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WWWHH/ddd | WWWHH | 2024-07-02T14:38:51Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T14:38:51Z | ---
license: apache-2.0
---
|
NikolayKozloff/Viking-13B-Q4_0-GGUF | NikolayKozloff | 2024-07-02T14:42:32Z | 0 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation-inference",
"fi",
"en",
"da",
"sv",
"no",
"nn",
"is",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:mc4",
"base_model:LumiOpen/Viking-13B",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T14:39:14Z | ---
base_model: LumiOpen/Viking-13B
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- mc4
language:
- fi
- en
- da
- sv
- 'no'
- nn
- is
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
- text-generation-inference
---
# NikolayKozloff/Viking-13B-Q4_0-GGUF
This model was converted to GGUF format from [`LumiOpen/Viking-13B`](https://huggingface.co/LumiOpen/Viking-13B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LumiOpen/Viking-13B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Viking-13B-Q4_0-GGUF --hf-file viking-13b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Viking-13B-Q4_0-GGUF --hf-file viking-13b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Viking-13B-Q4_0-GGUF --hf-file viking-13b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Viking-13B-Q4_0-GGUF --hf-file viking-13b-q4_0.gguf -c 2048
``` |
yiyic/mt5_me5_cyrl-script_32_2layers_corrector | yiyic | 2024-07-02T14:41:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:40:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
csl-aaa/DSSG_sketch_recognition | csl-aaa | 2024-07-02T15:07:40Z | 0 | 0 | null | [
"onnx",
"graph-ml",
"license:wtfpl",
"region:us"
] | graph-ml | 2024-07-02T14:41:44Z | ---
license: wtfpl
pipeline_tag: graph-ml
---
This model is provided for the Unreal Engine Marketplace plugin: 'Neural network for sketch recognition'
It is also the official model of the paper 'DSSG: Spatiotemporal Sketch Representation for Graph Data'(ICIVIS-2024)
Q: How to use it?<br />
A: 1.Download the model 'onnx_sage_x-100-2_index-2-100-sim.onnx'.<br />
  2.Drag and drop into the Unreal Editor's content browser.<br />
  3.Double-click to select CPU runtime or other CPU related options.<br />
  4.Open the plugin: 'Neural network for sketch recognition' and follow the tutorial to select the model.<br /> |
yiyic/mt5_me5_arab-script_32_2layers_inverter | yiyic | 2024-07-02T14:43:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:43:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MrezaPRZ/codegemma_query_picker_new | MrezaPRZ | 2024-07-02T14:47:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | 2024-07-02T14:43:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF | wangxing666 | 2024-07-02T14:44:32Z | 0 | 0 | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Qwen/Qwen2-7B-Instruct",
"llama-cpp",
"gguf-my-repo",
"base_model:paperplanedeemo/Qwen2-14B-merge",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T14:43:58Z | ---
base_model: paperplanedeemo/Qwen2-14B-merge
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Qwen/Qwen2-7B-Instruct
- llama-cpp
- gguf-my-repo
---
# wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF
This model was converted to GGUF format from [`paperplanedeemo/Qwen2-14B-merge`](https://huggingface.co/paperplanedeemo/Qwen2-14B-merge) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/paperplanedeemo/Qwen2-14B-merge) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF --hf-file qwen2-14b-merge-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF --hf-file qwen2-14b-merge-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF --hf-file qwen2-14b-merge-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo wangxing666/Qwen2-14B-merge-Q4_K_M-GGUF --hf-file qwen2-14b-merge-q4_k_m.gguf -c 2048
```
|
liminerity/Bitnet-Mistral.0.2-330m-v0.2-grokfast-v2.9 | liminerity | 2024-07-02T17:59:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T14:44:14Z | Entry not found |
sgonzalezsilot/whisper-base-es-Nemo_unique_2024-07-02_14-44-25 | sgonzalezsilot | 2024-07-02T17:03:15Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T14:44:26Z | Entry not found |
zakiravian/bart_multinews | zakiravian | 2024-07-02T14:46:21Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-07-02T14:44:29Z | ---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: bart_multinews
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart_multinews
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
sgonzalezsilot/whisper-tiny-es-Nemo_unified_2024-07-02_14-45-29 | sgonzalezsilot | 2024-07-02T17:25:41Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T14:45:30Z | Entry not found |
mayarmostafa/videomae-base-finetuned-bleeding-exp_9 | mayarmostafa | 2024-07-02T15:27:39Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] | video-classification | 2024-07-02T14:46:13Z | Entry not found |
habulaj/525683499464 | habulaj | 2024-07-02T14:46:40Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:46:36Z | Entry not found |
ProElectro07/MeddBBoTT750x2 | ProElectro07 | 2024-07-02T14:47:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:46:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Abhiram4/PlantDiseaseDetectorV2 | Abhiram4 | 2024-07-02T16:58:28Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-07-02T14:47:18Z | Entry not found |
roscoechen/ollama-test | roscoechen | 2024-07-02T15:09:13Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-02T14:48:08Z | ---
license: mit
---
add
tar -czvf - file | openssl des3 -salt -k password -out /path/to/file.tar.gz
de
openssl des3 -d -k password -salt -in /path/to/file.tar.gz | tar xzf -
|
Dzy6/PolygonGNN | Dzy6 | 2024-07-02T01:11:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:48:08Z | # KDD24 PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph
data is on [dropbox](https://www.dropbox.com/scl/fo/f7dir04pldz36n6m47m30/ABxnZk8Qyf16k0Yo75WqXpY?rlkey=f3lhgyv7um323ngpa2bmueimq&st=e4wg0uec&dl=0)
|
ferrazzipietro/Llama-2-7b-chat-hfspecialTkn_en.layer1_NoQuant_32_32_0.02_8 | ferrazzipietro | 2024-07-02T14:49:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:48:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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Vaaly/llama3-business-central | Vaaly | 2024-07-02T14:49:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:49:22Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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kyynaama/Ahma-3B-toxic-dpo-qlora | kyynaama | 2024-07-02T15:16:14Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-02T14:49:54Z | Uses unalignment/toxic-dpo-v0.2 |
Salvatore/MyLoRA | Salvatore | 2024-07-02T14:55:48Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-07-02T14:49:59Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK person
widget: []
---
<!-- 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. -->
# SDXL LoRA DreamBooth - Salvatore/MyLoRA
<Gallery />
## Model description
These are Salvatore/MyLoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK person to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Salvatore/MyLoRA/tree/main) them in the Files & versions tab.
