2605 | -94.4210 | -116.1301 | -0.4542 | -0.4253 |
| 0.1905 | 1.6 | 3500 | 0.1719 | 3.4941 | -1.7454 | 0.9246 | 5.2395 | -94.4204 | -116.1715 | -0.4479 | -0.4189 |
| 0.1354 | 1.64 | 3600 | 0.1749 | 3.5351 | -1.7024 | 0.9106 | 5.2375 | -94.3345 | -116.0895 | -0.4608 | -0.4303 |
| 0.1644 | 1.69 | 3700 | 0.1597 | 3.5736 | -1.6580 | 0.9246 | 5.2316 | -94.2457 | -116.0126 | -0.4469 | -0.4192 |
| 0.1598 | 1.73 | 3800 | 0.1613 | 3.6646 | -1.7035 | 0.9078 | 5.3681 | -94.3367 | -115.8306 | -0.4631 | -0.4349 |
| 0.1337 | 1.78 | 3900 | 0.1583 | 3.5502 | -1.8444 | 0.9134 | 5.3946 | -94.6184 | -116.0593 | -0.4658 | -0.4368 |
| 0.1534 | 1.83 | 4000 | 0.1572 | 3.5076 | -1.9137 | 0.9190 | 5.4213 | -94.7571 | -116.1446 | -0.4610 | -0.4328 |
| 0.1327 | 1.87 | 4100 | 0.1607 | 3.5711 | -1.9143 | 0.9218 | 5.4854 | -94.7583 | -116.0175 | -0.4404 | -0.4153 |
| 0.162 | 1.92 | 4200 | 0.1565 | 3.4852 | -2.0136 | 0.9330 | 5.4988 | -94.9568 | -116.1893 | -0.4641 | -0.4373 |
| 0.1471 | 1.96 | 4300 | 0.1524 | 3.5639 | -1.9766 | 0.9246 | 5.5406 | -94.8830 | -116.0319 | -0.4627 | -0.4338 |
| 0.1333 | 2.01 | 4400 | 0.1418 | 3.6173 | -1.9710 | 0.9162 | 5.5883 | -94.8717 | -115.9251 | -0.4608 | -0.4328 |
| 0.13 | 2.05 | 4500 | 0.1485 | 3.6275 | -1.9865 | 0.9358 | 5.6140 | -94.9027 | -115.9047 | -0.4604 | -0.4319 |
| 0.1311 | 2.1 | 4600 | 0.1503 | 3.4735 | -2.1194 | 0.9134 | 5.5928 | -95.1684 | -116.2128 | -0.4405 | -0.4123 |
| 0.1329 | 2.15 | 4700 | 0.1431 | 3.5793 | -2.1059 | 0.9218 | 5.6852 | -95.1415 | -116.0012 | -0.4519 | -0.4229 |
| 0.1346 | 2.19 | 4800 | 0.1494 | 3.6059 | -2.0642 | 0.9274 | 5.6701 | -95.0581 | -115.9479 | -0.4639 | -0.4332 |
| 0.1462 | 2.24 | 4900 | 0.1455 | 3.4721 | -2.1648 | 0.9218 | 5.6369 | -95.2593 | -116.2156 | -0.4553 | -0.4258 |
| 0.1221 | 2.28 | 5000 | 0.1538 | 3.6293 | -2.1472 | 0.9385 | 5.7764 | -95.2240 | -115.9012 | -0.4525 | -0.4268 |
| 0.1329 | 2.33 | 5100 | 0.1486 | 3.4734 | -2.1778 | 0.9358 | 5.6512 | -95.2853 | -116.2130 | -0.4578 | -0.4301 |
| 0.1284 | 2.37 | 5200 | 0.1527 | 3.4805 | -2.1670 | 0.9078 | 5.6474 | -95.2636 | -116.1988 | -0.4611 | -0.4329 |
| 0.1238 | 2.42 | 5300 | 0.1433 | 3.4570 | -2.1768 | 0.9274 | 5.6338 | -95.2832 | -116.2457 | -0.4451 | -0.4191 |
| 0.1317 | 2.46 | 5400 | 0.1421 | 3.5647 | -2.2232 | 0.9330 | 5.7880 | -95.3761 | -116.0303 | -0.4565 | -0.4342 |
| 0.131 | 2.51 | 5500 | 0.1478 | 3.4211 | -2.2681 | 0.9190 | 5.6892 | -95.4659 | -116.3175 | -0.4444 | -0.4147 |
| 0.1235 | 2.56 | 5600 | 0.1428 | 3.5292 | -2.2798 | 0.9413 | 5.8089 | -95.4892 | -116.1014 | -0.4485 | -0.4234 |
| 0.1122 | 2.6 | 5700 | 0.1445 | 3.6102 | -2.2363 | 0.9330 | 5.8465 | -95.4023 | -115.9393 | -0.4473 | -0.4233 |
| 0.1172 | 2.65 | 5800 | 0.1415 | 3.5813 | -2.1899 | 0.9246 | 5.7712 | -95.3095 | -115.9972 | -0.4648 | -0.4357 |
| 0.1257 | 2.69 | 5900 | 0.1428 | 3.4075 | -2.3047 | 0.9218 | 5.7122 | -95.5390 | -116.3447 | -0.4553 | -0.4269 |
| 0.1441 | 2.74 | 6000 | 0.1426 | 3.4287 | -2.3210 | 0.9190 | 5.7497 | -95.5717 | -116.3024 | -0.4673 | -0.4401 |
| 0.1359 | 2.78 | 6100 | 0.1479 | 3.4833 | -2.2993 | 0.9358 | 5.7826 | -95.5282 | -116.1931 | -0.4409 | -0.4173 |
| 0.1332 | 2.83 | 6200 | 0.1442 | 3.4741 | -2.2726 | 0.9330 | 5.7466 | -95.4748 | -116.2116 | -0.4512 | -0.4262 |
| 0.1454 | 2.88 | 6300 | 0.1397 | 3.4410 | -2.2911 | 0.9358 | 5.7320 | -95.5118 | -116.2778 | -0.4604 | -0.4355 |
| 0.1355 | 2.92 | 6400 | 0.1471 | 3.3740 | -2.3739 | 0.9330 | 5.7479 | -95.6775 | -116.4117 | -0.4473 | -0.4225 |
| 0.1114 | 2.97 | 6500 | 0.1397 | 3.4854 | -2.3222 | 0.9302 | 5.8076 | -95.5740 | -116.1889 | -0.4595 | -0.4345 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
LarryAIDraw/DDLC-10
|
LarryAIDraw
| 2023-12-05T14:16:28Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T14:09:17Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/220864/ddlc-monika-natsuki-sayori-yuri
|
LarryAIDraw/Ai_1_2
|
LarryAIDraw
| 2023-12-05T14:15:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T14:06:52Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/221846/miyashita-ai-oror-lovelive-lora
|
LarryAIDraw/Atlanta-2
|
LarryAIDraw
| 2023-12-05T14:15:20Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T14:05:15Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/221746/kantai-collection-atlanta
|
LarryAIDraw/ChiakiLora-10
|
LarryAIDraw
| 2023-12-05T14:15:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T14:04:47Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/221458/chiaki-matsushita-cote-lora
|
LarryAIDraw/kikyou_kushida_s2-lora-nochekaiser
|
LarryAIDraw
| 2023-12-05T14:14:44Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T14:00:11Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/221294/kikyou-kushida-classroom-of-the-elite
|
LarryAIDraw/Aisha_Hart
|
LarryAIDraw
| 2023-12-05T14:14:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T13:59:26Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/221174/aisha-hart-boushoku-no-berserk-berserk-of-gluttony
|
LarryAIDraw/ClorindeV2
|
LarryAIDraw
| 2023-12-05T14:14:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-05T13:58:32Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/134406/clorinde-genshin-impact
|
sw2703/path_to_saved_model
|
sw2703
| 2023-12-05T14:10:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"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
| 2023-12-05T03:04:26Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks humidifier
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sw2703/path_to_saved_model
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks humidifier using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
Pranavsenthilvel/t5-small-finetuned-xsum
|
Pranavsenthilvel
| 2023-12-05T14:10:17Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-05T12:11:02Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 47 | 0.0594 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
mlinmg/SG-Raccoon-Yi-55B-200k
|
mlinmg
| 2023-12-05T14:09:49Z | 64 | 4 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-28T00:05:36Z |
---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
- en,
pipeline_tag: conversational
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/644ba0c76ebb3ebf7264dbe9/PWn9I-0XH7kSP_YXcyxIg.png" width="400"/>
</p>
---
# This is a retired model since it was merged with a Capybara, which has been trained wrong with a missing eos_token. Check out the new model: [1](https://huggingface.co/mlinmg/SG-Raccoon-Yi-200k-2.0?text=Hi.)
