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text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - janetsw/sca
These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1-base. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion", "diffusers-training"], "base_model": "stabilityai/stable-diffusion-2-1-base", "inference": true} | janetsw/sca | null | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-28T15:01:15+00:00 |
null | transformers | {} | MustafaToprak/beit-base-patch16-224-in21k | null | [
"transformers",
"vit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:02:11+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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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
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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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. -->
**BibTeX:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/final20 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:02:15+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
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[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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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[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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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | whizhamza/mistral_7b_emotional_support | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:03:26+00:00 |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Undi95/Llama-3-LewdPlay-8B-evo](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B-evo) as a base.
### Models Merged
The following models were included in the merge:
* [Trelis/Meta-Llama-3-8B-Instruct-function-calling](https://huggingface.co/Trelis/Meta-Llama-3-8B-Instruct-function-calling)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Undi95/Llama-3-LewdPlay-8B-evo
# No parameters necessary for base model
- model: Trelis/Meta-Llama-3-8B-Instruct-function-calling
parameters:
density: 0.53
weight: 0.4
merge_method: dare_ties
base_model: Undi95/Llama-3-LewdPlay-8B-evo
parameters:
int8_mask: true
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Undi95/Llama-3-LewdPlay-8B-evo", "Trelis/Meta-Llama-3-8B-Instruct-function-calling"]} | Jebadiah/Llama-3-8B-source-lewd-function-calling | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Undi95/Llama-3-LewdPlay-8B-evo",
"base_model:Trelis/Meta-Llama-3-8B-Instruct-function-calling",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:03:47+00:00 |
null | null | {} | Schnorchelgubby/ShaneMorris | null | [
"region:us"
] | null | 2024-04-28T15:04:07+00:00 |
|
automatic-speech-recognition | transformers |
# Latvian Whisper tiny speech recognition model
Trained on combination of:
- Common Voice 17, custom selection of all validated clips, max 1000 clips per speaker
- Fleurs, test+train+validation
Both regular whisper model and CTranslate2 converted version for use with [faster-whisper](https://github.com/SYSTRAN/faster-whisper) as part of [Home Assistant Whisper integration](https://www.home-assistant.io/integrations/whisper/) are available.
Speech recognition quality is poor, more data is needed, donate your voice on [Balsu talka](https://balsutalka.lv/)
For better recognition quality use [whisper-small-lv](https://huggingface.co/RaivisDejus/whisper-small-lv) model, it is noticeably better and only slightly slower. | {"language": ["lv"], "license": "apache-2.0", "tags": ["Whisper"], "metrics": [{"name": "wer", "type": "wer", "value": 21.96}], "pipeline_tag": "automatic-speech-recognition"} | RaivisDejus/whisper-tiny-lv | null | [
"transformers",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"Whisper",
"lv",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-28T15:05:03+00:00 |
question-answering | transformers |
This model is a fine-tuned version of: https://huggingface.co/ali77sina/SECGPT
The instruction set is defined as follows:
```python
text = f"""### Question: {example['questions'][i]},
### Context: {example['sorted_chunks'][i]},
### Answer: {example['answers'][i]}"""
``` | {"license": "apache-2.0", "tags": ["finance"], "datasets": ["ali77sina/SEC-QA-sorted-chunks"], "pipeline_tag": "question-answering"} | ali77sina/SECGPT-FT-RAG | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"finance",
"question-answering",
"dataset:ali77sina/SEC-QA-sorted-chunks",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:05:54+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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]
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | kssumanth6/t5_small_sentence_polishing_generator_v3 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:09:17+00:00 |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8658
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1505 | 0.8246 |
| 0.1268 | 2.0 | 1050 | 0.1380 | 0.8503 |
| 0.0794 | 3.0 | 1575 | 0.1363 | 0.8658 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": []}]} | prl90777/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:09:24+00:00 |
null | null | {} | ToeBoe/ulya2 | null | [
"region:us"
] | null | 2024-04-28T15:10:05+00:00 |
|
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-PEPTIDE_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:10:51+00:00 |
|
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-PROPEP_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:11:39+00:00 |
|
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Olethros-8B/resolve/main/olethros.png>
L3-8b-Instruct tuned on roughly 6000 Opus generations in the hopes of adding a bit of sovl. | {"license": "llama3", "datasets": ["lodrick-the-lafted/OpusStories", "lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K", "lodrick-the-lafted/Samantha-Opus", "lodrick-the-lafted/Worldsim-Opus"]} | blockblockblock/Olethros-8B-bpw2.25-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/OpusStories",
"dataset:lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K",
"dataset:lodrick-the-lafted/Samantha-Opus",
"dataset:lodrick-the-lafted/Worldsim-Opus",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:13:18+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/stablecell_v46 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:13:35+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **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]
#### 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]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Linxier/opt-125m-gptq-4bit | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-28T15:14:57+00:00 |
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-SIGNAL_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:15:47+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# tmp_trainer
This model is a fine-tuned version of [ali77sina/SECGPT](https://huggingface.co/ali77sina/SECGPT) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "ali77sina/SECGPT", "model-index": [{"name": "tmp_trainer", "results": []}]} | ali77sina/tmp_trainer | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:ali77sina/SECGPT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:15:48+00:00 |
null | null | {} | jacklangerman/s23dr_v0 | null | [
"region:us"
] | null | 2024-04-28T15:15:56+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [Jebadiah/Llama-3-8B-source-lewd-function-calling](https://huggingface.co/Jebadiah/Llama-3-8B-source-lewd-function-calling) as a base.