## 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] |
HAZIQIHSANQ/HAIQ | HAZIQIHSANQ | 2024-07-02T14:51:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T14:51:02Z | ---
license: apache-2.0
---
|
yiyic/mt5_me5_cmn_Hani_32_2layers_corrector | yiyic | 2024-07-02T14:53:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:52:45Z | ---
library_name: transformers
tags: []
---
# 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.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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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 section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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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]
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## 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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**BibTeX:**
[More Information Needed]
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[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. -->
[More Information Needed]
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[More Information Needed] |
paulaolmedo/dqn-SpaceInvadersNoFrameskip-v4__ | paulaolmedo | 2024-07-02T14:53:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T14:53:04Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 5.00 +/- 7.07
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga paulaolmedo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga paulaolmedo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga paulaolmedo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
meghnareddy90/batch-7-6001-7000 | meghnareddy90 | 2024-07-02T14:53:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-07-02T14:53:42Z | ---
base_model: microsoft/phi-2
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: batch-7-6001-7000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# batch-7-6001-7000
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
dafqi/indobert-base-p1 | dafqi | 2024-07-02T14:55:29Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:55:29Z | Entry not found |
tunepanel/crunchy-small-m7b3 | tunepanel | 2024-07-02T23:29:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-07-02T14:55:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
whizzzzkid/whizzzzkid_417_5 | whizzzzkid | 2024-07-02T14:56:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T14:56:17Z | Entry not found |
koalo1koal803/Koalo | koalo1koal803 | 2024-07-02T14:56:52Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:56:52Z | Entry not found |
Bueorm/HMTM | Bueorm | 2024-07-02T15:13:53Z | 0 | 0 | null | [
"translation",
"en",
"es",
"de",
"fr",
"it",
"license:mit",
"region:us"
] | translation | 2024-07-02T14:57:02Z | ---
license: mit
language:
- en
- es
- de
- fr
- it
pipeline_tag: translation
---
## Model Details
### Model Description
Our model leverages the power of multiple pre-trained translation models from Helsinki-NLP, including translations between English, French, Spanish, German, and Italian. Each translation direction utilizes state-of-the-art models such as Helsinki-NLP/opus-mt-en-fr, Helsinki-NLP/opus-mt-fr-en, and others. This model integrates a Mixture of Experts (MoE) approach to dynamically select the most appropriate model for translation based on input language pairs, ensuring high accuracy and versatility. Developed by Bueorm, this model aims to provide robust translation capabilities across multiple languages, thanks to the pioneering work of Helsinki-NLP in machine translation.
- **Developed by:** BueormAI
- **Funded by:** Gerson Buenahora
- **Shared by:** Bueorm
- **Model type:** Translation
- **Language(s) (NLP):** English, Espanish, German, French, Italian
- **License:** MiT
## How to Use
```python
import torch
from transformers import MarianMTModel, MarianTokenizer
def load_model(file_path):
return torch.load(file_path)
loaded_model_dict = load_model('MoE_translation_model.pth')
loaded_models = loaded_model_dict['models']
loaded_tokenizers = loaded_model_dict['tokenizers']
def translate(text, src_lang, tgt_lang):
pair = f'{src_lang}-{tgt_lang}'
if pair in loaded_models:
model = loaded_models[pair]
tokenizer = loaded_tokenizers[pair]
inputs = tokenizer(text, return_tensors='pt')
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
return translated_text
else:
return f"No model found to translate from {src_lang} to {tgt_lang}"
text = "Hello, how are you?"
translated_text = translate(text, 'en', 'fr')
print(translated_text)
```
## Languages
- English to French (en, fr)
- French to English (fr, en)
- English to Spanish (en, es)
- Spanish to English (es, en)
- English to German (en, de)
- German to English (de, en)
- Spanish to German (es, de)
- German to Spanish (de, es)
- Spanish to French (es, fr)
- French to Spanish (fr, es)
- German to French (de, fr)
- French to German (fr, de)
- Spanish to Italian (es, it)
- Italian to Spanish (it, es)
- English to Italian (en, it)
- Italian to English (it, en)
|
vaibharn/mydiffusion | vaibharn | 2024-07-02T14:57:07Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | null | 2024-07-02T14:57:07Z | ---
license: afl-3.0
---
|
whizzzzkid/whizzzzkid_418_3 | whizzzzkid | 2024-07-02T14:57:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T14:57:23Z | Entry not found |
Moriacrafter/LLaMA3-8B-none_DepressionDetection | Moriacrafter | 2024-07-02T14:57:59Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:57:59Z | Entry not found |
itay-nakash/model_387dff9370_sweep_pretty-universe-1195 | itay-nakash | 2024-07-02T14:58:02Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T14:58:02Z | Entry not found |
dmehta/fine_tuned_model | dmehta | 2024-07-02T14:58:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T14:58:06Z | ---
base_model: unsloth/mistral-7b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** dmehta
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-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)
|
whizzzzkid/whizzzzkid_419_4 | whizzzzkid | 2024-07-02T14:58:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T14:58:35Z | Entry not found |
datascience-service/bert-french-ner | datascience-service | 2024-07-02T15:02:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-07-02T14:59:11Z | Entry not found |
braindao/iq-code-evmind-v3.1-granite-8b-instruct-expert | braindao | 2024-07-02T19:49:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"solidity",
"conversational",
"en",
"dataset:braindao/Solidity-Dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T14:59:31Z | ---
license: apache-2.0
datasets:
- braindao/Solidity-Dataset
language:
- en
tags:
- solidity
---
The model "braindao/iq-code-evmind-v3.1-granite-8b-instruct-expert" is a specialized Large Language Model (LLM hosted on Hugging Face, designed for generating Solidity code.
