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stephanie-jung/synthetic_training_output_5k | stephanie-jung | 2024-07-01T22:58:49Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:cyberseclabs/bert-classify-url-v1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T22:57:58Z | ---
base_model: cyberseclabs/bert-classify-url-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: synthetic_training_output_5k
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. -->
# synthetic_training_output_5k
This model is a fine-tuned version of [cyberseclabs/bert-classify-url-v1](https://huggingface.co/cyberseclabs/bert-classify-url-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Roc Auc: 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0898 | 0.3906 | 250 | 0.0132 | 0.9971 | 0.8761 | 0.99 | 0.9296 | 0.9991 |
| 0.0431 | 0.7812 | 500 | 0.0033 | 0.9994 | 0.9802 | 0.99 | 0.9851 | 0.9999 |
| 0.0349 | 1.1719 | 750 | 0.0035 | 0.9992 | 0.9706 | 0.99 | 0.9802 | 0.9999 |
| 0.0137 | 1.5625 | 1000 | 0.0065 | 0.9986 | 0.9346 | 1.0 | 0.9662 | 1.0000 |
| 0.0207 | 1.9531 | 1250 | 0.0014 | 0.9996 | 0.9804 | 1.0 | 0.9901 | 1.0 |
| 0.0079 | 2.3438 | 1500 | 0.0005 | 0.9998 | 0.9901 | 1.0 | 0.9950 | 1.0 |
| 0.0041 | 2.7344 | 1750 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
kellermp4/RVC-Models | kellermp4 | 2024-07-02T01:17:08Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-01T22:59:57Z | ---
license: openrail
---
|
Laim/Llama-3-WebAgentMap-8B-Instruct_v2 | Laim | 2024-07-01T23:04:19Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:04:19Z | Entry not found |
ncoskun/classification-different-15epoch-f78 | ncoskun | 2024-07-01T23:05:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T23:04:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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lucasdino123/hi | lucasdino123 | 2024-07-01T23:14:21Z | 0 | 0 | null | [
"arxiv:1910.09700",
"region:us"
] | null | 2024-07-01T23:06:42Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
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- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## How to Get Started with the Model
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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]
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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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blockblockblock/NuExtract-bpw4-exl2 | blockblockblock | 2024-07-01T23:09:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] | text-generation | 2024-07-01T23:07:49Z | ---
license: mit
language:
- en
---
# Structure Extraction Model by NuMind 🔥
NuExtract is a version of [phi-3-mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), fine-tuned on a private high-quality synthetic dataset for information extraction.
To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Try it here: https://huggingface.co/spaces/numind/NuExtract
We also provide a tiny(0.5B) and large(7B) version of this model: [NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Benchmark
Benchmark 0 shot (will release soon):
<p align="left">
<img src="result.png" width="600">
</p>
Benchmark fine-tunning (see blog post):
<p align="left">
<img src="result_ft.png" width="600">
</p>
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["", "", ""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
# We recommend using bf16 as it results in negligable performance loss
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
habulaj/243770214997 | habulaj | 2024-07-01T23:08:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:08:04Z | Entry not found |
abdoeid/quran-whisper-ar-tiny-tajweed | abdoeid | 2024-07-01T23:09:49Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-01T23:09:49Z | ---
license: mit
---
|
PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-101 | PhillipGuo | 2024-07-01T23:12:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T23:10:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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]
### 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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Big14/J | Big14 | 2024-07-01T23:10:14Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:10:14Z | Entry not found |
valerielucro/mistral_gsm8k_sft_v1_epoch4 | valerielucro | 2024-07-01T23:11:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:10:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[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
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PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-103 | PhillipGuo | 2024-07-01T23:10:49Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:10:49Z | Entry not found |
PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-102 | PhillipGuo | 2024-07-01T23:13:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T23:11:07Z | ---
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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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- 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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[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
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LeviAckermanUK/ModelsPonyXL | LeviAckermanUK | 2024-07-01T23:11:19Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:11:19Z | Entry not found |
gruhit-patel/PPO-LunarLandar-v2 | gruhit-patel | 2024-07-01T23:11:48Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-01T23:11:42Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -160.03 +/- 83.23
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-104 | PhillipGuo | 2024-07-01T23:12:02Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:12:02Z | Entry not found |