### Models Merged
The following models were included in the merge:
* [Jebadiah/Llama-3-8B-source-lewd-context](https://huggingface.co/Jebadiah/Llama-3-8B-source-lewd-context)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
base_model: Jebadiah/Llama-3-8B-source-lewd-function-calling
parameters:
normalize: true
models:
- model: Jebadiah/Llama-3-8B-source-lewd-context
parameters:
weight: 0.5
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Jebadiah/Llama-3-8B-source-lewd-context", "Jebadiah/Llama-3-8B-source-lewd-function-calling"]} | Jebadiah/Llama-3-8B-source-lewd-context-function-calling | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:Jebadiah/Llama-3-8B-source-lewd-context",
"base_model:Jebadiah/Llama-3-8B-source-lewd-function-calling",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:16:33+00:00 |
null | null | {"license": "apache-2.0"} | gao-NLP/Llama3-8x8b-MoE-Instruct | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:17:11+00:00 |
|
null | diffusers | {} | justin-shopcapsule/ddpm-belts-256 | null | [
"diffusers",
"tensorboard",
"safetensors",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-28T15:17:50+00:00 |
|
null | null | {} | gao-NLP/Llama3-8x8b-MoE-Base | null | [
"region:us"
] | null | 2024-04-28T15:17:50+00:00 |
|
null | diffusers | {} | justin-shopcapsule/ddpm-belts-128 | null | [
"diffusers",
"tensorboard",
"safetensors",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-28T15:18:23+00:00 |
|
null | null | {} | ivykopal/czech_adapter_cssquad_adapter_100k | null | [
"region:us"
] | null | 2024-04-28T15:19:29+00:00 |
|
text-generation | transformers | {} | w32zhong/s3d-full_finetune_layer728 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:21:25+00:00 |
|
null | null | {} | vclansience/instantid | null | [
"region:us"
] | null | 2024-04-28T15:21:34+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/nq0nnpj | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:22:05+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | kawagoshi-llm-team/12B_step600 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:22:21+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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[More Information Needed]
## Training Details
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[More Information Needed]
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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]
- **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] | {"library_name": "transformers", "tags": []} | shallow6414/nz0g7k6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:23:07+00:00 |
null | null | {} | Filipeqe/skullboypl | null | [
"region:us"
] | null | 2024-04-28T15:24:39+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** logmate
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | logmate/llama_script | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:25:10+00:00 |
text-generation | transformers | {} | w32zhong/s3d-full_finetune_layer324 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:25:44+00:00 |
|
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Olethros-8B/resolve/main/olethros.png>
L3-8b-Instruct tuned on roughly 6000 Opus generations in the hopes of adding a bit of sovl. | {"license": "llama3", "datasets": ["lodrick-the-lafted/OpusStories", "lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K", "lodrick-the-lafted/Samantha-Opus", "lodrick-the-lafted/Worldsim-Opus"]} | blockblockblock/Olethros-8B-bpw2.5-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/OpusStories",
"dataset:lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K",
"dataset:lodrick-the-lafted/Samantha-Opus",
"dataset:lodrick-the-lafted/Worldsim-Opus",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:27:19+00:00 |
null | null | {"license": "openrail"} | Loren85/giorgio-vanni-new-version | null | [
"license:openrail",
"region:us"
] | null | 2024-04-28T15:27:25+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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
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[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card if possible. -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | justinkarlin/idefics-9b-faces | null | [
"transformers",
"safetensors",
"idefics",
"pretraining",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-28T15:29:25+00:00 |
null | null | {} | Eduarte/bluePencil | null | [
"region:us"
] | null | 2024-04-28T15:30:17+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/1zy7yg6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:30:19+00:00 |
null | null | {} | asude55/android1-sentiment | null | [
"region:us"
] | null | 2024-04-28T15:30:51+00:00 |
|
text-generation | transformers | {} | mwaterl/llama-2-7b-custom-2 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:32:32+00:00 |
|
null | null | {"license": "apache-2.0"} | szhou95/codebert_finetuned_defect_prediction | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:32:44+00:00 |
|
text-generation | transformers | {} | w32zhong/s3d-full_finetune_layer122 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:33:14+00:00 |
|
sentence-similarity | null |
### Model Description
Machine learning models like [tensorflow-compress](https://www.mattmahoney.net/dc/text.html) which uses LSTM to compress text to achieve remarkable compression ratio with less maintenance on codes.
This model was trained with the *dynamic sapient technology*, it was SentencePiece unigram with the dataset [go_emotion](https://huggingface.co/datasets/go_emotions), and it can compress the bits much better than RLE.
- **Developed by:** Ziv Arin
- **Model type:** Sentence similarity lossless compression
- **License:** CC0-1.0
### Demo
Example bitarray (384-bit): 000000000000000000000010000000000000000000000000000000100010010000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000100000000000000000000000000100000000000000000000000000000000000000100000000000001000000000000000000000000001000001000
Compressed (208-bit): 1ab2ed09d7a9617206894e0608 (45.83% space-saving efficiency)
[The notebook:](https://huggingface.co/baiango/384_bit_comp/blob/main/384_bit_comp.ipynb)
```py
import sentencepiece as spm
bpe_processor = spm.SentencePieceProcessor(model_file='384_bit_comp.model')
def encode_id(bit_text):
encoded_pieces = bpe_processor.encode_as_pieces(bit_text)
encoded_ids = [bpe_processor.piece_to_id(s) - 3 for s in encoded_pieces]
assert any([id_ <= 255 for id_ in encoded_ids])
string_ids = "".join([format(id_, "02x") for id_ in encoded_ids])
return string_ids
def decode_id(hex_string):
u8_array = np.frombuffer(bytes.fromhex(hex_string), dtype='<u1') + 3
encoded_tokens = [bpe_processor.id_to_piece(int(id_)) for id_ in u8_array]
return encoded_tokens
# Encode text
new_sentence = "000000000000000000000010000000000000000000000000000000100010010000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000100000000000000000000000000100000000000000000000000000000000000000100000000000001000000000000000000000000001000001000"
encoded_tokens = bpe_processor.encode_as_pieces(new_sentence)
encoded_ids = encode_id(new_sentence)
decoded_tokens = decode_id(encoded_ids)
print("length:", len(encoded_tokens))
print("encoded_tokens:", encoded_tokens)
print("encoded_ids:", encoded_ids)
print("same?:", encoded_tokens == decoded_tokens)
count = Counter(encoded_tokens)
print("count:", count)
```
Output:
```
length: 13
encoded_tokens: ['▁0000000', '0000000000000001000000000000000000000', '00000000001000100', '1000000', '00000000000000000000000000000001000000000000000000000000000000000000000000000000000000', '00000000000000000001000000000000000000000000000000000', '0000000000000000000000000000000001000', '00000000000000000000000100000000000000000', '00000000010', '0000000000000000000000000000000000000100', '00000000000100000000000000000', '00000000010', '00001000']
encoded_ids: 1ab2ed09d7a9617206894e0608
same?: True
count: Counter({'00000000010': 2, '▁0000000': 1, '0000000000000001000000000000000000000': 1, '00000000001000100': 1, '1000000': 1, '00000000000000000000000000000001000000000000000000000000000000000000000000000000000000': 1, '00000000000000000001000000000000000000000000000000000': 1, '0000000000000000000000000000000001000': 1, '00000000000000000000000100000000000000000': 1, '0000000000000000000000000000000000000100': 1, '00000000000100000000000000000': 1, '00001000': 1})
```
## Bias, Risks, and Limitations
It doesn't have any sentient bias, except algorithmic bias. Don't worry about it, it's not a living thing.