It leverages the dataset "braindao/Solidity-Dataset" and is built on the foundation of "ibm-granite/granite-8b-code-instruct."
This LLM utilizes the "expert" column from the dataset to provide high-quality, expert-level code generation in Solidity, catering specifically to developers and projects in the blockchain and smart contract space. |
whizzzzkid/whizzzzkid_420_1 | whizzzzkid | 2024-07-02T14:59:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T14:59:35Z | Entry not found |
MettBrot/flan-t5-small-quaso-gen3 | MettBrot | 2024-07-02T15:01:43Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-07-02T15:00:24Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small-quaso-gen3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-quaso-gen3
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6376
- Rouge1: 0.3819
- Rouge2: 0.1461
- Rougel: 0.3088
- Rougelsum: 0.3635
- Gen Len: 39.812
## 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: 7e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.7575 | 1.0 | 11088 | 1.6600 | 0.3731 | 0.1410 | 0.3022 | 0.3556 | 38.84 |
| 1.7337 | 2.0 | 22176 | 1.6517 | 0.3744 | 0.1430 | 0.3042 | 0.3567 | 37.971 |
| 1.7441 | 3.0 | 33264 | 1.6458 | 0.3842 | 0.1463 | 0.3091 | 0.3657 | 40.323 |
| 1.7349 | 4.0 | 44352 | 1.6400 | 0.3817 | 0.1460 | 0.3089 | 0.3635 | 39.4747 |
| 1.715 | 5.0 | 55440 | 1.6376 | 0.3819 | 0.1461 | 0.3088 | 0.3635 | 39.812 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
whizzzzkid/whizzzzkid_421_7 | whizzzzkid | 2024-07-02T15:00:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T15:00:35Z | Entry not found |
whizzzzkid/whizzzzkid_422_6 | whizzzzkid | 2024-07-02T15:02:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T15:01:36Z | Entry not found |
RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_test_pipeline_1k_steps | RefalMachine | 2024-07-02T15:05:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_mean_init_03_07_24",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:02:00Z | ---
base_model: RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_mean_init_03_07_24
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mistral_darulm_20_05_24_part1-2_32000_bpe_test_pipeline_1k_steps
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_darulm_20_05_24_part1-2_32000_bpe_test_pipeline_1k_steps
This model is a fine-tuned version of [RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_mean_init_03_07_24](https://huggingface.co/RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_mean_init_03_07_24) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2672
- Accuracy: 0.5391
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5313 | 0.01 | 500 | 2.3291 | 0.5299 |
| 2.4789 | 0.01 | 1000 | 2.2672 | 0.5391 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
KYAGABA/wav2vec2-large-xls-r-300m-luo-googlefluers-10hr-v1 | KYAGABA | 2024-07-02T16:35:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T15:02:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-50hr-v2 | KasuleTrevor | 2024-07-03T00:32:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T15:02:31Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-lg-cv-50hr-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/l61a7jm2)
# wav2vec2-large-xls-r-300m-lg-cv-50hr-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5115
- Wer: 0.2522
- Cer: 0.0559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 2.6266 | 0.9995 | 932 | 0.4073 | 0.5606 | 0.1122 |
| 0.3178 | 2.0 | 1865 | 0.2853 | 0.3890 | 0.0761 |
| 0.221 | 2.9995 | 2797 | 0.2551 | 0.3462 | 0.0661 |
| 0.1738 | 4.0 | 3730 | 0.2489 | 0.3231 | 0.0621 |
| 0.1488 | 4.9995 | 4662 | 0.2566 | 0.3127 | 0.0607 |
| 0.1287 | 6.0 | 5595 | 0.2661 | 0.3083 | 0.0594 |
| 0.1151 | 6.9995 | 6527 | 0.2600 | 0.3046 | 0.0595 |
| 0.1028 | 8.0 | 7460 | 0.2699 | 0.2946 | 0.0571 |
| 0.0921 | 8.9995 | 8392 | 0.2699 | 0.2907 | 0.0564 |
| 0.0827 | 10.0 | 9325 | 0.2988 | 0.2967 | 0.0578 |
| 0.0765 | 10.9995 | 10257 | 0.3016 | 0.2885 | 0.0559 |
| 0.0686 | 12.0 | 11190 | 0.3053 | 0.2954 | 0.0560 |
| 0.0637 | 12.9995 | 12122 | 0.3079 | 0.2913 | 0.0560 |
| 0.0606 | 14.0 | 13055 | 0.3277 | 0.2866 | 0.0554 |
| 0.0559 | 14.9995 | 13987 | 0.3206 | 0.2889 | 0.0556 |
| 0.0534 | 16.0 | 14920 | 0.3395 | 0.2900 | 0.0561 |
| 0.05 | 16.9995 | 15852 | 0.3417 | 0.2823 | 0.0548 |
| 0.0482 | 18.0 | 16785 | 0.3338 | 0.2837 | 0.0547 |
| 0.0465 | 18.9995 | 17717 | 0.3577 | 0.2833 | 0.0548 |
| 0.0435 | 20.0 | 18650 | 0.3414 | 0.2734 | 0.0529 |
| 0.0412 | 20.9995 | 19582 | 0.3589 | 0.2758 | 0.0535 |
| 0.0412 | 22.0 | 20515 | 0.3507 | 0.2706 | 0.0528 |
| 0.0404 | 22.9995 | 21447 | 0.3464 | 0.2695 | 0.0524 |
| 0.0374 | 24.0 | 22380 | 0.3604 | 0.2685 | 0.0523 |
| 0.0365 | 24.9995 | 23312 | 0.3543 | 0.2686 | 0.0521 |
| 0.0343 | 26.0 | 24245 | 0.3703 | 0.2655 | 0.0523 |
| 0.0332 | 26.9995 | 25177 | 0.3684 | 0.2673 | 0.0518 |
| 0.0324 | 28.0 | 26110 | 0.3720 | 0.2626 | 0.0511 |
| 0.0299 | 28.9995 | 27042 | 0.3679 | 0.2606 | 0.0504 |
| 0.0296 | 30.0 | 27975 | 0.3659 | 0.2638 | 0.0509 |
| 0.0291 | 30.9995 | 28907 | 0.3709 | 0.2636 | 0.0502 |
| 0.0272 | 32.0 | 29840 | 0.3745 | 0.2586 | 0.0497 |
| 0.0265 | 32.9995 | 30772 | 0.3846 | 0.2564 | 0.0495 |
| 0.025 | 34.0 | 31705 | 0.3943 | 0.2550 | 0.0494 |
| 0.0244 | 34.9995 | 32637 | 0.3868 | 0.2565 | 0.0494 |
| 0.0233 | 36.0 | 33570 | 0.3862 | 0.2550 | 0.0490 |
| 0.0225 | 36.9995 | 34502 | 0.3937 | 0.2536 | 0.0489 |
| 0.0217 | 38.0 | 35435 | 0.3948 | 0.2505 | 0.0484 |
| 0.0212 | 38.9995 | 36367 | 0.3934 | 0.2488 | 0.0480 |
| 0.0204 | 40.0 | 37300 | 0.3962 | 0.2486 | 0.0480 |
| 0.0196 | 40.9995 | 38232 | 0.4000 | 0.2459 | 0.0472 |
| 0.0185 | 42.0 | 39165 | 0.3936 | 0.2444 | 0.0466 |
| 0.0178 | 42.9995 | 40097 | 0.4068 | 0.2467 | 0.0473 |
| 0.0179 | 44.0 | 41030 | 0.4059 | 0.2456 | 0.0471 |
| 0.0167 | 44.9995 | 41962 | 0.4105 | 0.2413 | 0.0463 |
| 0.0165 | 46.0 | 42895 | 0.4042 | 0.2444 | 0.0465 |
| 0.0156 | 46.9995 | 43827 | 0.3972 | 0.2410 | 0.0459 |
| 0.0151 | 48.0 | 44760 | 0.4026 | 0.2419 | 0.0460 |
| 0.0156 | 48.9995 | 45692 | 0.4060 | 0.2398 | 0.0455 |
| 0.0153 | 50.0 | 46625 | 0.4065 | 0.2383 | 0.0454 |
| 0.0137 | 50.9995 | 47557 | 0.4040 | 0.2414 | 0.0456 |