# SG Raccoon 55B
The first 55B auto-regressive causal LM created by combining 2x finetuned [Yi 34b](https://huggingface.co/01-ai/Yi-34B) with *200K context* into one.
# Prompting Format
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
# Merge process
The models used in the merge are [Tess-M-v1.3](https://huggingface.co/migtissera/Tess-M-v1.3/) and [Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B).
The layer ranges used are as follows:
```yaml
- model: migtissera/Tess-M-v1.3
layer_range: [0, 14]
- model: NousResearch/Nous-Capybara-34B
layer_range: [7, 21]
- model: migtissera/Tess-M-v1.3
layer_range: [15, 29]
- model: NousResearch/Nous-Capybara-34B
layer_range: [22, 36]
- model: migtissera/Tess-M-v1.3
layer_range: [30, 44]
- model: NousResearch/Nous-Capybara-34B
layer_range: [37, 51]
- model: migtissera/Tess-M-v1.3
layer_range: [45, 59]
```
# Tips
Being a Yi model, try disabling the BOS token and/or running a lower temperature with MinP (and no other samplers) if output doesn't seem right. Yi tends to run "hot" by default.
Sometimes the model "spells out" the stop token as </s> like Capybara, so you may need to add </s> as an additional stopping condition.
# Benchmarks
Coming soon.
# Acknowledgements
- Special thanks to [MSS](https://milanosamplesale.com/) for sponsoring this project
- [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).
- Great thanks to [@Undi95](https://huggingface.co/Undi95) for helping figuring out model merge options
- Also credits to the [01-ai](https://huggingface.co/01-ai) team for their amazing models
- This merged model is inspired by [Goliath 120B](https://huggingface.co/alpindale/goliath-120b)
|
LoneStriker/smol-7b-6.0bpw-h6-exl2
|
LoneStriker
| 2023-12-05T14:05:33Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"en",
"dataset:HuggingFaceH4/no_robots",
"base_model:openchat/openchat_3.5",
"base_model:finetune:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T13:54:00Z |
---
license: apache-2.0
base_model: openchat/openchat_3.5
datasets:
- HuggingFaceH4/no_robots
language:
- en
widget:
- text: |
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
output:
text: >-
Ahoy there, me hearty! As a friendly pirate chatbot, I be tellin' ye that a human cannot eat a helicopter, as it be a large machine made of metal and suchlike, not fit for human consumption. A human can eat food, like a fine feast of roasted meat and sweet fruits, but a helicopter? That be nonsense, me hearty! So, the answer be none, none at all. Arr!
tags:
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: smol-7b
results: []
---
# Smol 7B
This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on the open source dataset [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using the recipes published in [The Alignment Handbook](https://github.com/huggingface/alignment-handbook).
## Model date
rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
## Evaluation
It achieves the following results on the [Open_LLM_Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). At the time of release, smol-7b is the highest ranked 7B chat model on the [MMLU Benchmark](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ---------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
| **rishiraj/smol-7b** | **67.11** | **63.74** | **84.77** | **65** | **46.17** | **80.66** | **62.32** |
| argilla/notus-7b-v1 | 63.49 | 64.59 | 84.83 | 63.04 | 54.35 | 79.56 | 34.57 |
| Intel/neural-chat-7b-v3-1 | 61.59 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 |
| HuggingFaceH4/zephyr-7b-beta | 61.59 | 62.46 | 84.35 | 60.7 | 57.83 | 77.11 | 27.07 |
| Qwen/Qwen-7B | 59.19 | 51.37 | 78.47 | 59.84 | 47.79 | 72.69 | 44.96 |
| microsoft/Orca-2-7b | 54.55 | 54.1 | 76.19 | 56.37 | 52.45 | 73.48 | 14.71 |
| 01-ai/Yi-6B | 54.08 | 55.55 | 76.57 | 64.11 | 41.96 | 74.19 | 12.13 |
## Inference procedure
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
```
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate"
},
{
"role": "user",
"content": "How many helicopters can a human eat in one sitting?"
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0569 | 0.16 | 3 | 2.0409 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
## Citation Information
```
@misc{rishiraj2023smol,
author = {Rishiraj Acharya},
title = {Smol 7B},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
}
```
|
LoneStriker/smol-7b-4.0bpw-h6-exl2
|
LoneStriker
| 2023-12-05T14:05:25Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"en",
"dataset:HuggingFaceH4/no_robots",
"base_model:openchat/openchat_3.5",
"base_model:finetune:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T13:40:37Z |
---
license: apache-2.0
base_model: openchat/openchat_3.5
datasets:
- HuggingFaceH4/no_robots
language:
- en
widget:
- text: |
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
output:
text: >-
Ahoy there, me hearty! As a friendly pirate chatbot, I be tellin' ye that a human cannot eat a helicopter, as it be a large machine made of metal and suchlike, not fit for human consumption. A human can eat food, like a fine feast of roasted meat and sweet fruits, but a helicopter? That be nonsense, me hearty! So, the answer be none, none at all. Arr!
tags:
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: smol-7b
results: []
---
# Smol 7B
This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on the open source dataset [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using the recipes published in [The Alignment Handbook](https://github.com/huggingface/alignment-handbook).
## Model date
rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
## Evaluation
It achieves the following results on the [Open_LLM_Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). At the time of release, smol-7b is the highest ranked 7B chat model on the [MMLU Benchmark](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ---------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
| **rishiraj/smol-7b** | **67.11** | **63.74** | **84.77** | **65** | **46.17** | **80.66** | **62.32** |
| argilla/notus-7b-v1 | 63.49 | 64.59 | 84.83 | 63.04 | 54.35 | 79.56 | 34.57 |
| Intel/neural-chat-7b-v3-1 | 61.59 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 |
| HuggingFaceH4/zephyr-7b-beta | 61.59 | 62.46 | 84.35 | 60.7 | 57.83 | 77.11 | 27.07 |
| Qwen/Qwen-7B | 59.19 | 51.37 | 78.47 | 59.84 | 47.79 | 72.69 | 44.96 |
| microsoft/Orca-2-7b | 54.55 | 54.1 | 76.19 | 56.37 | 52.45 | 73.48 | 14.71 |
| 01-ai/Yi-6B | 54.08 | 55.55 | 76.57 | 64.11 | 41.96 | 74.19 | 12.13 |
## Inference procedure
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
```
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate"
},
{
"role": "user",
"content": "How many helicopters can a human eat in one sitting?"