The model doesn't compress well strings with fewer zeros.
## Environmental Impact
- **Hardware Type:** I5-9300H
- **Hours used:** 3 hours | {"license": "cc0-1.0", "datasets": ["go_emotions"], "pipeline_tag": "sentence-similarity"} | baiango/384_bit_comp | null | [
"sentence-similarity",
"dataset:go_emotions",
"license:cc0-1.0",
"region:us"
] | null | 2024-04-28T15:33:21+00:00 |
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-TRANSIT_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:33:32+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the linear [DARE](https://arxiv.org/abs/2311.03099) merge method using [Nitral-AI/Echidna-7b-128k](https://huggingface.co/Nitral-AI/Echidna-7b-128k) as a base.
### Models Merged
The following models were included in the merge:
* [Jebadiah/Llama-3-8B-source-lewd-context-function-calling](https://huggingface.co/Jebadiah/Llama-3-8B-source-lewd-context-function-calling)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Nitral-AI/Echidna-7b-128k
# no parameters necessary for base model
- model: Jebadiah/Llama-3-8B-source-lewd-context-function-calling
parameters:
density: 0.2
weight: 0.3
merge_method: dare_linear
base_model: Nitral-AI/Echidna-7b-128k
parameters:
normalize: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Jebadiah/Llama-3-8B-source-lewd-context-function-calling", "Nitral-AI/Echidna-7b-128k"]} | Jebadiah/Aria-7b-128k-v1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"custom_code",
"arxiv:2311.03099",
"base_model:Jebadiah/Llama-3-8B-source-lewd-context-function-calling",
"base_model:Nitral-AI/Echidna-7b-128k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:34:01+00:00 |
null | transformers |
# Model Card for Model ID
Fine-tuned Llama3-8b model with Lora (trained with max_steps=300 on colap T4 for experimental purposes)
Base Model: unsloth/llama-3-8b-bnb-4bit
Fine-tuning process https://www.youtube.com/watch?v=pK8u4QfdLx0&ab_channel=DavidOndrej
Fine-tuning data : tolgadev/turkish_73k_instruct_extended
| {"library_name": "transformers", "tags": ["unsloth"]} | Yudum/llama3-lora-turkish | null | [
"transformers",
"safetensors",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:34:30+00:00 |
null | transformers | {"license": "apache-2.0"} | lINoRIl/mistral-q4 | null | [
"transformers",
"gguf",
"mistral",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:34:49+00:00 |
|
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-STRAND_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:36:40+00:00 |
|
null | null | {} | ivykopal/slovak_prompt_sksquad_adapter_100k | null | [
"region:us"
] | null | 2024-04-28T15:37:25+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/JunhuuteyRaden | null | [
"license:openrail",
"region:us"
] | null | 2024-04-28T15:38:01+00:00 |
|
text-generation | transformers | {} | w32zhong/s3d-full_finetune_alternate3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:39:07+00:00 |
|
null | null |
## Introduce
Quantizing the [UnicomLLM/Unichat-llama3-Chinese-8B](https://huggingface.co/UnicomLLM/Unichat-llama3-Chinese-8B) to f16, q2, q3, q4, q5, q6 and q8 with Llama.cpp.
## Prompt template
```
{system_message}
Human: {prompt}
Assistant:
``` | {"license": "apache-2.0"} | Monor/Unichat-llama3-Chinese-8B-gguf | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:39:09+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | RobertML/sn6e | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:40:23+00:00 |
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Olethros-8B/resolve/main/olethros.png>
L3-8b-Instruct tuned on roughly 6000 Opus generations in the hopes of adding a bit of sovl. | {"license": "llama3", "datasets": ["lodrick-the-lafted/OpusStories", "lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K", "lodrick-the-lafted/Samantha-Opus", "lodrick-the-lafted/Worldsim-Opus"]} | blockblockblock/Olethros-8B-bpw3-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/OpusStories",
"dataset:lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K",
"dataset:lodrick-the-lafted/Samantha-Opus",
"dataset:lodrick-the-lafted/Worldsim-Opus",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"3-bit",
"region:us"
] | null | 2024-04-28T15:41:25+00:00 |
null | null | {"license": "mit"} | pbaodoge/ryo | null | [
"license:mit",
"region:us"
] | null | 2024-04-28T15:41:51+00:00 |
|
null | null | {"license": "apache-2.0"} | MubarakB/echo | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:43:14+00:00 |
|
null | peft |
<!-- 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. -->
# GenAI-task2-ModelD-DS
This model is a fine-tuned version of [petals-team/falcon-rw-1b](https://huggingface.co/petals-team/falcon-rw-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6983
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5185 | 0.0316 | 20 | 1.4331 |
| 2.0381 | 0.0631 | 40 | 1.4158 |
| 2.0446 | 0.0947 | 60 | 1.3606 |
| 1.5993 | 0.1263 | 80 | 1.2881 |