| 0.0136 | 52.0 | 48490 | 0.4081 | 0.2399 | 0.0455 |
| 0.0141 | 52.9995 | 49422 | 0.4073 | 0.2377 | 0.0453 |
| 0.0136 | 54.0 | 50355 | 0.4053 | 0.2375 | 0.0450 |
| 0.0132 | 54.9995 | 51287 | 0.4084 | 0.2368 | 0.0450 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
kaveri1184/llama2-7b_finetuned_try | kaveri1184 | 2024-07-02T15:02:45Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:02:45Z | Entry not found |
marcoaversa/results | marcoaversa | 2024-07-02T15:02:58Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:02:58Z | Entry not found |
slm-research-vn/Qwen2-7B-Merged-Einstein-v7-Arcee-Spark | slm-research-vn | 2024-07-02T15:07:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:03:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Rishabh-sucks-at-code/trainer-chapter4 | Rishabh-sucks-at-code | 2024-07-02T15:04:29Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T15:03:58Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: trainer-chapter4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trainer-chapter4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2479
- Accuracy: 0.92
- F1: 0.9200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 313 | 0.2773 | 0.9087 | 0.9086 |
| 0.3039 | 2.0 | 626 | 0.2479 | 0.92 | 0.9200 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF | Isaak-Carter | 2024-07-02T15:04:43Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"de",
"base_model:Isaak-Carter/josiev4o-7b-stage1-beta3.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:04:23Z | ---
base_model: Isaak-Carter/josiev4o-7b-stage1-beta3.0
language:
- en
- de
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`Isaak-Carter/josiev4o-7b-stage1-beta3.0`](https://huggingface.co/Isaak-Carter/josiev4o-7b-stage1-beta3.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Isaak-Carter/josiev4o-7b-stage1-beta3.0) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF --hf-file josiev4o-7b-stage1-beta3.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF --hf-file josiev4o-7b-stage1-beta3.0-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF --hf-file josiev4o-7b-stage1-beta3.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Isaak-Carter/josiev4o-7b-stage1-beta3.0-Q4_K_M-GGUF --hf-file josiev4o-7b-stage1-beta3.0-q4_k_m.gguf -c 2048
```
|
Molapsa/Lina | Molapsa | 2024-07-02T15:05:45Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2024-07-02T15:05:45Z | ---
license: bigscience-bloom-rail-1.0
---
|
Vaaly/llama3-tickets | Vaaly | 2024-07-02T15:06:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:06:14Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
urpopssocks/testing | urpopssocks | 2024-07-02T15:06:40Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | 2024-07-02T15:06:40Z | ---
license: unknown
---
|
Rishabh-sucks-at-code/sft_cml4 | Rishabh-sucks-at-code | 2024-07-02T15:16:00Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:07:37Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: sft_cml4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft_cml4
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6594 | 0.32 | 200 | 3.5973 |
| 3.3107 | 0.64 | 400 | 3.4906 |
| 3.0987 | 0.96 | 600 | 3.3723 |
| 2.1443 | 1.28 | 800 | 3.4440 |
| 1.9649 | 1.6 | 1000 | 3.4159 |
| 1.9109 | 1.92 | 1200 | 3.4024 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
MrGonk/Gonk_2 | MrGonk | 2024-07-02T15:10:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:08:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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mradermacher/X-NoroChronos-13B-i1-GGUF | mradermacher | 2024-07-02T21:39:13Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"not-for-all-audiences",
"nsfw",
"en",
"base_model:NeverSleep/X-NoroChronos-13B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:08:43Z | ---
base_model: NeverSleep/X-NoroChronos-13B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- not-for-all-audiences
- nsfw
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/NeverSleep/X-NoroChronos-13B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/X-NoroChronos-13B-GGUF
## 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/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/X-NoroChronos-13B-i1-GGUF/resolve/main/X-NoroChronos-13B.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
HarshN-07/fake-news-detection | HarshN-07 | 2024-07-02T20:29:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:09:25Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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<!-- 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]
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JJSLL/Nupzook_MIT_object | JJSLL | 2024-07-02T16:31:13Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-07-02T15:09:34Z | ---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
inference: true
---
<!-- 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 - JJSLL/Nupzook_MIT_object
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. 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] |
houbw/llama38b_ruozhiba_8 | houbw | 2024-07-02T15:09:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:09:45Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** houbw
- **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)
|
ninho86/testeRonald | ninho86 | 2024-07-02T15:10:06Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:10:06Z | Entry not found |
YashJain/GitAI-Qwen2-0.5B-Instruct-MLX-v2 | YashJain | 2024-07-02T15:19:56Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-07-02T15:11:19Z | ---
language:
- en
license: apache-2.0
tags:
- chat
- mlx
pipeline_tag: text-generation
---
# YashJain/GitAI-Qwen2-0.5B-Instruct-MLX-v2
The Model [YashJain/GitAI-Qwen2-0.5B-Instruct-MLX-v2](https://huggingface.co/YashJain/GitAI-Qwen2-0.5B-Instruct-MLX-v2) was converted to MLX format from [Qwen/Qwen2-0.5B-Instruct-MLX](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct-MLX) using mlx-lm version **0.14.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("YashJain/GitAI-Qwen2-0.5B-Instruct-MLX-v2")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