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0569 | 0.16 | 3 | 2.0409 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
## Citation Information
```
@misc{rishiraj2023smol,
author = {Rishiraj Acharya},
title = {Smol 7B},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
}
```
|
LoneStriker/smol-7b-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-05T14:05:21Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"en",
"dataset:HuggingFaceH4/no_robots",
"base_model:openchat/openchat_3.5",
"base_model:finetune:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-05T13:33:58Z |
---
license: apache-2.0
base_model: openchat/openchat_3.5
datasets:
- HuggingFaceH4/no_robots
language:
- en
widget:
- text: |
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
output:
text: >-
Ahoy there, me hearty! As a friendly pirate chatbot, I be tellin' ye that a human cannot eat a helicopter, as it be a large machine made of metal and suchlike, not fit for human consumption. A human can eat food, like a fine feast of roasted meat and sweet fruits, but a helicopter? That be nonsense, me hearty! So, the answer be none, none at all. Arr!
tags:
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: smol-7b
results: []
---
# Smol 7B
This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on the open source dataset [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using the recipes published in [The Alignment Handbook](https://github.com/huggingface/alignment-handbook).
## Model date
rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
## Evaluation
It achieves the following results on the [Open_LLM_Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). At the time of release, smol-7b is the highest ranked 7B chat model on the [MMLU Benchmark](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu).
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ---------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
| **rishiraj/smol-7b** | **67.11** | **63.74** | **84.77** | **65** | **46.17** | **80.66** | **62.32** |
| argilla/notus-7b-v1 | 63.49 | 64.59 | 84.83 | 63.04 | 54.35 | 79.56 | 34.57 |
| Intel/neural-chat-7b-v3-1 | 61.59 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 |
| HuggingFaceH4/zephyr-7b-beta | 61.59 | 62.46 | 84.35 | 60.7 | 57.83 | 77.11 | 27.07 |
| Qwen/Qwen-7B | 59.19 | 51.37 | 78.47 | 59.84 | 47.79 | 72.69 | 44.96 |
| microsoft/Orca-2-7b | 54.55 | 54.1 | 76.19 | 56.37 | 52.45 | 73.48 | 14.71 |
| 01-ai/Yi-6B | 54.08 | 55.55 | 76.57 | 64.11 | 41.96 | 74.19 | 12.13 |
## Inference procedure
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
```
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate"
},
{
"role": "user",
"content": "How many helicopters can a human eat in one sitting?"
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0569 | 0.16 | 3 | 2.0409 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
## Citation Information
```
@misc{rishiraj2023smol,
author = {Rishiraj Acharya},
title = {Smol 7B},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
}
```
|
teo-sanchez/tfjs_vision_embeddings
|
teo-sanchez
| 2023-12-05T13:54:21Z | 0 | 0 | null |
[
"image-classification",
"en",
"region:us"
] |
image-classification
| 2023-12-05T12:45:26Z |
---
language:
- en
pipeline_tag: image-classification
---
Export of DenseNet169, DenseNet201, and ResNet152 as tfjs binaries.
Exported from the corresponding Keras versions in https://www.tensorflow.org/api_docs/python/tf/keras/applications
|
Ahmed107/nllb200-ar-en_v8
|
Ahmed107
| 2023-12-05T13:51:12Z | 13 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-12-05T12:50:52Z |
---
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-600M
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nllb200-ar-en_v8
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. -->
# nllb200-ar-en_v8
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7426
- Bleu: 54.0881
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
VRT-2428211/NLP2_Base_3e-5
|
VRT-2428211
| 2023-12-05T13:41:59Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-05T11:31:18Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: NLP2_Base_3e-5
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. -->
# NLP2_Base_3e-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow37
|
FounderOfHuggingface
| 2023-12-05T13:41:28Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:41:26Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow35
|
FounderOfHuggingface
| 2023-12-05T13:41:19Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:41:17Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2-new2
|
pavitemple
| 2023-12-05T13:41:19Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-05T10:40:39Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-Accident-MultipleLabels-Video-subset-v2-new2
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. -->
# finetuned-Accident-MultipleLabels-Video-subset-v2-new2
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8100
- Accuracy: 0.2963
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.13 | 4 | 1.8680 | 0.0938 |
| No log | 1.13 | 8 | 1.9307 | 0.0469 |
| 1.7131 | 2.13 | 12 | 2.0182 | 0.0312 |
| 1.7131 | 3.13 | 16 | 1.9762 | 0.0625 |
| 1.4337 | 4.13 | 20 | 1.9643 | 0.0469 |
| 1.4337 | 5.13 | 24 | 1.9054 | 0.0625 |
| 1.4337 | 6.13 | 28 | 1.8946 | 0.0938 |
| 1.4096 | 7.07 | 30 | 1.8918 | 0.0938 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231202+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow33
|
FounderOfHuggingface
| 2023-12-05T13:41:10Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:41:08Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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## 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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#### Hardware
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow31
|
FounderOfHuggingface
| 2023-12-05T13:41:02Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:41:00Z |
---
library_name: peft
base_model: gpt2
---
# 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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#### 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).
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow30
|
FounderOfHuggingface
| 2023-12-05T13:40:57Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:55Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow26
|
FounderOfHuggingface
| 2023-12-05T13:40:39Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:37Z |
---
library_name: peft
base_model: gpt2
---
# 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.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
[More Information Needed]
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## Evaluation
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## 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]
### Model Architecture and Objective
[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]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow22
|
FounderOfHuggingface
| 2023-12-05T13:40:22Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:20Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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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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## Training Details
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#### Preprocessing [optional]
[More Information Needed]
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<!-- 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. -->
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[More Information Needed]
#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow21
|
FounderOfHuggingface
| 2023-12-05T13:40:17Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:15Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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<!-- 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
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<!-- 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. -->
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[More Information Needed]
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<!-- 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. -->
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow20
|
FounderOfHuggingface
| 2023-12-05T13:40:13Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:11Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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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## Technical Specifications [optional]
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow19
|
FounderOfHuggingface
| 2023-12-05T13:40:09Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:40:07Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Training Details
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## Evaluation
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### Results
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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).
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## Technical Specifications [optional]
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow15
|
FounderOfHuggingface
| 2023-12-05T13:39:51Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:49Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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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
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[More Information Needed]
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## Evaluation
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow14
|
FounderOfHuggingface
| 2023-12-05T13:39:47Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:45Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- 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
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[More Information Needed]
#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[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 [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow13
|
FounderOfHuggingface
| 2023-12-05T13:39:43Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:41Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- 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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#### Preprocessing [optional]
[More Information Needed]
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<!-- 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
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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]
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[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow9
|
FounderOfHuggingface
| 2023-12-05T13:39:26Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:23Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- 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. -->
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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#### Metrics
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow8
|
FounderOfHuggingface
| 2023-12-05T13:39:21Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:20Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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#### Metrics
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[More Information Needed]
### Results
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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).