| 1.7903 | 0.1579 | 100 | 1.2838 |
| 1.2226 | 0.1894 | 120 | 1.1627 |
| 1.4407 | 0.2210 | 140 | 1.1587 |
| 1.5104 | 0.2526 | 160 | 1.1219 |
| 1.1543 | 0.2841 | 180 | 1.0469 |
| 1.5322 | 0.3157 | 200 | 1.0498 |
| 1.0461 | 0.3473 | 220 | 0.9775 |
| 1.2949 | 0.3788 | 240 | 0.9830 |
| 1.3357 | 0.4104 | 260 | 0.9445 |
| 1.0266 | 0.4420 | 280 | 0.9118 |
| 1.3746 | 0.4736 | 300 | 0.9135 |
| 0.9231 | 0.5051 | 320 | 0.8550 |
| 1.21 | 0.5367 | 340 | 0.8641 |
| 1.3771 | 0.5683 | 360 | 0.8333 |
| 0.885 | 0.5998 | 380 | 0.8256 |
| 1.3633 | 0.6314 | 400 | 0.8445 |
| 0.8467 | 0.6630 | 420 | 0.7880 |
| 1.1924 | 0.6946 | 440 | 0.8053 |
| 1.152 | 0.7261 | 460 | 0.7812 |
| 0.8539 | 0.7577 | 480 | 0.7842 |
| 1.1079 | 0.7893 | 500 | 0.7932 |
| 0.7215 | 0.8208 | 520 | 0.7558 |
| 0.993 | 0.8524 | 540 | 0.7734 |
| 1.0678 | 0.8840 | 560 | 0.7496 |
| 0.8093 | 0.9155 | 580 | 0.7520 |
| 1.185 | 0.9471 | 600 | 0.7628 |
| 0.7553 | 0.9787 | 620 | 0.7391 |
| 1.0549 | 1.0103 | 640 | 0.7356 |
| 0.7007 | 1.0418 | 660 | 0.7312 |
| 1.1089 | 1.0734 | 680 | 0.7379 |
| 0.7699 | 1.1050 | 700 | 0.7222 |
| 0.808 | 1.1365 | 720 | 0.7227 |
| 0.995 | 1.1681 | 740 | 0.7198 |
| 0.684 | 1.1997 | 760 | 0.7142 |
| 0.9129 | 1.2313 | 780 | 0.7163 |
| 0.7775 | 1.2628 | 800 | 0.7110 |
| 0.8643 | 1.2944 | 820 | 0.7135 |
| 0.9359 | 1.3260 | 840 | 0.7096 |
| 0.728 | 1.3575 | 860 | 0.7108 |
| 0.9421 | 1.3891 | 880 | 0.7130 |
| 0.7606 | 1.4207 | 900 | 0.7042 |
| 0.9158 | 1.4522 | 920 | 0.7077 |
| 0.9677 | 1.4838 | 940 | 0.7045 |
| 0.6616 | 1.5154 | 960 | 0.7023 |
| 0.9689 | 1.5470 | 980 | 0.7024 |
| 0.8237 | 1.5785 | 1000 | 0.7010 |
| 0.8537 | 1.6101 | 1020 | 0.7034 |
| 1.0436 | 1.6417 | 1040 | 0.7014 |
| 0.6457 | 1.6732 | 1060 | 0.6999 |
| 0.8927 | 1.7048 | 1080 | 0.7000 |
| 0.7719 | 1.7364 | 1100 | 0.6991 |
| 0.7837 | 1.7680 | 1120 | 0.6989 |
| 1.0018 | 1.7995 | 1140 | 0.6988 |
| 0.6091 | 1.8311 | 1160 | 0.6984 |
| 0.9807 | 1.8627 | 1180 | 0.6984 |
| 0.8018 | 1.8942 | 1200 | 0.6983 |
| 0.7864 | 1.9258 | 1220 | 0.6983 |
| 0.8791 | 1.9574 | 1240 | 0.6983 |
| 0.8781 | 1.9890 | 1260 | 0.6983 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "petals-team/falcon-rw-1b", "model-index": [{"name": "GenAI-task2-ModelD-DS", "results": []}]} | Katochh/GenAI-task2-ModelD-DS | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:petals-team/falcon-rw-1b",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:43:17+00:00 |
null | null | <div align="center">
# EmoLLM-心理健康大模型
</div>
<p align="center">
<a href="https://github.com/aJupyter/EmoLLM/">
<img src="https://github.com/SmartFlowAI/EmoLLM/raw/main/assets/logo.jpeg" alt="Logo" width="30%">
</a>
<div align="center">
<!-- PROJECT SHIELDS -->
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Issues][issues-shield]][issues-url]
[![OpenXLab_App][OpenXLab_App-image]][OpenXLab_App-url]
[![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url]
[![MIT License][license-shield]][license-url]
[![Stargazers][stars-shield]][stars-url]
</div>
<h3 align="center">EmoLLM</h3>
<div align="center">
简体中文| <a href="README_EN.md" >English</a>
<br />
<br />
<a href="https://github.com/aJupyter/EmoLLM"><strong>探索本项目的文档 »</strong></a>
<br />
<br />
<a href="https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0">体验EmoLLM 2.0</a>
·
<a href="https://github.com/aJupyter/EmoLLM/issues">报告Bug</a>
·
<a href="https://github.com/aJupyter/EmoLLM/issues">提出新特性</a>
</div>
<!-- 本篇README.md面向开发者 -->
**EmoLLM** 是一系列能够支持 **理解用户-支持用户-帮助用户** 心理健康辅导链路的心理健康大模型，由 `LLM`指令微调而来，欢迎大家star~⭐⭐。目前已经开源的 `LLM` 微调配置如下：
<div align="center">
| 模型 | 类型 |
| :-------------------: | :------: |
| InternLM2_7B_chat | QLORA |
| InternLM2_7B_chat | 全量微调 |
| InternLM2_1_8B_chat | 全量微调 |
| InternLM2_20B_chat | LORA |
| Qwen_7b_chat | QLORA |
| Qwen1_5-0_5B-Chat | 全量微调 |
| Baichuan2_13B_chat | QLORA |
| ChatGLM3_6B | LORA |
| DeepSeek MoE_16B_chat | QLORA |
| Mixtral 8x7B_instruct | QLORA |
| …… | …… |
</div>
欢迎大家为本项目做出贡献~
---
心理健康大模型（Mental Health Grand Model）是一个综合性的概念，它旨在全面理解和促进个体、群体乃至整个社会的心理健康状态。这个模型通常包含以下几个关键组成部分：
- 认知因素：涉及个体的思维模式、信念系统、认知偏差以及解决问题的能力。认知因素对心理健康有重要影响，因为它们影响个体如何解释和应对生活中的事件。
- 情感因素：包括情绪调节、情感表达和情感体验。情感健康是心理健康的重要组成部分，涉及个体如何管理和表达自己的情感，以及如何从负面情绪中恢复。
- 行为因素：涉及个体的行为模式、习惯和应对策略。这包括应对压力的技巧、社交技能以及自我效能感，即个体对自己能力的信心。
- 社会环境：包括家庭、工作、社区和文化背景等外部因素，这些因素对个体的心理健康有着直接和间接的影响。
- 生理健康：身体健康与心理健康紧密相关。良好的身体健康可以促进心理健康，反之亦然。
- 心理韧性：指个体在面对逆境时的恢复力和适应能力。心理韧性强的人更能够从挑战中恢复，并从中学习和成长。
- 预防和干预措施：心理健康大模型还包括预防心理问题和促进心理健康的策略，如心理教育、心理咨询、心理治疗和社会支持系统。
- 评估和诊断工具：为了有效促进心理健康，需要有科学的工具来评估个体的心理状态，以及诊断可能存在的心理问题。
### 🎇最近更新
- 【2024.3.12】在百度飞浆平台发布[艾薇](https://aistudio.baidu.com/community/app/63335)
- 【2024.3.11】 **EmoLLM V2.0 相比 EmoLLM V1.0 全面提升，已超越 Role-playing ChatGPT 在心理咨询任务上的能力！**[点击体验EmoLLM V2.0](https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0)，更新[数据集统计及详细信息](./datasets/)、[路线图](./assets/Roadmap_ZH.png)
- 【2024.3.9】 新增并发功能加速 [QA 对生成](./scripts/qa_generation/)、[RAG pipeline](./rag/)