knowhate/counterhate-twitter-hateberttuga | knowhate | 2024-07-01T23:16:04Z | 0 | 0 | keras | [
"keras",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T23:13:43Z | ---
license: apache-2.0
---
|
PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-106 | PhillipGuo | 2024-07-01T23:14:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:14:15Z | Entry not found |
lucasbalponti/split5 | lucasbalponti | 2024-07-01T23:16:02Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:neuralmind/bert-large-portuguese-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T23:15:02Z | ---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: split5
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. -->
# split5
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1687
- Accuracy: 0.9545
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2732 | 1.0 | 8509 | 0.1903 | 0.9339 |
| 0.2389 | 2.0 | 17018 | 0.1403 | 0.9570 |
| 0.2034 | 3.0 | 25527 | 0.1687 | 0.9545 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|
PhillipGuo/hp-lat-llama-No_PCA-epsilon0.0-pgd_layer0-def_layer-1-wikitext-fullrank-away0-sft0-105 | PhillipGuo | 2024-07-01T23:15:23Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:15:23Z | Entry not found |
habulaj/11203136034 | habulaj | 2024-07-01T23:17:35Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:17:27Z | Entry not found |
habulaj/12105195631 | habulaj | 2024-07-01T23:17:52Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:17:50Z | Entry not found |
Edgar404/donut-shivi-cheques-cheques_best_320_test | Edgar404 | 2024-07-02T04:04:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:18:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
blockblockblock/NuExtract-bpw3-exl2 | blockblockblock | 2024-07-01T23:20:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"3-bit",
"exl2",
"region:us"
] | text-generation | 2024-07-01T23:18:35Z | ---
license: mit
language:
- en
---
# Structure Extraction Model by NuMind 🔥
NuExtract is a version of [phi-3-mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), fine-tuned on a private high-quality synthetic dataset for information extraction.
To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Try it here: https://huggingface.co/spaces/numind/NuExtract
We also provide a tiny(0.5B) and large(7B) version of this model: [NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Benchmark
Benchmark 0 shot (will release soon):
<p align="left">
<img src="result.png" width="600">
</p>
Benchmark fine-tunning (see blog post):
<p align="left">
<img src="result_ft.png" width="600">
</p>
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["", "", ""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
# We recommend using bf16 as it results in negligable performance loss
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
Alex01837178373/Qwen2-1.5B-ab | Alex01837178373 | 2024-07-01T23:18:44Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:18:44Z | Entry not found |
milfre/milfre23 | milfre | 2024-07-01T23:19:15Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T23:19:15Z | ---
license: apache-2.0
---
|
habulaj/517822490834 | habulaj | 2024-07-01T23:19:27Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:19:24Z | Entry not found |
tiagoblima/newsdata-bertimbal-balanced | tiagoblima | 2024-07-01T23:37:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T23:20:04Z | Entry not found |
daedalus16/bart-M3d-strat_eff | daedalus16 | 2024-07-01T23:21:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-07-01T23:20:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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jkodiyil/valueClassifyGF-v1 | jkodiyil | 2024-07-02T00:25:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T23:20:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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habulaj/189353163499 | habulaj | 2024-07-01T23:22:02Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:21:41Z | Entry not found |
habulaj/176055151249 | habulaj | 2024-07-01T23:26:21Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:26:18Z | Entry not found |
Bafeuaniy/lora_model | Bafeuaniy | 2024-07-01T23:26:41Z | 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 | 2024-07-01T23:26:32Z | ---
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:** Bafeuaniy
- **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)
|
habulaj/73934408408 | habulaj | 2024-07-01T23:28:10Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:27:57Z | Entry not found |
kyynaama/Ahma-3B_checkpoint_3140-exl2-6bpw | kyynaama | 2024-07-01T23:40:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T23:29:06Z | ---
library_name: transformers
tags: []
---
This is the 6bpw exllamav2 quant of Finnish-NLP/Ahma-3B_hf_2024_06_20_08_52_28_checkpoint-3140
Original model card:
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is first release candidate for Ahma-3B-instruct/chat model
These are preliminary scores, official scores coming later \
<b>MT Bench: </b> \
roleplay, score 5.6 \
extraction, score 2.1 \
reasoning, score 4.1 \
math, score 4.1 \
writing, score 6.8 \
stem, score 4.4 \
humanities, score 4.9 \
mt_bench avg, score 4.571428571428571
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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blockblockblock/NuExtract-bpw3.5-exl2 | blockblockblock | 2024-07-01T23:31:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-07-01T23:29:20Z | ---
license: mit
language:
- en
---
# Structure Extraction Model by NuMind 🔥
NuExtract is a version of [phi-3-mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), fine-tuned on a private high-quality synthetic dataset for information extraction.