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## Technical Specifications [optional]
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[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow5
|
FounderOfHuggingface
| 2023-12-05T13:39:09Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:06Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Evaluation
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow4
|
FounderOfHuggingface
| 2023-12-05T13:39:03Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:39:01Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[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 [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow3
|
FounderOfHuggingface
| 2023-12-05T13:38:58Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:38:55Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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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
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<!-- 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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#### 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
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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]
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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 [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow41
|
FounderOfHuggingface
| 2023-12-05T13:38:53Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:38:51Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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## 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]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- 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. -->
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow40
|
FounderOfHuggingface
| 2023-12-05T13:38:49Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:38:46Z |
---
library_name: peft
base_model: gpt2
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **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
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[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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_member_shadow39
|
FounderOfHuggingface
| 2023-12-05T13:38:44Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:38:41Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
dtu-deep-learning-course-f2023/msmarco-rag-finetune
|
dtu-deep-learning-course-f2023
| 2023-12-05T13:38:19Z | 1 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-05T13:21:56Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 51 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 15,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow19
|
FounderOfHuggingface
| 2023-12-05T13:36:12Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:36:09Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow18
|
FounderOfHuggingface
| 2023-12-05T13:36:07Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:36:05Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow15
|
FounderOfHuggingface
| 2023-12-05T13:35:55Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:53Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow14
|
FounderOfHuggingface
| 2023-12-05T13:35:51Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:49Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### 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
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### 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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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow13
|
FounderOfHuggingface
| 2023-12-05T13:35:47Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:45Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
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### 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]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
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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]
- **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]
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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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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow11
|
FounderOfHuggingface
| 2023-12-05T13:35:39Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:37Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **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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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
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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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
Youku2/corgy_LilyAurora_LoRA
|
Youku2
| 2023-12-05T13:35:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-05T13:35:14Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of LilyAurora person
license: openrail++
---
# SDXL LoRA DreamBooth - Youku2/corgy_LilyAurora_LoRA
<Gallery />
## Model description
These are Youku2/corgy_LilyAurora_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of LilyAurora person to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Youku2/corgy_LilyAurora_LoRA/tree/main) them in the Files & versions tab.
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow5
|
FounderOfHuggingface
| 2023-12-05T13:35:13Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:11Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **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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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow4
|
FounderOfHuggingface
| 2023-12-05T13:35:09Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:06Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow3
|
FounderOfHuggingface
| 2023-12-05T13:35:04Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:35:02Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
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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]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow2
|
FounderOfHuggingface
| 2023-12-05T13:34:59Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:34:56Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t300_e20_non_member_shadow1
|
FounderOfHuggingface
| 2023-12-05T13:34:54Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-12-05T13:34:52Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
cgoosen/llm_firewall_distilbert-base-uncased
|
cgoosen
| 2023-12-05T13:29:50Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-20T11:41:41Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llm_firewall_distilbert-base-uncased
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. -->
# llm_firewall_distilbert-base-uncased
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1218
- Accuracy: 0.9451
# Latest finetune 5 Dec 2023
{'eval_loss': 0.12179878354072571,
'eval_accuracy': 0.9450980392156862,
'eval_runtime': 5.8053,
'eval_samples_per_second': 43.925,
'eval_steps_per_second': 2.756,
'epoch': 20.0}
## Model description
Finetuned distilbert-uncased on prompts that are either malicious or benign.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3191 | 1.0 | 64 | 0.5996 | 0.7255 |
| 0.5065 | 2.0 | 128 | 0.4536 | 0.8 |
| 0.4134 | 3.0 | 192 | 0.3856 | 0.8275 |
| 0.3294 | 4.0 | 256 | 0.2654 | 0.8824 |
| 0.2536 | 5.0 | 320 | 0.1977 | 0.9216 |
| 0.2001 | 6.0 | 384 | 0.1671 | 0.9412 |
| 0.2144 | 7.0 | 448 | 0.1670 | 0.9373 |
| 0.2017 | 8.0 | 512 | 0.1575 | 0.9333 |
| 0.1819 | 9.0 | 576 | 0.1866 | 0.9294 |
| 0.143 | 10.0 | 640 | 0.1834 | 0.9373 |
| 0.153 | 11.0 | 704 | 0.1589 | 0.9412 |
| 0.1469 | 12.0 | 768 | 0.1347 | 0.9451 |
| 0.1568 | 13.0 | 832 | 0.1425 | 0.9451 |
| 0.139 | 14.0 | 896 | 0.1438 | 0.9451 |
| 0.1889 | 15.0 | 960 | 0.1330 | 0.9451 |
| 0.1185 | 16.0 | 1024 | 0.1323 | 0.9451 |
| 0.1166 | 17.0 | 1088 | 0.1280 | 0.9451 |
| 0.1475 | 18.0 | 1152 | 0.1233 | 0.9451 |
| 0.1145 | 19.0 | 1216 | 0.1225 | 0.9451 |
| 0.1121 | 20.0 | 1280 | 0.1218 | 0.9451 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
SidXXD/CD_flw_2_512-Ppt_flw
|
SidXXD
| 2023-12-05T13:29:22Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-05T13:25:14Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: photo of a <new1> flower
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - SidXXD/CD_flw_2_512-Ppt_flw
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> flower using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
yesj1234/koen_xlsr_100p_run1
|
yesj1234
| 2023-12-05T13:25:18Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"./train_dataset.py",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-05T13:23:23Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- ./train_dataset.py
- generated_from_trainer
model-index:
- name: koen_xlsr_100p_run1
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. -->
# koen_xlsr_100p_run1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./TRAIN_DATASET.PY - NA 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
chintanshah-pathai/contrimix
|
chintanshah-pathai
| 2023-12-05T13:13:29Z | 0 | 1 | null |
[
"en",
"dataset:wltjr1007/Camelyon17-WILDS",
"arxiv:2306.04527",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-12-04T17:02:48Z |
---
license: cc-by-nc-sa-4.0
language:
- en
datasets:
- wltjr1007/Camelyon17-WILDS
---
# ContriMix: Unsupervised disentanglement of content and attribute for domain generalization in microscopy image analysis
Contrimix is an approach for domain generalization proposed in [Contrimix: Unsupervised disentanglement of content and attribute for domain generalization in microscopy image analysis](https://arxiv.org/abs/2306.04527).
|
ai-forever/mGPT
|
ai-forever
| 2023-12-05T13:12:21Z | 10,644 | 258 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"multilingual",
"PyTorch",
"Transformers",
"gpt3",
"Deepspeed",
"Megatron",
"ar",
"he",
"vi",
"id",
"jv",
"ms",
"tl",
"lv",
"lt",
"eu",
"ml",
"ta",
"te",
"hy",
"bn",
"mr",
"hi",
"ur",
"af",
"da",
"en",
"de",
"sv",
"fr",
"it",
"pt",
"ro",
"es",
"el",
"os",
"tg",
"fa",
"ja",
"ka",
"ko",
"th",
"bxr",
"xal",
"mn",
"sw",
"yo",
"be",
"bg",
"ru",
"uk",
"pl",
"my",
"uz",
"ba",
"kk",
"ky",
"tt",
"az",
"cv",
"tr",
"tk",
"tyv",
"sax",
"et",
"fi",
"hu",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2112.10668",
"arxiv:2204.07580",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-07T09:13:42Z |
---
license: apache-2.0
language:
- ar
- he
- vi
- id
- jv
- ms
- tl
- lv
- lt
- eu
- ml
- ta
- te
- hy
- bn
- mr
- hi
- ur
- af
- da
- en
- de
- sv
- fr
- it
- pt
- ro
- es
- el
- os
- tg
- fa
- ja
- ka
- ko
- th
- bxr
- xal
- mn
- sw
- yo
- be
- bg
- ru
- uk
- pl
- my
- uz
- ba
- kk
- ky
- tt
- az
- cv
- tr
- tk
- tyv
- sax
- et
- fi
- hu
pipeline_tag: text-generation
tags:
- multilingual
- PyTorch
- Transformers
- gpt3
- gpt2
- Deepspeed
- Megatron
datasets:
- mc4
- wikipedia
thumbnail: "https://github.com/sberbank-ai/mgpt"
---
# Multilingual GPT model
We introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus.