- 【2024.3.3】 [基于InternLM2-7B-chat全量微调版本EmoLLM V2.0开源](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full)，需要两块A100*80G，更新专业评估，详见[evaluate](./evaluate/)，更新基于PaddleOCR的PDF转txt工具脚本，详见[scripts](./scripts/)
- 【2024.2.29】更新客观评估计算，详见[evaluate](./evaluate/)，更新一系列数据集，详见[datasets](./datasets/)
- 【2024.2.27】更新英文readme和一系列数据集（舔狗和单轮对话）
- 【2024.2.23】推出基于InternLM2_7B_chat_qlora的 `温柔御姐心理医生艾薇`，[点击获取模型权重](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei)，[配置文件](xtuner_config/aiwei-internlm2_chat_7b_qlora.py)，[在线体验链接](https://openxlab.org.cn/apps/detail/ajupyter/EmoLLM-aiwei)
- 【2024.2.23】更新[若干微调配置](/xtuner_config/)，新增 [data_pro.json](/datasets/data_pro.json)（数量更多、场景更全、更丰富）和 [aiwei.json](/datasets/aiwei.json)（温柔御姐角色扮演专用，带有Emoji表情），即将推出 `温柔御姐心理医生艾薇`
- 【2024.2.18】 [基于Qwen1_5-0_5B-Chat全量微调版本开源](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary)，算力有限的道友可以玩起来~
<details>
<summary>查看更多</summary>
- 【2024.2.6】 EmoLLM在[**Openxlab** ](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model) 平台下载量高达18.7k，欢迎大家体验！
<p align="center">
<img src="https://github.com/aJupyter/EmoLLM/assets/62385492/7e931682-c54d-4ded-bc67-79130c68d744" alt="模型下载量">
</p>
- 【2024.2.5】 项目荣获公众号**NLP工程化**推文宣传[推文链接](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A)，为博主推广一波，欢迎大家关注！！🥳🥳
<p align="center">
<img src="https://github.com/aJupyter/EmoLLM/assets/62385492/47868d6a-2e91-4aa9-a630-e594c14295b4" alt="公众号二维码">
</p>
- 【2024.2.3】 [项目宣传视频](https://www.bilibili.com/video/BV1N7421N76X/)完成 😊
- 【2024.1.27】 完善数据构建文档、微调指南、部署指南、Readme等相关文档 👏
- 【2024.1.25】 EmoLLM V1.0 已部署上线 https://openxlab.org.cn/apps/detail/jujimeizuo/EmoLLM 😀
</details>
### 🎯路线图
<p align="center">
<a href="https://github.com/aJupyter/EmoLLM/">
<img src="https://github.com/SmartFlowAI/EmoLLM/raw/main/assets/Roadmap_ZH.png" alt="Roadmap_ZH">
</a>
## 目录
- [EmoLLM-心理健康大模型](#emollm-心理健康大模型)
- [🎇最近更新](#最近更新)
- [🎯路线图](#路线图)
- [目录](#目录)
- [开发前的配置要求](#开发前的配置要求)
- [**使用指南**](#使用指南)
- [数据构建](#数据构建)
- [微调指南](#微调指南)
- [部署指南](#部署指南)
- [RAG(检索增强生成)Pipeline](#rag检索增强生成pipeline)
- [使用到的框架](#使用到的框架)
- [如何参与本项目](#如何参与本项目)
- [作者（排名不分先后）](#作者排名不分先后)
- [版权说明](#版权说明)
- [特别鸣谢](#特别鸣谢)
- [Star History](#star-history)
- [🌟 Contributors](#-contributors)
- [交流群](#交流群)
###### 开发前的配置要求
- 硬件：A100 40G（仅针对InternLM2_7B_chat+qlora微调+deepspeed zero2优化）
###### **使用指南**
1. Clone the repo
```sh
git clone https://github.com/SmartFlowAI/EmoLLM.git
```
2. 依次阅读或者选择感兴趣的部分阅读：
- [数据构建](#数据构建)
- [微调指南](#微调指南)
- [部署指南](#部署指南)
- [RAG](#rag检索增强生成pipeline)
- 查看更多详情
### 数据构建
- 请阅读[数据构建指南](generate_data/tutorial.md)查阅
- 微调用到的数据集见[datasets](datasets/data.json)
### 微调指南
详见[微调指南](xtuner_config/README.md)
### 部署指南
- Demo部署：详见[部署指南](demo/README.md)
- 基于[LMDeploy](https://github.com/InternLM/lmdeploy/)的量化部署：详见[deploy](./deploy/lmdeploy.md)
### RAG(检索增强生成)Pipeline
- 详见[RAG](./rag/)
<details>
<summary>更多详情</summary>
### 使用到的框架
- [Xtuner](https://github.com/InternLM/xtuner)：用于微调
- [Transformers](https://github.com/huggingface/transformers)
- [Pytorch](https://pytorch.org/)
- [LMDeploy](https://github.com/InternLM/lmdeploy/)：用于量化部署
- [Stremlit](https://streamlit.io/)：用于构建Demo
- [DeepSpeed](https://github.com/microsoft/DeepSpeed)：并行训练
- …
#### 如何参与本项目
贡献使开源社区成为一个学习、激励和创造的绝佳场所。你所作的任何贡献都是**非常感谢**的。
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
</details>
### 作者（排名不分先后）
| 用户名 | 学校/组织 | 备注 | 贡献 |
| :----------: | :--------------------: | :-------------------: | :----------: |
| [aJupyter](https://github.com/aJupyter) | 南开大学在读硕士 | DataWhale成员 | 项目发起人 |
| [jujimeizuo](https://github.com/jujimeizuo) | 江南大学在读硕士 | | |
| [Smiling-Weeping-zhr](https://github.com/Smiling-Weeping-zhr) | 哈尔滨工业大学（威海）在读本科生 | | |
| [8baby8](https://github.com/8baby8) | 飞桨领航团区域主管 | 文心大模型核心开发者 | |
| [zxazys](https://github.com/zxazys) | 南开大学在读硕士 | | |
| [MING-ZCH](https://github.com/MING-ZCH) | 华中科技大学在读本科生 | | |
| [JasonLLLLLLLLLLL](https://github.com/JasonLLLLLLLLLLL) | swufe | | |
| [MrCatAI](https://github.com/MrCatAI) | AI搬用工 | | |
| [ZeyuBa](https://github.com/ZeyuBa) | 自动化所在读硕士 | | |
| [aiyinyuedejustin](https://github.com/aiyinyuedejustin) | 宾夕法尼亚大学在读硕士 | | |
| [Nobody-ML](https://github.com/Nobody-ML) | 中国石油大学（华东）在读本科生 | | |
| [chg0901](https://github.com/chg0901) | [MiniSora](https://github.com/mini-sora/minisora/) |MiniSora主要维护|数据清洗、文档翻译|
| [Mxoder](https://github.com/Mxoder) | 北京航空航天大学在读本科生 | | |
| [Anooyman](https://github.com/Anooyman) | 南京理工大学硕士 | | |
| [Vicky-3021](https://github.com/Vicky-3021) | 西安电子科技大学硕士（研0） | | |
| [SantiagoTOP](https://github.com/santiagoTOP) | 太原理工大学在读硕士 | | |
### 版权说明
该项目签署了 MIT 授权许可，详情请参阅 [LICENSE](https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE)
### 引用
如果本项目对您的工作有所帮助，请使用以下格式引用：
```bibtex
@misc{EmoLLM,
title={EmoLLM},
author={EmoLLM},
url={https://github.com/SmartFlowAI/EmoLLM/},
year={2024}
}
```
### 特别鸣谢
- [Sanbu](https://github.com/sanbuphy)
- [上海人工智能实验室](https://www.shlab.org.cn/)
- [闻星大佬（小助手）](https://github.com/vansin)
- [扫地升（公众号宣传）](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A)
- 阿布（北大心理学硕士）