purvag-iu/Clydeo2DUpdateBg | purvag-iu | 2024-07-02T15:13:35Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-02T15:11:45Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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. -->
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whizzzzkid/whizzzzkid_423_2 | whizzzzkid | 2024-07-02T15:13:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T15:12:46Z | Entry not found |
not1010011010/lora_model-AIVABOT | not1010011010 | 2024-07-02T15:24:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:12:51Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** not1010011010
- **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)
|
gisang-lee/mistral-7b-qlora-arc-wandb-test-arc-easy-all | gisang-lee | 2024-07-02T15:23:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-07-02T15:12:59Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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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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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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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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]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
Grayx/john_paul_van_damme_69 | Grayx | 2024-07-02T15:13:51Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:13:33Z | Entry not found |
Dilan7896/Wingom7hhhh | Dilan7896 | 2024-07-02T15:14:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:13:45Z | Entry not found |
ferrazzipietro/Llama-2-7b-chat-hfspecialTkn_en.layer1_NoQuant_64_16_0.02_8 | ferrazzipietro | 2024-07-02T15:14:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:13:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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[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]
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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
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
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Grayx/john_paul_van_damme_70 | Grayx | 2024-07-02T15:14:23Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:14:09Z | Entry not found |
Grayx/john_paul_van_damme_71 | Grayx | 2024-07-02T15:14:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:14:44Z | Entry not found |
Grayx/john_paul_van_damme_72 | Grayx | 2024-07-02T15:15:27Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:15:15Z | Entry not found |
Grayx/john_paul_van_damme_73 | Grayx | 2024-07-02T15:15:56Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:15:42Z | Entry not found |
Grayx/john_paul_van_damme_74 | Grayx | 2024-07-02T15:16:27Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:16:17Z | Entry not found |
manbeast3b/ZZZZZZZZdriver135 | manbeast3b | 2024-07-02T15:19:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T15:16:58Z | Entry not found |
Sam1995/ikea_room_designs_sdxl_full_finetuning020720241513 | Sam1995 | 2024-07-02T15:17:08Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:17:08Z | Entry not found |
apwic/summarization-lora-2 | apwic | 2024-07-02T18:34:27Z | 0 | 0 | null | [
"generated_from_trainer",
"id",
"base_model:LazarusNLP/IndoNanoT5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T15:17:33Z | ---
language:
- id
license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization-lora-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# summarization-lora-2
This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5351
- Rouge1: 0.3585
- Rouge2: 0.0
- Rougel: 0.3555
- Rougelsum: 0.357
- Gen Len: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2329 | 1.0 | 1787 | 0.5976 | 0.3912 | 0.0 | 0.3916 | 0.392 | 1.0 |
| 0.7952 | 2.0 | 3574 | 0.5580 | 0.3919 | 0.0 | 0.3921 | 0.3921 | 1.0 |
| 0.7407 | 3.0 | 5361 | 0.5366 | 0.3893 | 0.0 | 0.3879 | 0.3866 | 1.0 |
| 0.7152 | 4.0 | 7148 | 0.5402 | 0.354 | 0.0 | 0.3512 | 0.3523 | 1.0 |
| 0.7029 | 5.0 | 8935 | 0.5351 | 0.3585 | 0.0 | 0.3555 | 0.357 | 1.0 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
noland-peart/Mana-Design-1.0 | noland-peart | 2024-07-02T18:45:36Z | 0 | 0 | transformers | [
"transformers",
"stable_diffusion",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:17:35Z | ---
license: mit
---
|
Salvatore/DebiasedDiffusion | Salvatore | 2024-07-02T15:19:24Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | 2024-07-02T15:17:39Z | Entry not found |
lgodwangl/sn9_vx | lgodwangl | 2024-07-02T15:20:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:17:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### 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]
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[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. -->
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ironlanderl/phi-3-mini-4k-f16 | ironlanderl | 2024-07-02T15:43:25Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"dataset:CohereForAI/aya_collection_language_split",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:18:50Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
datasets:
- CohereForAI/aya_collection_language_split
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
sgonzalezsilot/whisper-small-es-Nemo_unified_2024-07-02_15-19-06 | sgonzalezsilot | 2024-07-02T17:28:24Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T15:19:07Z | Entry not found |
ironlanderl/phi-3-mini-4k-lora | ironlanderl | 2024-07-02T15:35:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:19:43Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
maxseats/SungBeom-whisper-small-ko-set18 | maxseats | 2024-07-02T15:20:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"speech-recognition",
"ko",
"dataset:maxseats/aihub-464-preprocessed-680GB-set-18",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T15:19:44Z |
---
language: ko
tags:
- whisper
- speech-recognition
datasets:
- maxseats/aihub-464-preprocessed-680GB-set-18
metrics:
- cer
---
# Model Name : maxseats/SungBeom-whisper-small-ko-set17
# Description
- 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-18
# 설명
- AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요.