We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, [Deepspeed](https://github.com/microsoft/DeepSpeed) and [Megatron](https://github.com/NVIDIA/Megatron-LM) frameworks allows us to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released [XGLM](https://arxiv.org/pdf/2112.10668.pdf) models at the same time covering more languages and enhancing NLP possibilities for low resource languages.
## Code
The source code for the mGPT XL model is available on [Github](https://github.com/sberbank-ai/mgpt)
## Paper
mGPT: Few-Shot Learners Go Multilingual
[Abstract](https://arxiv.org/abs/2204.07580) [PDF](https://arxiv.org/pdf/2204.07580.pdf)
```
@misc{https://doi.org/10.48550/arxiv.2204.07580,
doi = {10.48550/ARXIV.2204.07580},
url = {https://arxiv.org/abs/2204.07580},
author = {Shliazhko, Oleh and Fenogenova, Alena and Tikhonova, Maria and Mikhailov, Vladislav and Kozlova, Anastasia and Shavrina, Tatiana},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2; I.2.7, 68-06, 68-04, 68T50, 68T01},
title = {mGPT: Few-Shot Learners Go Multilingual},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
## Languages
Model supports 61 languages:
ISO codes:
```ar he vi id jv ms tl lv lt eu ml ta te hy bn mr hi ur af da en de sv fr it pt ro es el os tg fa ja ka ko th bxr xal mn sw yo be bg ru uk pl my uz ba kk ky tt az cv tr tk tyv sax et fi hu```
Languages:
```Arabic, Hebrew, Vietnamese, Indonesian, Javanese, Malay, Tagalog, Latvian, Lithuanian, Basque, Malayalam, Tamil, Telugu, Armenian, Bengali, Marathi, Hindi, Urdu, Afrikaans, Danish, English, German, Swedish, French, Italian, Portuguese, Romanian, Spanish, Greek, Ossetian, Tajik, Persian, Japanese, Georgian, Korean, Thai, Buryat, Kalmyk, Mongolian, Swahili, Yoruba, Belarusian, Bulgarian, Russian, Ukrainian, Polish, Burmese, Uzbek, Bashkir, Kazakh, Kyrgyz, Tatar, Azerbaijani, Chuvash, Turkish, Turkmen, Tuvan, Yakut, Estonian, Finnish, Hungarian```
## Training Data Statistics
- Size: 488 Billion UTF characters
<img style="text-align:center; display:block;" src="https://huggingface.co/sberbank-ai/mGPT/resolve/main/stats.png">
"General training corpus statistics"
## Details
The model was trained with sequence length 512 using Megatron and Deepspeed libs by [SberDevices](https://sberdevices.ru/) team on a dataset of 600 GB of texts in 61 languages. The model has seen 440 billion BPE tokens in total.
Total training time was around 14 days on 256 Nvidia V100 GPUs.
|
mireiaplalis/bert-finetuned-ner-cadec
|
mireiaplalis
| 2023-12-05T12:58:53Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-05T12:28:54Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-cadec
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-cadec
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2301
- Precision: 0.5948
- Recall: 0.6779
- F1: 0.6336
- Accuracy: 0.9265
- Adr Precision: 0.5579
- Adr Recall: 0.6812
- Adr F1: 0.6134
- Disease Precision: 0.2273
- Disease Recall: 0.1562
- Disease F1: 0.1852
- Drug Precision: 0.8136
- Drug Recall: 0.8775
- Drug F1: 0.8443
- Finding Precision: 0.2667
- Finding Recall: 0.2759
- Finding F1: 0.2712
- Symptom Precision: 0.5
- Symptom Recall: 0.0435
- Symptom F1: 0.08
- B-adr Precision: 0.7749
- B-adr Recall: 0.8513
- B-adr F1: 0.8113
- B-disease Precision: 1.0
- B-disease Recall: 0.1562
- B-disease F1: 0.2703
- B-drug Precision: 0.9327
- B-drug Recall: 0.9557
- B-drug F1: 0.9440
- B-finding Precision: 0.5909
- B-finding Recall: 0.4483
- B-finding F1: 0.5098
- B-symptom Precision: 0.5
- B-symptom Recall: 0.0435
- B-symptom F1: 0.08
- I-adr Precision: 0.5725
- I-adr Recall: 0.6782
- I-adr F1: 0.6209
- I-disease Precision: 0.4091
- I-disease Recall: 0.3103
- I-disease F1: 0.3529
- I-drug Precision: 0.8458
- I-drug Recall: 0.8873
- I-drug F1: 0.8660
- I-finding Precision: 0.3529
- I-finding Recall: 0.2222
- I-finding F1: 0.2727
- I-symptom Precision: 0.0
- I-symptom Recall: 0.0
- I-symptom F1: 0.0
- Macro Avg F1: 0.4728
- Weighted Avg F1: 0.7278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Adr Precision | Adr Recall | Adr F1 | Disease Precision | Disease Recall | Disease F1 | Drug Precision | Drug Recall | Drug F1 | Finding Precision | Finding Recall | Finding F1 | Symptom Precision | Symptom Recall | Symptom F1 | B-adr Precision | B-adr Recall | B-adr F1 | B-disease Precision | B-disease Recall | B-disease F1 | B-drug Precision | B-drug Recall | B-drug F1 | B-finding Precision | B-finding Recall | B-finding F1 | B-symptom Precision | B-symptom Recall | B-symptom F1 | I-adr Precision | I-adr Recall | I-adr F1 | I-disease Precision | I-disease Recall | I-disease F1 | I-drug Precision | I-drug Recall | I-drug F1 | I-finding Precision | I-finding Recall | I-finding F1 | I-symptom Precision | I-symptom Recall | I-symptom F1 | Macro Avg F1 | Weighted Avg F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:------------:|:---------------:|
| No log | 1.0 | 127 | 0.2653 | 0.5472 | 0.6201 | 0.5814 | 0.9128 | 0.4942 | 0.6376 | 0.5568 | 0.0 | 0.0 | 0.0 | 0.7952 | 0.8186 | 0.8068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7530 | 0.7731 | 0.7629 | 0.0 | 0.0 | 0.0 | 0.9179 | 0.8818 | 0.8995 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4915 | 0.6325 | 0.5532 | 0.1429 | 0.0345 | 0.0556 | 0.855 | 0.8382 | 0.8465 | 0.3333 | 0.0370 | 0.0667 | 0.0 | 0.0 | 0.0 | 0.3184 | 0.6587 |
| No log | 2.0 | 254 | 0.2307 | 0.5896 | 0.6632 | 0.6242 | 0.9254 | 0.5546 | 0.6722 | 0.6077 | 0.2222 | 0.1875 | 0.2034 | 0.8093 | 0.8529 | 0.8305 | 0.2083 | 0.1724 | 0.1887 | 0.0 | 0.0 | 0.0 | 0.7663 | 0.8263 | 0.7952 | 1.0 | 0.1562 | 0.2703 | 0.9366 | 0.9458 | 0.9412 | 0.625 | 0.3448 | 0.4444 | 0.0 | 0.0 | 0.0 | 0.5649 | 0.6600 | 0.6088 | 0.2963 | 0.2759 | 0.2857 | 0.8495 | 0.8578 | 0.8537 | 0.3846 | 0.1852 | 0.25 | 0.0 | 0.0 | 0.0 | 0.4449 | 0.7127 |
| No log | 3.0 | 381 | 0.2301 | 0.5948 | 0.6779 | 0.6336 | 0.9265 | 0.5579 | 0.6812 | 0.6134 | 0.2273 | 0.1562 | 0.1852 | 0.8136 | 0.8775 | 0.8443 | 0.2667 | 0.2759 | 0.2712 | 0.5 | 0.0435 | 0.08 | 0.7749 | 0.8513 | 0.8113 | 1.0 | 0.1562 | 0.2703 | 0.9327 | 0.9557 | 0.9440 | 0.5909 | 0.4483 | 0.5098 | 0.5 | 0.0435 | 0.08 | 0.5725 | 0.6782 | 0.6209 | 0.4091 | 0.3103 | 0.3529 | 0.8458 | 0.8873 | 0.8660 | 0.3529 | 0.2222 | 0.2727 | 0.0 | 0.0 | 0.0 | 0.4728 | 0.7278 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
RaushanTurganbay/GPT2_instruct_tuned
|
RaushanTurganbay
| 2023-12-05T12:57:35Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:Anthropic/hh-rlhf",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-03T19:57:23Z |
---
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
language:
- en
pipeline_tag: text-generation
---
# GPT-2 Medium Fine-Tuned on Anthropic-hh Dataset
This repository houses a GPT-2 Medium model fine-tuned on the Anthropic-hh dataset. The fine-tuning process involved masking Human's utterances, with the loss computed exclusively on the Assistant's responses.