To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Try it here: https://huggingface.co/spaces/numind/NuExtract
We also provide a tiny(0.5B) and large(7B) version of this model: [NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Benchmark
Benchmark 0 shot (will release soon):
<p align="left">
<img src="result.png" width="600">
</p>
Benchmark fine-tunning (see blog post):
<p align="left">
<img src="result_ft.png" width="600">
</p>
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["", "", ""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
# We recommend using bf16 as it results in negligable performance loss
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
abhayesian/LLama3_HarmBench_NoAttack_3 | abhayesian | 2024-07-01T23:30:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:30:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
NotFabian/falcon-7b-qlora-chat-support-bot-faq | NotFabian | 2024-07-01T23:33:00Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:32:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Card Contact
[More Information Needed] |
habulaj/129828104759 | habulaj | 2024-07-01T23:32:46Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:32:44Z | Entry not found |
SRaswan/finbert-dict | SRaswan | 2024-07-01T23:32:46Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:32:46Z | Entry not found |
habulaj/7278653406 | habulaj | 2024-07-01T23:33:42Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:33:39Z | Entry not found |
habulaj/3271830514 | habulaj | 2024-07-01T23:33:59Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:33:53Z | Entry not found |
habulaj/132778108166 | habulaj | 2024-07-01T23:33:58Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:33:56Z | Entry not found |
maheshmnj/gemma-Code-Instruct-Finetune-test | maheshmnj | 2024-07-01T23:34:21Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:34:21Z | Entry not found |
Badral/whisper-large3-mon | Badral | 2024-07-02T01:22:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"automatic-speech-recognition",
"mn",
"dataset:mozilla-foundation/common_voice_17_0",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-01T23:38:55Z | ---
library_name: transformers
datasets:
- mozilla-foundation/common_voice_17_0
language:
- mn
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
# 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:** Badral
- **Model type:** Transformer
- **Language(s) (NLP):** Mongolian
- **Finetuned from model:** Whisper-large-v3
### 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
blockblockblock/NuExtract-bpw4.4-exl2 | blockblockblock | 2024-07-01T23:42:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-07-01T23:39:48Z | ---
license: mit
language:
- en
---
# Structure Extraction Model by NuMind 🔥
NuExtract is a version of [phi-3-mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), fine-tuned on a private high-quality synthetic dataset for information extraction.