<!-- links -->
<!-- [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555 -->
<!-- [linkedin-url]: https://linkedin.com/in/aJupyter -->
## Star History
[](https://star-history.com/#SmartFlowAI/EmoLLM&Date)
## 🌟 Contributors
[](https://github.com/SmartFlowAI/EmoLLM/graphs/contributors)
[your-project-path]: SmartflowAI/EmoLLM
[contributors-shield]: https://img.shields.io/github/contributors/SmartflowAI/EmoLLM.svg?style=flat-square
[contributors-url]: https://github.com/SmartflowAI/EmoLLM/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/SmartflowAI/EmoLLM.svg?style=flat-square
[forks-url]: https://github.com/SmartflowAI/EmoLLM/network/members
[stars-shield]: https://img.shields.io/github/stars/SmartflowAI/EmoLLM.svg?style=flat-square
[stars-url]: https://github.com/SmartflowAI/EmoLLM/stargazers
[issues-shield]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg?style=flat-square
[issues-url]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg
[license-shield]: https://img.shields.io/github/license/SmartflowAI/EmoLLM.svg?style=flat-square
[license-url]: https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE
[OpenXLab_App-image]: https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg
[OpenXLab_Model-image]: https://cdn-static.openxlab.org.cn/header/openxlab_models.svg
[OpenXLab_App-url]: https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0
[OpenXLab_Model-url]: https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full
## 交流群
- 如果失效，请移步Issue区
<p align="center">
<img width="30%" src="https://github.com/SmartFlowAI/EmoLLM/assets/62385492/55ecd0aa-4832-4269-ad57-4c26f9aa286b" alt="EmoLLM官方交流群">
</p>
| {} | chg0901/EmoLLM-Llama3-8B-Instruct3.0 | null | [
"region:us"
] | null | 2024-04-28T15:45:29+00:00 |
null | transformers | {} | wendys-llc/household-recipes-attempt-2 | null | [
"transformers",
"pytorch",
"mistral",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:46:45+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hossein0677/my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0318
- Validation Loss: 0.2818
- Train Accuracy: 0.9304
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1324 | 0.1952 | 0.9283 | 0 |
| 0.0649 | 0.2200 | 0.9301 | 1 |
| 0.0318 | 0.2818 | 0.9304 | 2 |
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "hossein0677/my_awesome_model", "results": []}]} | hossein0677/my_awesome_model | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:47:09+00:00 |
null | null | {} | asude55/android2-sentiment | null | [
"region:us"
] | null | 2024-04-28T15:47:32+00:00 |
|
null | null | {} | ivykopal/english_prompt_mlqa_adapter_100k | null | [
"region:us"
] | null | 2024-04-28T15:48:32+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [Nitral-AI/Echidna-7b-128k](https://huggingface.co/Nitral-AI/Echidna-7b-128k)
* [Jebadiah/Llama-3-8B-source-lewd-context-function-calling](https://huggingface.co/Jebadiah/Llama-3-8B-source-lewd-context-function-calling)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Nitral-AI/Echidna-7b-128k
parameters:
weight: 3.0
- model: Jebadiah/Llama-3-8B-source-lewd-context-function-calling
parameters:
weight: 0.3
merge_method: linear
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Nitral-AI/Echidna-7b-128k", "Jebadiah/Llama-3-8B-source-lewd-context-function-calling"]} | Jebadiah/Aria-7b-128k-v2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"custom_code",
"arxiv:2203.05482",
"base_model:Nitral-AI/Echidna-7b-128k",
"base_model:Jebadiah/Llama-3-8B-source-lewd-context-function-calling",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:49:44+00:00 |
null | null | {} | LeoHuaDY/test_cv | null | [
"region:us"
] | null | 2024-04-28T15:50:18+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** arthrod
- **License:** apache-2.0
- **Finetuned from model :** meta-llama/Meta-Llama-3-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"} | arthrod/ciceroptllamav1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-28T15:50:23+00:00 |
text-generation | transformers | {} | andrealexroom/LexLLMv0.0.0.x.10.24_041 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:51:55+00:00 |
|
null | null | {"license": "apache-2.0"} | Alina-ntnrml-frvr/Jesuslove | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:52:06+00:00 |
|
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Unclad3610/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | Unclad3610/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-28T15:52:21+00:00 |
null | null | {} | asude55/android3-sentiment | null | [
"region:us"
] | null | 2024-04-28T15:52:39+00:00 |
|
null | null | {"license": "apache-2.0"} | XyJu/XyText | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:53:03+00:00 |
|
text-generation | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | ajeya-op/autotrain-mf1bv-xfico | null | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:55:32+00:00 |