## Model Information
- **Base Model:** GPT-2 Medium
- **Training Data:** Anthropic-hh dataset
- **Fine-Tuning Approach:** Supervised fine-tuning with a focus on Assistant's responses.
## How to Use
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("RaushanTurganbay/GPT2_instruct_tuned")
model = GPT2LMHeadModel.from_pretrained("RaushanTurganbay/GPT2_instruct_tuned")
# Generate responses
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = [stop.to("cuda") for stop in stops]
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[0][-len(stop):])).item():
return True
return False
def stopping_criteria(tokenizer, stop_words):
stop_words_ids = [tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words]
stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
return stopping_criteria
# Generate responses
stopping = stopping_criteria(tokenizer, ["\n\nHuman:"])
prompt = "\n\nHuman: {your_instruction}\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, stopping_criteria=stopping, max_length=150)
print("Model Response:", tokenizer.batch_decode(outputs))
```
|
BramVanroy/falcon-7b-ft-mc4_nl_cleaned_tiny
|
BramVanroy
| 2023-12-05T12:57:14Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"base_model:tiiuae/falcon-7b",
"base_model:finetune:tiiuae/falcon-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-24T21:31:06Z |
---
license: apache-2.0
base_model: tiiuae/falcon-7b
datasets:
- yhavinga/mc4_nl_cleaned
model-index:
- name: falcon-7b-ft-mc4_nl_cleaned_tiny
results: []
language:
- nl
inference: false
tags:
- falcon
---
# falcon-7b-ft-mc4_nl_cleaned_tiny
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
on the [yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) dataset (`tiny` partition) on a context of 2048 tokens.
See the original [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) for more information, intended use, and biases.
## Intended uses & limitations
This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes.
Importantly, the original Falcon 7B model was only trained on English and French. Therefore, Dutch generations should be taken with a massive grain of salt. I
wanted to see if the performance would be reasonable after finetuning this model on a Dutch dataset. I find that it is okay but not great. It's especially not coherent.
## Training and evaluation data
Trained on the [yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) dataset (`tiny` partition) for one epoch. The canonical
validation split was not used but instead 5% of `train` was used as validation.
At 2048 tokens context length, the training set was around 2M (2,008,858) samples, and the model was trained for 1 epoch. That means that the model was trained for
around 4B Dutch tokens (`2048 * 2008858 = 4.114.141.184`).
## Training procedure
Trained with LoRA targetting `['query_key_value', 'dense', 'dense_h_to_4h', 'dense_4h_to_h']` in 4 bit and merged before upload.
The adapters are in the `adapters` branch.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 12
- eval_batch_size: 24
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 6
- total_train_batch_size: 1152
- total_eval_batch_size: 384
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6094 | 0.1 | 170 | 2.5980 |
| 2.4503 | 0.19 | 340 | 2.4405 |
| 2.3243 | 0.29 | 510 | 2.3428 |
| 2.2822 | 0.39 | 680 | 2.2752 |
| 2.238 | 0.49 | 850 | 2.2248 |
| 2.2015 | 0.58 | 1020 | 2.1865 |
| 2.1678 | 0.68 | 1190 | 2.1560 |
| 2.1301 | 0.78 | 1360 | 2.1312 |
| 2.1161 | 0.88 | 1530 | 2.1112 |
| 2.0997 | 0.97 | 1700 | 2.0928 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
BramVanroy/falcon-7b-ft-alpaca-cleaned-dutch
|
BramVanroy
| 2023-12-05T12:52:29Z | 21 | 1 |
transformers
|
[
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"nl",
"dataset:BramVanroy/alpaca-cleaned-dutch",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-02T09:15:45Z |
---
language:
- nl
license: cc-by-nc-4.0
datasets:
- BramVanroy/alpaca-cleaned-dutch
inference: false
base_model: ybelkada/falcon-7b-sharded-bf16
model-index:
- name: falcon-7b-ft-alpaca-cleaned-dutch
results: []
---
# falcon-7b-ft-alpaca-cleaned-dutch
## Model description
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the [BramVanroy/alpaca-cleaned-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) dataset.
See the original [Falcon 7B model](https://huggingface.co/tiiuae/falcon-7b/) for more information, intended use, and biases.
## Intended uses & limitations
This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes, and an opportunity for me to test hyperparameters and stability.
Importantly, the original Falcon 7B model was only trained on English and French. Therefore, Dutch generations should be taken with a massive grain of salt.
## Training and evaluation data
Trained on the synthetic [BramVanroy/alpaca-cleaned-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) instruction dataset.
Therefore, commercial use of this model is forbidden. The model is intended for research purposes only.