- 680GB 중 set_0~17 데이터(180GB)까지 파인튜닝한 모델을 불러와서, set_18 데이터(10GB)를 학습한 모델입니다.
- 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-18
|
amitdev2024/Amit | amitdev2024 | 2024-07-02T15:25:08Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"code",
"text-generation",
"ab",
"dataset:OpenGVLab/ShareGPT-4o",
"dataset:HuggingFaceFW/fineweb",
"license:llama3",
"region:us"
] | text-generation | 2024-07-02T15:21:28Z | ---
license: llama3
datasets:
- OpenGVLab/ShareGPT-4o
- HuggingFaceFW/fineweb
language:
- ab
metrics:
- character
- charcut_mt
library_name: adapter-transformers
pipeline_tag: text-generation
tags:
- code
--- |
JoseGR1702/result_ver222 | JoseGR1702 | 2024-07-02T15:21:46Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:21:46Z | Entry not found |
NikolayKozloff/Viking-33B-Q2_K-GGUF | NikolayKozloff | 2024-07-02T15:27:41Z | 0 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation-inference",
"fi",
"en",
"da",
"sv",
"no",
"nn",
"is",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:mc4",
"base_model:LumiOpen/Viking-33B",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T15:23:53Z | ---
base_model: LumiOpen/Viking-33B
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- mc4
language:
- fi
- en
- da
- sv
- 'no'
- nn
- is
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
- text-generation-inference
---
# NikolayKozloff/Viking-33B-Q2_K-GGUF
This model was converted to GGUF format from [`LumiOpen/Viking-33B`](https://huggingface.co/LumiOpen/Viking-33B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LumiOpen/Viking-33B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Viking-33B-Q2_K-GGUF --hf-file viking-33b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Viking-33B-Q2_K-GGUF --hf-file viking-33b-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Viking-33B-Q2_K-GGUF --hf-file viking-33b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Viking-33B-Q2_K-GGUF --hf-file viking-33b-q2_k.gguf -c 2048
``` |
ostris/vae-kl-f8-d16 | ostris | 2024-07-02T18:50:09Z | 0 | 11 | diffusers | [
"diffusers",
"safetensors",
"license:mit",
"region:us"
] | null | 2024-07-02T15:23:55Z | ---
license: mit
library_name: diffusers
---
# Ostris VAE - KL-f8-d16
A 16 channel VAE with 8x downsample. Trained from scratch on a balance of photos, artistic, text, cartoons, vector images.
It is lighter weight that most VAEs with only 57,266,643 parameters (vs SD3 VAE: 83,819,683) which means it is faster and uses less VRAM yet scores quite similarly
on real images. Plus it is MIT licensed so you can do whatever you want with it.
| VAE|PSNR (higher better)| LPIPS (lower better) | # params |
|----|----|----|----|
| sd-vae-ft-mse|26.939|0.0581|83,653,863|
| SDXL|27.370|0.0540|83,653,863|
| SD3|31.681|0.0187|83,819,683|
| **Ostris KL-f8-d16** |**31.166**|**0.0198**|**57,266,643**|
### Compare
Check out the comparison at [imgsli](https://imgsli.com/Mjc2MjA3).
### What do I do with this?
If you don't know, you probably don't need this. This is made as an open source lighter version of a 16ch vae.
You would need to train it into a network before it is useful. I plan to do this myself for SD 1.5, SDXL, and possibly pixart.
[Follow me on Twitter](https://x.com/ostrisai) to keep up with my work on that.
### Note: Not SD3 compatable
This VAE is not SD3 compatable as it is trained from scratch and has an entirely different latent space. |
Marcos12886/distilhubert-finetuned-donateacry | Marcos12886 | 2024-07-02T21:03:19Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-07-02T15:24:53Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-donateacry
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7415730337078652
---
<!-- 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. -->
# distilhubert-finetuned-donateacry
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8626
- Accuracy: 0.7416
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 23 | 0.8626 | 0.7416 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|
mradermacher/Nethena-20B-GGUF | mradermacher | 2024-07-02T22:31:06Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:NeverSleep/Nethena-20B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:25:06Z | ---
base_model: NeverSleep/Nethena-20B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NeverSleep/Nethena-20B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF
## 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/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q2_K.gguf) | Q2_K | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.IQ3_XS.gguf) | IQ3_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.IQ3_S.gguf) | IQ3_S | 8.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q3_K_S.gguf) | Q3_K_S | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.IQ3_M.gguf) | IQ3_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q3_K_M.gguf) | Q3_K_M | 9.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q3_K_L.gguf) | Q3_K_L | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.IQ4_XS.gguf) | IQ4_XS | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q4_K_S.gguf) | Q4_K_S | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q4_K_M.gguf) | Q4_K_M | 12.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q5_K_S.gguf) | Q5_K_S | 13.9 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q5_K_M.gguf) | Q5_K_M | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q6_K.gguf) | Q6_K | 16.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-GGUF/resolve/main/Nethena-20B.Q8_0.gguf) | Q8_0 | 21.3 | fast, best quality |
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 -->
|
lrqlrqlrq/MixTex | lrqlrqlrq | 2024-07-02T15:31:45Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T15:25:08Z | ---
license: apache-2.0
--- |
saridormi/OpenCodeInterpreter-DS-1.3B | saridormi | 2024-07-02T15:26:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:26:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
jangmin/3d-icon-sdxl-lora | jangmin | 2024-07-02T15:26:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T15:26:48Z | Entry not found |
Darshan7575/slurp_multiconvformer_conv_fusion | Darshan7575 | 2024-07-02T15:45:27Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp_entity",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2024-07-02T15:27:20Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slurp_entity
license: cc-by-4.0
---
## ESPnet2 ASR model
### `Darshan7575/slurp_multiconvformer_conv_fusion`
This model was trained by Darshan using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout edb6ec64bb5d4f2c68a3b81674f0c2822e2e5b58
pip install -e .