To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Try it here: https://huggingface.co/spaces/numind/NuExtract
We also provide a tiny(0.5B) and large(7B) version of this model: [NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Benchmark
Benchmark 0 shot (will release soon):
<p align="left">
<img src="result.png" width="600">
</p>
Benchmark fine-tunning (see blog post):
<p align="left">
<img src="result_ft.png" width="600">
</p>
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["", "", ""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
# We recommend using bf16 as it results in negligable performance loss
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
Kaizennns/limonkiz | Kaizennns | 2024-07-02T02:19:11Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:40:27Z | Entry not found |
kanoml/car-detection-yolo | kanoml | 2024-07-01T23:47:25Z | 0 | 0 | null | [
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T23:43:14Z | ---
license: apache-2.0
---
Car-Detection YOLOv8 model
Metrics:
|
habulaj/12066395870 | habulaj | 2024-07-01T23:43:25Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:43:18Z | Entry not found |
habulaj/1596126627 | habulaj | 2024-07-01T23:44:56Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:44:54Z | Entry not found |
habulaj/6614049334 | habulaj | 2024-07-01T23:45:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:45:31Z | Entry not found |
ncoskun/classification-another-15epoch-f84 | ncoskun | 2024-07-01T23:50:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T23:49:37Z | ---
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] |
blockblockblock/NuExtract-bpw4.8-exl2 | blockblockblock | 2024-07-01T23:53:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-07-01T23:51:20Z | ---
license: mit
language:
- en
---
# Structure Extraction Model by NuMind 🔥
NuExtract is a version of [phi-3-mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), fine-tuned on a private high-quality synthetic dataset for information extraction.
To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Try it here: https://huggingface.co/spaces/numind/NuExtract
We also provide a tiny(0.5B) and large(7B) version of this model: [NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Benchmark
Benchmark 0 shot (will release soon):
<p align="left">
<img src="result.png" width="600">
</p>
Benchmark fine-tunning (see blog post):
<p align="left">
<img src="result_ft.png" width="600">
</p>
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["", "", ""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
# We recommend using bf16 as it results in negligable performance loss
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
SonicInGug/SpongeBob-June-30th-2000 | SonicInGug | 2024-07-01T23:52:31Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:52:12Z | Entry not found |
neural-commons/upscaling-error-v1 | neural-commons | 2024-07-02T17:26:03Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:52:55Z | Entry not found |
Augusto777/vit-base-patch16-224-ve-U13b-80RX3 | Augusto777 | 2024-07-02T00:06:33Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-07-01T23:53:01Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U13b-80RX3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9130434782608695
---
<!-- 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. -->
# vit-base-patch16-224-ve-U13b-80RX3
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4344
- Accuracy: 0.9130
## 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: 4.74e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.33 | 0.99 | 51 | 1.3133 | 0.3478 |
| 1.0288 | 2.0 | 103 | 1.0045 | 0.5652 |
| 0.7322 | 2.99 | 154 | 0.7309 | 0.8043 |
| 0.5476 | 4.0 | 206 | 0.6316 | 0.7826 |
| 0.2863 | 4.99 | 257 | 0.5598 | 0.8043 |
| 0.3149 | 6.0 | 309 | 0.5428 | 0.8478 |
| 0.1489 | 6.99 | 360 | 0.5150 | 0.8696 |
| 0.1134 | 8.0 | 412 | 0.4585 | 0.8043 |
| 0.1613 | 8.99 | 463 | 0.6284 | 0.8478 |
| 0.1855 | 10.0 | 515 | 0.5985 | 0.8478 |
| 0.1908 | 10.99 | 566 | 1.0336 | 0.7391 |
| 0.2293 | 12.0 | 618 | 0.7746 | 0.8043 |
| 0.1414 | 12.99 | 669 | 0.6517 | 0.8261 |
| 0.0877 | 14.0 | 721 | 0.5639 | 0.8261 |
| 0.1302 | 14.99 | 772 | 0.7687 | 0.8261 |
| 0.047 | 16.0 | 824 | 0.6773 | 0.8696 |
| 0.1045 | 16.99 | 875 | 0.4344 | 0.9130 |
| 0.0751 | 18.0 | 927 | 1.0160 | 0.7391 |
| 0.1141 | 18.99 | 978 | 0.6643 | 0.8696 |
| 0.1756 | 20.0 | 1030 | 0.5582 | 0.8913 |
| 0.1212 | 20.99 | 1081 | 0.5641 | 0.8913 |
| 0.0903 | 22.0 | 1133 | 0.6990 | 0.8261 |