## Training procedure
Trained with LoRA and merged before upload.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9832 | 0.03 | 10 | 1.8889 |
| 1.9355 | 0.05 | 20 | 1.8834 |
| 1.9694 | 0.08 | 30 | 1.8671 |
| 1.9048 | 0.1 | 40 | 1.8328 |
| 1.8443 | 0.13 | 50 | 1.7970 |
| 1.7448 | 0.16 | 60 | 1.7711 |
| 1.8004 | 0.18 | 70 | 1.7522 |
| 1.7767 | 0.21 | 80 | 1.7370 |
| 1.7733 | 0.23 | 90 | 1.7248 |
| 1.7926 | 0.26 | 100 | 1.7149 |
| 1.8258 | 0.29 | 110 | 1.7066 |
| 1.6709 | 0.31 | 120 | 1.6993 |
| 1.6612 | 0.34 | 130 | 1.6926 |
| 1.8463 | 0.36 | 140 | 1.6867 |
| 1.8413 | 0.39 | 150 | 1.6814 |
| 1.7659 | 0.42 | 160 | 1.6765 |
| 1.69 | 0.44 | 170 | 1.6715 |
| 1.7219 | 0.47 | 180 | 1.6673 |
| 1.6755 | 0.49 | 190 | 1.6627 |
| 1.7823 | 0.52 | 200 | 1.6584 |
| 1.7635 | 0.55 | 210 | 1.6545 |
| 1.7335 | 0.57 | 220 | 1.6506 |
| 1.7272 | 0.6 | 230 | 1.6471 |
| 1.718 | 0.63 | 240 | 1.6436 |
| 1.6899 | 0.65 | 250 | 1.6403 |
| 1.622 | 0.68 | 260 | 1.6370 |
| 1.6556 | 0.7 | 270 | 1.6337 |
| 1.7912 | 0.73 | 280 | 1.6304 |
| 1.6025 | 0.76 | 290 | 1.6274 |
| 1.7181 | 0.78 | 300 | 1.6246 |
| 1.7452 | 0.81 | 310 | 1.6217 |
| 1.5975 | 0.83 | 320 | 1.6189 |
| 1.5754 | 0.86 | 330 | 1.6162 |
| 1.7077 | 0.89 | 340 | 1.6136 |
| 1.5848 | 0.91 | 350 | 1.6112 |
| 1.7011 | 0.94 | 360 | 1.6087 |
| 1.6697 | 0.96 | 370 | 1.6065 |
| 1.6633 | 0.99 | 380 | 1.6042 |
| 1.6722 | 1.02 | 390 | 1.6015 |
| 1.7181 | 1.04 | 400 | 1.5993 |
| 1.6414 | 1.07 | 410 | 1.5972 |
| 1.6856 | 1.09 | 420 | 1.5952 |
| 1.6491 | 1.12 | 430 | 1.5930 |
| 1.6736 | 1.15 | 440 | 1.5912 |
| 1.619 | 1.17 | 450 | 1.5893 |
| 1.6452 | 1.2 | 460 | 1.5870 |
| 1.6498 | 1.22 | 470 | 1.5854 |
| 1.675 | 1.25 | 480 | 1.5839 |
| 1.684 | 1.28 | 490 | 1.5823 |
| 1.6379 | 1.3 | 500 | 1.5802 |
| 1.5173 | 1.33 | 510 | 1.5786 |
| 1.6443 | 1.35 | 520 | 1.5773 |
| 1.5628 | 1.38 | 530 | 1.5755 |
| 1.7287 | 1.41 | 540 | 1.5738 |
| 1.5615 | 1.43 | 550 | 1.5725 |
| 1.6129 | 1.46 | 560 | 1.5712 |
| 1.6709 | 1.48 | 570 | 1.5700 |
| 1.5818 | 1.51 | 580 | 1.5683 |
| 1.6358 | 1.54 | 590 | 1.5672 |
| 1.6513 | 1.56 | 600 | 1.5662 |
| 1.5637 | 1.59 | 610 | 1.5654 |
| 1.612 | 1.62 | 620 | 1.5643 |
| 1.6396 | 1.64 | 630 | 1.5630 |
| 1.6414 | 1.67 | 640 | 1.5620 |
| 1.6096 | 1.69 | 650 | 1.5611 |
| 1.6149 | 1.72 | 660 | 1.5603 |
| 1.5886 | 1.75 | 670 | 1.5593 |
| 1.537 | 1.77 | 680 | 1.5582 |
| 1.5883 | 1.8 | 690 | 1.5574 |
| 1.6512 | 1.82 | 700 | 1.5566 |
| 1.683 | 1.85 | 710 | 1.5559 |
| 1.7059 | 1.88 | 720 | 1.5549 |
| 1.5453 | 1.9 | 730 | 1.5542 |
| 1.5738 | 1.93 | 740 | 1.5536 |
| 1.6004 | 1.95 | 750 | 1.5530 |
| 1.6753 | 1.98 | 760 | 1.5523 |
| 1.6362 | 2.01 | 770 | 1.5517 |
| 1.5805 | 2.03 | 780 | 1.5511 |
| 1.6416 | 2.06 | 790 | 1.5508 |
| 1.5755 | 2.08 | 800 | 1.5506 |
| 1.5763 | 2.11 | 810 | 1.5501 |
| 1.7112 | 2.14 | 820 | 1.5497 |
| 1.6533 | 2.16 | 830 | 1.5493 |
| 1.6008 | 2.19 | 840 | 1.5489 |
| 1.5731 | 2.21 | 850 | 1.5485 |
| 1.4975 | 2.24 | 860 | 1.5480 |
| 1.6158 | 2.27 | 870 | 1.5478 |
| 1.6063 | 2.29 | 880 | 1.5474 |
| 1.628 | 2.32 | 890 | 1.5470 |
| 1.6177 | 2.34 | 900 | 1.5468 |
| 1.5646 | 2.37 | 910 | 1.5467 |
| 1.5272 | 2.4 | 920 | 1.5466 |
| 1.5402 | 2.42 | 930 | 1.5464 |
| 1.5815 | 2.45 | 940 | 1.5461 |
| 1.4857 | 2.47 | 950 | 1.5459 |
| 1.5923 | 2.5 | 960 | 1.5458 |
| 1.6167 | 2.53 | 970 | 1.5456 |
| 1.7214 | 2.55 | 980 | 1.5456 |
| 1.5467 | 2.58 | 990 | 1.5455 |
| 1.6455 | 2.61 | 1000 | 1.5453 |
| 1.6137 | 2.63 | 1010 | 1.5453 |
| 1.6104 | 2.66 | 1020 | 1.5453 |
| 1.6756 | 2.68 | 1030 | 1.5451 |
| 1.5818 | 2.71 | 1040 | 1.5450 |
| 1.5829 | 2.74 | 1050 | 1.5450 |
| 1.5753 | 2.76 | 1060 | 1.5450 |
| 1.6484 | 2.79 | 1070 | 1.5450 |
| 1.6765 | 2.81 | 1080 | 1.5450 |
| 1.623 | 2.84 | 1090 | 1.5449 |
| 1.6901 | 2.87 | 1100 | 1.5449 |
| 1.6601 | 2.89 | 1110 | 1.5449 |
| 1.6763 | 2.92 | 1120 | 1.5449 |
| 1.6203 | 2.94 | 1130 | 1.5449 |
| 1.5113 | 2.97 | 1140 | 1.5448 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jnanadn/my-pet-dog-azm
|
jnanadn
| 2023-12-05T12:46:17Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-05T12:43:59Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-azm Dreambooth model trained by jnanadn following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21AME018
Sample pictures of this concept:
.jpg)
|
alexviol/whisper-small-bn
|
alexviol
| 2023-12-05T12:38:46Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:bangla-speech-processing/BanglaASR",
"base_model:finetune:bangla-speech-processing/BanglaASR",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-05T11:06:42Z |
---
license: mit
base_model: bangla-speech-processing/BanglaASR
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-bn
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-bn
This model is a fine-tuned version of [bangla-speech-processing/BanglaASR](https://huggingface.co/bangla-speech-processing/BanglaASR) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4061
- Wer: 40.6689
## 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: 26
- eval_batch_size: 26
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4439 | 0.15 | 100 | 0.4061 | 40.6689 |
| 0.1294 | 0.3 | 200 | 0.1215 | 99.9192 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
UmerHA/baby-sd
|
UmerHA
| 2023-12-05T12:38:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-05T12:37:17Z |
A pipeline with a tiny, untrained Unet for testing purposes
|
thaddeusjulio/swin-base-patch4-window7-224-finetuned-lora-scenes
|
thaddeusjulio
| 2023-12-05T12:08:49Z | 6 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:adapter:microsoft/swin-base-patch4-window7-224",
"region:us"
] | null | 2023-12-05T11:31:10Z |
---
library_name: peft
base_model: microsoft/swin-base-patch4-window7-224
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
Sagicc/whisper-large-v3-sr-combined
|
Sagicc
| 2023-12-05T11:59:16Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"sr",
"dataset:mozilla-foundation/common_voice_13_0",
"dataset:google/fleurs",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-09T18:28:05Z |
---
language:
- sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Large v3 Sr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 0.05560382276281494
---
<!-- 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. -->
# UPDATE
Use an updated fine tunned version [Sagicc/whisper-large-v3-sr-cmb](https://huggingface.co/Sagicc/whisper-large-v3-sr-cmb) with new 50+ hours of dataset.