cd egs2_imp/slurp_entity/asr1
./run.sh --skip_data_prep false --skip_train true --download_model Darshan7575/slurp_multiconvformer_conv_fusion
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Feb 21 01:04:03 EST 2024`
- python version: `3.9.18 (main, Sep 11 2023, 13:41:44) [GCC 11.2.0]`
- espnet version: `espnet 202310`
- pytorch version: `pytorch 2.1.2+cu118`
- Git hash: `edb6ec64bb5d4f2c68a3b81674f0c2822e2e5b58`
- Commit date: `Fri Feb 9 21:26:35 2024 +0530`
## exp/slurp_multiconvformer_conv_fusion
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|262176|84.1|7.5|8.5|2.7|18.7|47.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|1245475|90.0|3.0|7.0|3.1|13.1|47.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## exp/slurp_multiconvformer_conv_fusion/decode_asr_asr_model_valid.acc.ave_10best
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/devel|8690|178058|84.9|7.4|7.7|3.1|18.1|48.7|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/devel|8690|847400|90.9|2.9|6.2|3.4|12.5|48.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
### Intent Classification
- Valid Intent Classification Result:
0.8882623705408516
- Test Intent Classification Result:
0.8737574552683897
### Entity
|Slu f1|Precision|Recall|F-Measure|
|:---:|:---:|:---:|:---:|
| test | 0.8076 | 0.7710 | 0.7889 |
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_multiconv_e12_mlp3072_linear2048_layerdrop.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/slurp_multiconvformer_conv_fusion
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 40439
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 60
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- kaldi_ark
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/devel/wav.scp
- speech
- kaldi_ark
- - dump/raw/devel/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 35000
token_list:
- <blank>
- <unk>
- ▁SEP
- ▁FILL
- s
- ▁the
- a
- ▁to
- ▁i
- ▁me
- e
- ▁s
- ▁a
- i
- ▁you
- ▁what
- er
- ing
- u
- ▁is
- ''''
- o
- p
- ▁in
- ▁p
- y
- ▁my
- ▁please
- d
- c
- m
- ▁b
- l
- ▁m
- ▁c
- st
- date
- n
- ▁d
- le
- b
- ▁for
- re
- t
- ▁on
- en
- h
- 'on'
- ar
- person
- ▁re
- ▁f
- ▁g
- ▁of
- an
- ▁
- g
- ▁today
- ▁t
- or
- ▁it
- ▁this
- ▁h
- r
- f
- at
- ch
- ce
- place_name
- ▁email
- ▁do
- es
- ri
- ▁e
- ▁w
- ic
- in
- ▁that
- event_name
- ▁play
- ▁and
- al
- ▁n
- ▁can
- email_query
- ve
- ▁new
- day
- it
- ate
- ▁from
- ▁have
- k
- time
- ▁am
- media_type
- email_sendemail
- ent
- ▁olly
- qa_factoid
- se
- v
- et
- ck
- ▁any
- calendar_set
- ly
- th
- ▁how
- ▁meeting
- ed
- ▁tell
- ▁st
- x
- ur
- ro
- ▁at
- nd
- ▁list
- w
- ▁u
- ou
- ▁not
- ▁about
- ▁an
- ▁o
- general_negate
- ut
- ▁time
- ▁be
- ▁ch
- ▁are
- social_post
- business_name
- la
- ty
- play_music
- ot
- general_quirky
- ▁l
- ▁sh
- ▁tweet
- om
- ▁week
- um
- ▁one
- ter
- ▁he
- ▁up
- ▁com
- general_praise
- weather_query
- ▁next
- ▁th
- ▁check
- calendar_query
- ▁last
- ▁ro
- ad
- is
- ▁with
- ay
- ▁send
- pe
- ▁pm
- ▁tomorrow
- ▁j
- un
- ▁train
- general_explain
- ▁v
- one
- ▁r
- ra
- news_query
- ation
- ▁emails
- us
- if
- ct
- ▁co
- ▁add
- ▁will
- ▁se
- nt
- ▁was
- ine
- ▁de
- ▁set
- ▁ex
- ▁would
- ir
- ow
- ber
- general_repeat
- ight
- ook
- ▁again
- ▁song
- currency_name
- ll
- ▁ha
- ▁go
- relation
- te
- ion
- and
- ▁y
- ▁ye
- general_affirm
- general_confirm
- ery
- ▁po
- ff
- ▁we
- ▁turn
- ▁did
- ▁mar
- ▁alarm
- ▁like
- datetime_query
- ers
- ▁all
- ▁remind
- ▁so
- qa_definition
- ▁calendar
- end
- ▁said
- ci
- ▁off
- ▁john
- ▁day
- ss
- pla
- ume
- ▁get
- ail
- pp
- z
- ry
- am
- ▁need
- as
- ▁thank
- ▁wh
- ▁want
- ▁right
- ▁jo
- ▁facebook
- ▁k
- ge
- ld
- ▁fri
- ▁two
- general_dontcare
- ▁news
- ol
- oo
- ant
- ▁five
- ▁event
- ake
- definition_word
- transport_type
- ▁your
- vi
- orn
- op
- ▁weather
- ome
- ▁app
- ▁lo
- de
- ▁music
- weather_descriptor
- ak
- ke
- ▁there
- ▁si
- ▁lights
- ▁now
- ▁mo
- calendar_remove
- our
- ▁dollar
- food_type
- me
- ▁more
- ▁no
- ▁birthday
- orrect
- ▁rep
- ▁show
- play_radio
- ▁mon
- ▁does
- ood
- ag
- li
- ▁sto
- ▁contact
- cket
- email_querycontact
- ▁ev
- ▁could
- ange
- ▁just
- out
- ame
- .