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Olethros-8B/resolve/main/olethros.png>
L3-8b-Instruct tuned on roughly 6000 Opus generations in the hopes of adding a bit of sovl. | {"license": "llama3", "datasets": ["lodrick-the-lafted/OpusStories", "lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K", "lodrick-the-lafted/Samantha-Opus", "lodrick-the-lafted/Worldsim-Opus"]} | blockblockblock/Olethros-8B-bpw3.5-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/OpusStories",
"dataset:lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K",
"dataset:lodrick-the-lafted/Samantha-Opus",
"dataset:lodrick-the-lafted/Worldsim-Opus",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:55:52+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | berkouille/assistant_DPO_10 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:56:11+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/itl8gvd | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:56:42+00:00 |
text-generation | transformers | {"license": "mit"} | 23tanmay/BioDistillGPT2 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:56:50+00:00 |
|
null | null | {} | NepNep13/test | null | [
"region:us"
] | null | 2024-04-28T15:56:57+00:00 |
|
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1639
- F1: 0.8591
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2836 | 1.0 | 715 | 0.1859 | 0.8212 |
| 0.1484 | 2.0 | 1430 | 0.1632 | 0.8487 |
| 0.0953 | 3.0 | 2145 | 0.1639 | 0.8591 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | prl90777/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:57:53+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet-swaghjal/model_out
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
You can find some example images below.
prompt: High-quality close-up dslr photo of man wearing a hat with trees in the background
prompt: Girl smiling, professional dslr photograph, dark background, studio lights, high quality
prompt: Portrait of a clown face, oil on canvas, bittersweet expression
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "controlnet", "diffusers-training"], "base_model": "stabilityai/stable-diffusion-2-1-base", "inference": true} | swaghjal/model_out | null | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-28T15:58:00+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jdqwoi/Mistral-dolphin-mix-cine-open-Ne
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF/resolve/main/Mistral-dolphin-mix-cine-open-Ne.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "jdqwoi/Mistral-dolphin-mix-cine-open", "NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story"], "base_model": "jdqwoi/Mistral-dolphin-mix-cine-open-Ne", "quantized_by": "mradermacher"} | mradermacher/Mistral-dolphin-mix-cine-open-Ne-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"jdqwoi/Mistral-dolphin-mix-cine-open",
"NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story",
"en",
"base_model:jdqwoi/Mistral-dolphin-mix-cine-open-Ne",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T15:58:43+00:00 |
null | transformers | {"license": "apache-2.0"} | lINoRIl/llama_3_4bitQ | null | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T15:59:39+00:00 |
|
null | peft |
<!-- 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. -->
# style-mixed-gorrila
This model is a fine-tuned version of [gorilla-llm/gorilla-openfunctions-v2](https://huggingface.co/gorilla-llm/gorilla-openfunctions-v2) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "gorilla-llm/gorilla-openfunctions-v2", "model-index": [{"name": "style-mixed-gorrila", "results": []}]} | RuoxiL/style-mixed-gorrila | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:gorilla-llm/gorilla-openfunctions-v2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T15:59:43+00:00 |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Nitral-AI/Echidna-7b-128k](https://huggingface.co/Nitral-AI/Echidna-7b-128k) as a base.
### Models Merged
The following models were included in the merge:
* [Jebadiah/Aria-7b-128k-v2](https://huggingface.co/Jebadiah/Aria-7b-128k-v2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Jebadiah/Aria-7b-128k-v2
parameters:
density: 0.6
weight: 0.5
merge_method: dare_ties
base_model: Nitral-AI/Echidna-7b-128k
parameters:
normalize: false
int8_mask: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Nitral-AI/Echidna-7b-128k", "Jebadiah/Aria-7b-128k-v2"]} | Jebadiah/Aria-7b-128k-v3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"custom_code",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Nitral-AI/Echidna-7b-128k",
"base_model:Jebadiah/Aria-7b-128k-v2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T16:01:12+00:00 |
text-classification | transformers |
# bert-drug-review-to-condition
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on this dataset: Kallumadi,Surya and Grer,Felix. (2018). Drug Reviews (Drugs.com). UCI Machine Learning Repository. https://doi.org/10.24432/C5SK5S.