| 0.0693 | 22.99 | 1184 | 0.5548 | 0.8913 |
| 0.0048 | 24.0 | 1236 | 0.6958 | 0.8478 |
| 0.0785 | 24.99 | 1287 | 0.7886 | 0.8043 |
| 0.0373 | 26.0 | 1339 | 0.6345 | 0.8478 |
| 0.0763 | 26.99 | 1390 | 0.6830 | 0.8696 |
| 0.0621 | 28.0 | 1442 | 0.7294 | 0.8478 |
| 0.0367 | 28.99 | 1493 | 0.6636 | 0.8696 |
| 0.0124 | 30.0 | 1545 | 0.8031 | 0.8478 |
| 0.0759 | 30.99 | 1596 | 0.7076 | 0.8696 |
| 0.0786 | 32.0 | 1648 | 0.8024 | 0.8261 |
| 0.0487 | 32.99 | 1699 | 0.7927 | 0.8696 |
| 0.0664 | 34.0 | 1751 | 0.9607 | 0.8261 |
| 0.0054 | 34.99 | 1802 | 0.9702 | 0.8261 |
| 0.0277 | 36.0 | 1854 | 0.8351 | 0.8261 |
| 0.0025 | 36.99 | 1905 | 0.9318 | 0.8261 |
| 0.0188 | 38.0 | 1957 | 0.8995 | 0.8478 |
| 0.0385 | 38.99 | 2008 | 0.8928 | 0.8478 |
| 0.0474 | 39.61 | 2040 | 0.8863 | 0.8478 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
bernardzzz/test | bernardzzz | 2024-07-01T23:53:47Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:53:47Z | Entry not found |
ichrak550/llama-model-finetunned6 | ichrak550 | 2024-07-02T00:03:35Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T23:54:20Z | Entry not found |
habulaj/171126146998 | habulaj | 2024-07-01T23:55:07Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T23:55:04Z | Entry not found |
quirky-lats-at-mats/wmdp_ga_bio_2 | quirky-lats-at-mats | 2024-07-01T23:56:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T23:56:23Z | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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|
rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-frozen | rcannizzaro | 2024-07-02T18:23:21Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-07-01T23:58:23Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
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. -->
# Text-to-image finetuning - rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-frozen
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **osazuwa/dsprite-counterfactual** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A very large square, with no rotation, at the center horizontally and vertically.', 'A very large ellipse, with no rotation, at the center horizontally and vertically.', 'A very large heart shape, with no rotation, at the center horizontally and vertically.']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-frozen", torch_dtype=torch.float16)
prompt = "A very large square, with no rotation, at the center horizontally and vertically."
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 3
* Learning rate: 1e-05
* Batch size: 100
* Gradient accumulation steps: 4
* Image resolution: 64
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ricardocannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-frozen/runs/37krt6lb).
## 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] |
cozfuttu/dataturk | cozfuttu | 2024-07-02T00:02:09Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:02:08Z | Entry not found |
CrackedFl/Sampha | CrackedFl | 2024-07-02T00:48:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:02:25Z | Entry not found |
habulaj/158648136400 | habulaj | 2024-07-02T00:04:23Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:04:21Z | Entry not found |
rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-unfrozen | rcannizzaro | 2024-07-02T18:28:40Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-07-02T00:04:48Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
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. -->
# Text-to-image finetuning - rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-unfrozen
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **osazuwa/dsprite-counterfactual** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A very large square, with no rotation, at the center horizontally and vertically.', 'A very large ellipse, with no rotation, at the center horizontally and vertically.', 'A very large heart shape, with no rotation, at the center horizontally and vertically.']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("rcannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-unfrozen", torch_dtype=torch.float16)
prompt = "A very large square, with no rotation, at the center horizontally and vertically."
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 3
* Learning rate: 1e-05
* Batch size: 100
* Gradient accumulation steps: 4
* Image resolution: 64
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ricardocannizzaro/sd-dsprites-counterfactual-with-vae-loss-vae-unfrozen/runs/78d8xaoa).