- ▁ja
- ▁confirm
- qa_currency
- ▁man
- ▁late
- ▁think
- ▁some
- timeofday
- ▁bo
- qa_stock
- ong
- ▁start
- ▁work
- ▁ten
- int
- ▁command
- all
- ▁make
- ▁la
- j
- ▁answ
- ▁hour
- ▁cle
- ah
- ▁find
- ▁service
- ▁fa
- qu
- general_commandstop
- ai
- ▁when
- ▁te
- ▁by
- social_query
- ard
- ▁tw
- ul
- id
- ▁seven
- ▁where
- ▁much
- art
- ▁appointment
- ver
- artist_name
- el
- device_type
- ▁know
- ▁three
- ▁events
- ▁tr
- ▁li
- ork
- red
- ect
- ▁let
- ▁respon
- ▁par
- zz
- ▁give
- ▁twenty
- ▁ti
- ▁curre
- play_podcasts
- ▁radio
- cooking_recipe
- transport_query
- ▁con
- gh
- ▁le
- lists_query
- ▁rem
- recommendation_events
- house_place
- alarm_set
- play_audiobook
- ist
- ase
- music_genre
- ive
- ast
- player_setting
- ort
- lly
- news_topic
- list_name
- ▁playlist
- ▁ne
- business_type
- personal_info
- ind
- ust
- di
- ress
- recommendation_locations
- lists_createoradd
- iot_hue_lightoff
- lists_remove
- ord
- ▁light
- ere
- alarm_query
- audio_volume_mute
- music_query
- ▁audio
- rain
- ▁date
- ▁order
- audio_volume_up
- ▁ar
- ▁podcast
- transport_ticket
- mail
- iot_hue_lightchange
- iot_coffee
- radio_name
- ill
- ▁ri
- '@'
- takeaway_query
- song_name
- takeaway_order
- ▁ra
- email_addcontact
- play_game
- book
- transport_traffic
- ▁house
- music_likeness
- her
- transport_taxi
- iot_hue_lightdim
- ment
- ght
- fo
- order_type
- color_type
- '1'
- ven
- ould
- general_joke
- ess
- ain
- qa_maths
- ▁place
- ▁twe
- cast
- iot_cleaning
- ▁che
- ▁cont
- ith
- audiobook_name
- email_address
- game_name
- ▁cal
- general_frequency
- ▁tom
- ▁food
- act
- iot_hue_lightup
- '2'
- alarm_remove
- podcast_descriptor
- ▁definition
- audio_volume_down
- ▁media
- email_folder
- dia
- meal_type
- ▁mus
- recommendation_movies
- ▁ad
- ree
- pt
- now
- playlist_name
- ▁person
- change_amount
- ▁pla
- escri
- datetime_convert
- podcast_name
- ▁ab
- time_zone
- ▁def
- ting
- iot_wemo_on
- music_settings
- iot_wemo_off
- orre
- cy
- ank
- music_descriptor
- lar
- app_name
- row
- joke_type
- xt
- of
- ition
- ▁meet
- ink
- ▁confir
- transport_agency
- general_greet
- ▁business
- ▁art
- ▁ag
- urn
- escript
- rom
- ▁rel
- ▁au
- ▁currency
- audio_volume_other
- iot_hue_lighton
- ▁artist
- '?'
- ▁bus
- cooking_type
- movie_name
- coffee_type
- ingredient
- ather
- music_dislikeness
- sp
- q
- ▁ser
- esc
- ▁bir
- ▁cur
- name
- ▁tran
- ▁hou
- ek
- uch
- ▁conf
- ▁face
- '9'
- ▁birth
- I
- sw
- transport_descriptor
- ▁comm
- lease
- transport_name
- aid
- movie_type
- ▁device
- alarm_type
- audiobook_author
- '5'
- drink_type
- ▁joh
- ▁defin
- word
- ▁curren
- order
- iness
- W
- cooking_query
- sport_type
- ▁relation
- oint
- H
- '8'
- A
- '0'
- ▁dol
- vice
- ▁pers
- '&'
- T
- ▁appoint
- _
- '7'
- '3'
- '-'
- game_type
- ▁pod
- N
- M
- E
- list
- music_album
- dio
- ▁transport
- qa_query
- C
- O
- U
- query_detail
- ']'
- '['
- descriptor
- ':'
- spon
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
brctc_risk_strategy: exp
brctc_group_strategy: end
brctc_risk_factor: 0.0
joint_net_conf: null
use_preprocessor: true
use_lang_prompt: false
use_nlp_prompt: false
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: null
preencoder_conf: {}
encoder: multiconv_conformer
encoder_conf:
output_size: 512
attention_heads: 8
selfattention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 3072
multicgmlp_type: concat_fusion
multicgmlp_kernel_sizes: 7,15,23,31
multicgmlp_merge_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.1
linear_units: 1152
positionwise_layer_type: linear
macaron_style: true
use_cnn_module: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.2
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202310'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
manbeast3b/ZZZZZZZZdriver136 | manbeast3b | 2024-07-02T15:30:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T15:28:01Z | Entry not found |
chenghenry/gemma-2-9b-it-GGUF | chenghenry | 2024-07-02T15:37:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"base_model:google/gemma-2-9b-it",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T15:28:12Z | ---
license: gemma
library_name: transformers
base_model: google/gemma-2-9b-it
--- |
igoranoni/model | igoranoni | 2024-07-02T15:45:43Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T15:31:58Z | Entry not found |
Sathwik-kom/Llama2-chefGuru | Sathwik-kom | 2024-07-02T15:33:55Z | 0 | 0 | peft | [
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2024-07-02T15:32:27Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
BlackSamorez/Meta-Llama-3-70B-Instruct-GPTQ | BlackSamorez | 2024-07-02T20:07:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T15:34:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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### Testing Data, Factors & Metrics
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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John6666/featureless-mix-pony-v2-sdxl | John6666 | 2024-07-02T15:41:20Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-02T15:36:10Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
---
Original model is [here](https://civitai.com/models/466145/featureless-mix-pony?modelVersionId=614228).
|
John6666/featureless-mix-pony-v2-sdxl-spo | John6666 | 2024-07-02T15:43:30Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"SPO",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-02T15:36:39Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
- SPO
---
Original model is [here](https://civitai.com/models/466145/featureless-mix-pony?modelVersionId=614228).
|
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