It achieves the following results on the evaluation set:
- Loss: 0.6678
- Accuracy: 0.8376
- Precision: 0.8325
- Recall: 0.8376
- F1: 0.8317
## Model description
"bert-base-uncased" fine-tuned for text-classification (multiclass): from input text, the model outputs the most likely medical pathology of the person. Training based on predicting 'condition' feature from 'review' feature (i.e., the person reviews the drugs they are taking for their condition)
## Intended uses & limitations
Personal project
## Training and evaluation data
The 100 most frequent conditions of the dataset are selected:
{0: 'multiple sclerosis', 1: 'overactive bladde', 2: 'hyperhidrosis', 3: 'ibromyalgia', 4: 'menstrual disorders', 5: 'hypogonadism, male', 6: 'rosacea', 7: 'muscle spasm', 8: 'high blood pressure', 9: 'epilepsy', 10: 'psoriatic arthritis', 11: 'post traumatic stress disorde', 12: 'smoking cessation', 13: 'not listed / othe', 14: 'herpes simplex', 15: 'opiate dependence', 16: 'social anxiety disorde', 17: 'urticaria', 18: 'allergic rhinitis', 19: 'polycystic ovary syndrome', 20: 'obsessive compulsive disorde', 21: 'depression', 22: 'migraine prevention', 23: 'neuropathic pain', 24: 'ankylosing spondylitis', 25: 'skin or soft tissue infection', 26: 'constipation, drug induced', 27: 'obesity', 28: 'vaginal yeast infection', 29: 'osteoarthritis', 30: 'restless legs syndrome', 31: 'plaque psoriasis', 32: 'panic disorde', 33: 'abnormal uterine bleeding', 34: 'adhd', 35: 'high cholesterol', 36: 'diabetes, type 2', 37: 'anxiety and stress', 38: 'asthma, maintenance', 39: 'pneumonia', 40: 'schizophrenia', 41: 'opiate withdrawal', 42: 'osteoporosis', 43: 'influenza', 44: 'weight loss', 45: 'cough and nasal congestion', 46: 'birth control', 47: 'benign prostatic hyperplasia', 48: 'helicobacter pylori infection', 49: 'anxiety', 50: 'bronchitis', 51: 'rheumatoid arthritis', 52: 'narcolepsy', 53: 'generalized anxiety disorde', 54: 'insomnia', 55: 'nasal congestion', 56: 'major depressive disorde', 57: 'schizoaffective disorde', 58: 'psoriasis', 59: 'premenstrual dysphoric disorde', 60: 'bacterial vaginitis', 61: 'motion sickness', 62: 'erectile dysfunction', 63: 'constipation, chronic', 64: 'copd, maintenance', 65: 'back pain', 66: 'alcohol dependence', 67: 'migraine', 68: 'bladder infection', 69: 'underactive thyroid', 70: 'ulcerative colitis', 71: 'chronic pain', 72: 'hiv infection', 73: 'cold sores', 74: 'breast cance', 75: 'bipolar disorde', 76: 'irritable bowel syndrome', 77: 'anesthesia', 78: 'onychomycosis, toenail', 79: 'chlamydia infection', 80: 'gerd', 81: 'endometriosis', 82: 'seizures', 83: 'alcohol withdrawal', 84: 'bowel preparation', 85: 'hot flashes', 86: 'bacterial infection', 87: 'inflammatory conditions', 88: 'constipation', 89: 'headache', 90: 'urinary tract infection', 91: 'sinusitis', 92: 'emergency contraception', 93: 'cough', 94: 'acne', 95: 'atrial fibrillation', 96: 'pain', 97: 'nausea/vomiting', 98: 'hepatitis c', 99: 'postmenopausal symptoms'}
The 'review' feature is lowercased and are only selected examples with more than 16 characters.
## Training procedure
See code available at: https://github.com/mlafuentem/Marcuswas-bert-drug-review-to-condition/blob/main/Exercise_classification_conditions_code.ipynb
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8469 | 1.0 | 13390 | 0.8275 | 0.7673 | 0.7686 | 0.7673 | 0.7551 |
| 0.6319 | 2.0 | 26780 | 0.6895 | 0.8094 | 0.8090 | 0.8094 | 0.7978 |
| 0.4116 | 3.0 | 40170 | 0.6678 | 0.8376 | 0.8325 | 0.8376 | 0.8317 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "medical", "biology", "text-classification", "multiclass classification", "pathologies", "illness", "diagnose"], "datasets": ["Zakia/drugscom_reviews"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-drug-review-to-condition", "results": []}]} | Marcuswas/bert-drug-review-to-condition | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"medical",
"biology",
"multiclass classification",
"pathologies",
"illness",
"diagnose",
"en",
"dataset:Zakia/drugscom_reviews",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:02:01+00:00 |
null | null | {} | Roshith74/MyModel | null | [
"region:us"
] | null | 2024-04-28T16:02:04+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/a3f12wb | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T16:02:07+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Jebadiah/Aria-7b-128k-v2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Aria-7b-128k-v2-GGUF/resolve/main/Aria-7b-128k-v2.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "Jebadiah/Aria-7b-128k-v2", "quantized_by": "mradermacher"} | mradermacher/Aria-7b-128k-v2-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Jebadiah/Aria-7b-128k-v2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:02:39+00:00 |
text-generation | transformers | {} | pranavGenAI/ESG_RFP_Llama-2-7b-chat | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T16:02:40+00:00 |
|
null | peft |
<!-- 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. -->
# grad_ascent_2e-05_WikiMIA_QA_256_5
This model was trained from scratch 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 26
- training_steps: 133
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openlm-research/open_llama_7b", "model-index": [{"name": "grad_ascent_2e-05_WikiMIA_QA_256_5", "results": []}]} | lluvecwonv/grad_ascent_2e-05_WikiMIA_QA_256_5 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openlm-research/open_llama_7b",
"region:us"
] | null | 2024-04-28T16:03:26+00:00 |
null | null | {"license": "apache-2.0"} | JeffersonMusic/MJTIIDE2009 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T16:03:59+00:00 |
|
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Unclad3610/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | Unclad3610/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-28T16:05:17+00:00 |
null | null | {"license": "mit"} | dzdevel/clip | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2024-04-28T16:05:48+00:00 |
|
image-classification | transformers |
# test-image-classifier-mps
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Benjoyo/test-image-classifier-mps | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:05:54+00:00 |
null | null | {} | anushkat/my_awesome-model | null | [
"safetensors",
"region:us"
] | null | 2024-04-28T16:06:02+00:00 |
|
null | null | {} | 2star/tristana | null | [
"region:us"
] | null | 2024-04-28T16:07:22+00:00 |
|
fill-mask | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | sureshkm/camembert-base | null | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:07:30+00:00 |
null | transformers | {} | PurCL/codeart-binsim | null | [
"transformers",
"pytorch",
"codeart",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:07:45+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** np28work
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | np28work/mistral_func_calling | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:07:46+00:00 |
null | null | {"license": "mit"} | dzdevel/search | null | [
"license:mit",
"region:us"
] | null | 2024-04-28T16:08:19+00:00 |
|
null | null |
<!-- 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. -->
# summarization-llama-2-finetuned
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the ssummarised/llama dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8379 | 1.0 | 1992 | 1.9292 |
| 1.8651 | 2.0 | 3984 | 1.9321 |
| 1.8298 | 3.0 | 5976 | 1.9534 |
| 1.3509 | 4.0 | 7968 | 1.9702 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "llama2", "tags": ["generated_from_trainer"], "datasets": ["ssummarised/llama"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "summarization-llama-2-finetuned", "results": []}]} | kev108/summarization-llama-2-finetuned | null | [
"safetensors",
"generated_from_trainer",
"dataset:ssummarised/llama",
"base_model:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-04-28T16:08:53+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** drgary
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | drgary/ft_llama3_athena2 | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T16:09:05+00:00 |
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