# Whisper Large v3 Sr
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on Serbian Mozilla/Common Voice 13 and Google/Fleurs datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1628
- Wer Ortho: 0.1635
- Wer: 0.0556
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0567 | 1.34 | 500 | 0.1512 | 0.1676 | 0.0717 |
| 0.0256 | 2.67 | 1000 | 0.1482 | 0.1585 | 0.0610 |
| 0.0114 | 4.01 | 1500 | 0.1628 | 0.1635 | 0.0556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
SidXXD/CD_flw_2_256-Ppt_flw
|
SidXXD
| 2023-12-05T11:58:16Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-05T11:54:12Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: photo of a <new1> flower
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - SidXXD/CD_flw_2_256-Ppt_flw
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> flower using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
FremyCompany/roberta-large-nl-oscar23
|
FremyCompany
| 2023-12-05T11:57:21Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-05T06:32:59Z |
# RobBERT-2023: Keeping Dutch Language Models Up-To-Date
RobBERT-2023 is the 2023 release of the [Dutch RobBERT model](https://pieter.ai/robbert/).
It is a new version of original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) model on the 2023 version of the OSCAR version.
We release a base model, but this time we also release an additional large model with 355M parameters (x3 over robbert-2022-base). We are particularly proud of the performance of both models, surpassing both the robbert-v2-base and robbert-2022-base models with +2.9 and +0.9 points on the [DUMB benchmark](https://dumbench.nl) from GroNLP. In addition, we also surpass BERTje with +18.6 points with `robbert-2023-dutch-large`.
This is the same model with the same weights as [`DTAI-KULeuven/robbert-2023-dutch-large`](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-large).
|
lnxdx/B2_1000_1e-5_hp-mehrdad
|
lnxdx
| 2023-12-05T11:55:12Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-04T17:02:01Z |
---
base_model: lnxdx/21_2500_1e-4_hp-mehrdad
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 2_1000_1e-5_hp-mehrdad
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. -->
# 2_1000_1e-5_hp-mehrdad
This model is a fine-tuned version of [lnxdx/21_2500_1e-4_hp-mehrdad](https://huggingface.co/lnxdx/21_2500_1e-4_hp-mehrdad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss on ShEMO train set: 0.6809
- Loss on ShEMO dev set: 0.6591
- WER on ShEMO train set: 27.41
- WER on ShEMO dev set: 31.37 (Why not 31.36?)
- WER on Common Voice 13 test set: 19.26
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7624 | 0.62 | 100 | 0.6708 | 0.3236 |
| 0.78 | 1.25 | 200 | 0.6668 | 0.3245 |
| 0.7856 | 1.88 | 300 | 0.6600 | 0.3274 |
| 0.7239 | 2.5 | 400 | 0.6672 | 0.3233 |
| 0.7311 | 3.12 | 500 | 0.6748 | 0.3143 |
| 0.7408 | 3.75 | 600 | 0.6518 | 0.3248 |
| 0.713 | 4.38 | 700 | 0.6587 | 0.3178 |
| 0.7068 | 5.0 | 800 | 0.6600 | 0.3172 |
| 0.6938 | 5.62 | 900 | 0.6598 | 0.3157 |
| 0.6809 | 6.25 | 1000 | 0.6591 | 0.3137 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
lnxdx/B4_1000_1e-5_hp-myself-2
|
lnxdx
| 2023-12-05T11:53:41Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO",
"base_model:finetune:lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-04T18:49:46Z |
---
base_model: lnxdx/20_2000_1e-5_hp-mehrdad
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: B4_1000_1e-5_hp-myself-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# B4_1000_1e-5_hp-myself-2
This model is a fine-tuned version of [lnxdx/20_2000_1e-5_hp-mehrdad](https://huggingface.co/lnxdx/20_2000_1e-5_hp-mehrdad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss on ShEMO train set: 0.7516
- Loss on ShEMO dev set: 0.6705
- WER on ShEMO train set: 28.02
- WER on ShEMO dev set: 31.16
- WER on Common Voice 13 test set: 19.34
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8083 | 0.62 | 100 | 0.6766 | 0.3271 |
| 0.8414 | 1.25 | 200 | 0.6774 | 0.3259 |
| 0.8465 | 1.88 | 300 | 0.6686 | 0.3262 |
| 0.7819 | 2.5 | 400 | 0.6749 | 0.3207 |
| 0.7905 | 3.12 | 500 | 0.6848 | 0.3178 |
| 0.8078 | 3.75 | 600 | 0.6571 | 0.3245 |
| 0.7771 | 4.38 | 700 | 0.6683 | 0.3145 |
| 0.7786 | 5.0 | 800 | 0.6688 | 0.3137 |
| 0.7656 | 5.62 | 900 | 0.6703 | 0.3134 |
| 0.7516 | 6.25 | 1000 | 0.6706 | 0.3131 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Rakoo04/corgy_dog_LoRA
|
Rakoo04
| 2023-12-05T11:48:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-05T11:48:42Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - Rakoo04/corgy_dog_LoRA
<Gallery />
## Model description
These are Rakoo04/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Rakoo04/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
|
Arabic-Clip-Archive/arabertv2-Vit-B-16-plus-epoch-60-trained-mscoco-training-fp32
|
Arabic-Clip-Archive
| 2023-12-05T11:48:31Z | 7 | 0 |
keras
|
[
"keras",
"pytorch",
"tf",
"bert",
"region:us"
] | null | 2023-11-04T11:43:38Z |
https://wandb.ai/uos_mlalp/mscoco_teacher_learning_full_data/runs/jy36eur7?workspace=user-malbarham
|
datleviet/ComOM-VIDeBERTa-3
|
datleviet
| 2023-12-05T11:45:03Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:Fsoft-AIC/videberta-base",
"base_model:finetune:Fsoft-AIC/videberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-05T11:37:21Z |
---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ComOM-VIDeBERTa-3
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. -->
# ComOM-VIDeBERTa-3
This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0971
- Precision: 0.1319
- Recall: 0.1029
- F1: 0.1156
- Accuracy: 0.6647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 78 | 1.2306 | 0.0768 | 0.0370 | 0.0499 | 0.6486 |
| No log | 2.0 | 156 | 1.1902 | 0.0755 | 0.0609 | 0.0674 | 0.6407 |
| No log | 3.0 | 234 | 1.1627 | 0.0923 | 0.0679 | 0.0783 | 0.6499 |
| No log | 4.0 | 312 | 1.1489 | 0.1159 | 0.0879 | 0.1000 | 0.6530 |
| No log | 5.0 | 390 | 1.1219 | 0.0997 | 0.0749 | 0.0856 | 0.6529 |
| No log | 6.0 | 468 | 1.1130 | 0.1245 | 0.0879 | 0.1030 | 0.6589 |
| 1.0673 | 7.0 | 546 | 1.1095 | 0.1247 | 0.0919 | 0.1058 | 0.6600 |
| 1.0673 | 8.0 | 624 | 1.0971 | 0.1319 | 0.1029 | 0.1156 | 0.6647 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.