## 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] |
Piotrasz/Llama-2-7b-hf-ROME-100-en | Piotrasz | 2024-07-02T00:08:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:05:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
z3n7r4ck3r/filtered_dataset_20240702_020530 | z3n7r4ck3r | 2024-07-02T00:05:30Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:05:30Z | Entry not found |
habulaj/86377152269 | habulaj | 2024-07-02T00:06:43Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:06:34Z | Entry not found |
mqliu/mantis-8b-idefics2_1024_qlora_reproduce | mqliu | 2024-07-02T22:28:05Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-02T00:07:25Z | Entry not found |
lordspline/gemma-pruned | lordspline | 2024-07-02T09:32:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:08:41Z | ---
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]
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[More Information Needed]
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[More Information Needed]
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Dmenorsz/FB | Dmenorsz | 2024-07-02T00:14:01Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-02T00:12:19Z | ---
license: openrail
---
|
sander-wood/melodyt5 | sander-wood | 2024-07-03T01:12:08Z | 0 | 1 | null | [
"music",
"dataset:sander-wood/melodyhub",
"arxiv:2407.02277",
"arxiv:2402.19155",
"license:mit",
"region:us"
] | null | 2024-07-02T00:14:03Z | ---
license: mit
datasets:
- sander-wood/melodyhub
tags:
- music
---
# MelodyT5: A Unified Score-to-Score Transformer for Symbolic Music Processing [ISMIR 2024]
This repository contains the code for the MelodyT5 model as described in the paper [MelodyT5: A Unified Score-to-Score Transformer for Symbolic Music Processing](https://arxiv.org/abs/2407.02277).
MelodyT5 is an unified framework for symbolic music processing, using an encoder-decoder architecture to handle multiple melody-centric tasks, such as generation, harmonization, and segmentation, by treating them as score-to-score transformations. Pre-trained on [MelodyHub](https://huggingface.co/datasets/sander-wood/melodyhub), a large dataset of melodies in ABC notation, it demonstrates the effectiveness of multi-task transfer learning in symbolic music processing.
## Model Description
In the domain of symbolic music research, the progress of developing scalable systems has been notably hindered by the scarcity of available training data and the demand for models tailored to specific tasks. To address these issues, we propose MelodyT5, a novel unified framework that leverages an encoder-decoder architecture tailored for symbolic music processing in ABC notation. This framework challenges the conventional task-specific approach, considering various symbolic music tasks as score-to-score transformations. Consequently, it integrates seven melody-centric tasks, from generation to harmonization and segmentation, within a single model. Pre-trained on MelodyHub, a newly curated collection featuring over 261K unique melodies encoded in ABC notation and encompassing more than one million task instances, MelodyT5 demonstrates superior performance in symbolic music processing via multi-task transfer learning. Our findings highlight the efficacy of multi-task transfer learning in symbolic music processing, particularly for data-scarce tasks, challenging the prevailing task-specific paradigms and offering a comprehensive dataset and framework for future explorations in this domain.
We provide the codes of MelodyT5 on [GitHub](https://github.com/sanderwood/melodyt5).
## ABC Notation
ABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.
For those looking to interact with ABC notation in various ways, there are several tools available:
1. **[Online ABC Player](https://abc.rectanglered.com/):** This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.
2. **[ABC Sheet Music Editor - EasyABC](https://easyabc.sourceforge.net/):** EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.
To learn more about ABC notaton, please see [ABC Examples](https://abcnotation.com/examples) and [ABC Strandard](https://abcnotation.com/wiki/abc:standard).
## Installation
To set up the MelodyT5 environment and install the necessary dependencies, follow these steps:
1. **Create and Activate Conda Environment**
```bash
conda create --name melodyt5 python=3.7.9
conda activate melodyt5
```
2. **Install Dependencies**
```bash
pip install -r requirements.txt
```
3. **Install Pytorch**
```bash
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
```
4. **Download Pre-trained MelodyT5 Weights (Optional)**
For those interested in starting with pre-trained models, MelodyT5 weights are available on [Hugging Face](https://huggingface.co/sander-wood/melodyt5/blob/main/weights.pth). This step is optional but recommended for users looking to leverage the model's capabilities without training from scratch.
## Usage
- `config.py`: Configuration settings for training and inference.
- `generate.py`: Perform inference tasks (e.g., generation and conversion) using pre-trained models.
- `train-cls.py`: Training script for classification models.
- `train-gen.py`: Training script for generative models.
- `utils.py`: Utility functions supporting model operations and data processing.
### Setting Up Inference Parameters
Before running the inference script, you can configure the following parameters in `config.py` or directly via command-line arguments:
- `-num_tunes`: Number of independently computed returned tunes (default: 3)
- `-max_patch`: Maximum length in tokens of each tune (default: 128)
- `-top_p`: Tokens within the sample operation of text generation (default: 0.8)
- `-top_k`: Tokens within the sample operation of text generation (default: 8)
- `-temperature`: Temperature of the sampling operation (default: 2.6)
- `-seed`: Seed for random state (default: None)
- `-show_control_code`: Whether to show control codes (default: True)
These parameters control how the model generates melodies based on the input provided in `prompt.txt`.
### Running Inference
To perform inference tasks using MelodyT5, follow these steps:
1. **Prepare Your Prompt**
- Edit `prompt.txt` to specify the task and input for the model. Each line in `prompt.txt` should contain a single prompt.
2. **Execute Inference**
- Run the following command to execute the inference script:
```bash
python inference.py -num_tunes 3 -max_patch 128 -top_p 0.8 -top_k 8 -temperature 2.6 -seed <seed_value> -show_control_code True
```
Replace `<seed_value>` with your chosen seed value or leave it as `None` for a random seed.
3. **Interpreting the Output**
- The script will generate melodies based on the prompts specified in `prompt.txt` using the configured parameters.
## How to Use
Follow these steps to effectively utilize MelodyT5 for symbolic music processing:
1. Prepare Your Data
Ensure your dataset follows the format and style of MelodyHub, which uses ABC notation for uniform representation of melodies. If not using MelodyHub data, adapt your dataset to match this style.
2. Configure Your Model
Adjust model hyperparameters, training parameters, and file paths in the config.py file.
3. Train the Model
Run the train.py script to train MelodyT5. Use the following command, adjusting for your specific setup:
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py
```
This command utilizes distributed training across multiple GPUs (modify --nproc_per_node as needed).
4. Run Inference
To perform inference tasks such as melody generation or harmonization, execute `inference.py`. The script reads prompts from `prompt.txt` to specify the task and input for the model. Customize prompts in `prompt.txt` to define different tasks and inputs for MelodyT5. Refer to the examples below for guidance on setting up prompts.
Ensure the encoder input is complete, while the output (decoder input) is optional. If you need the model to continue a given output, use `%%input` and `%%output` to mark the beginning of each section. Additionally, the output must not contain incomplete bars. Here is an example prompt:
```
%%input
%%variation
L:1/8
M:6/8
K:D
|: AFD DFA | Add B2 A | ABA F3 | GFG EFG | AFD DFA | Add B2 A | ABA F2 E | FDD D3 :: fdd ede |
fdd d2 g | fdd def | gfg e2 g | fed B2 A | AdF A3 | ABA F2 E | FDD D3 :|
%%output
E:8
L:1/8
M:6/8
K:D
|: B |
```
## Inference Examples
Below are the MelodyT5 results on seven MelodyHub tasks, using random samples from the validation set. Three independent outputs were generated without cherry-picking. Each `X:0` output corresponds to the original input for that task and is not generated by the model, while `X:1`, `X:2`, and `X:3` are generated outputs.
To view the musical scores and listen to the tunes, you can use online ABC notation platforms such as [Online ABC Player](https://abc.rectanglered.com/) or the [ABC Sheet Music Editor - EasyABC](https://easyabc.sourceforge.net/). Simply copy and paste the ABC notation into these tools to see the sheet music and hear the audio playback.
1. Cataloging
```
%%input
%%cataloging
T:
O:
Y:
L:1/4
Q:1/4=120
M:3/4
K:C
A/0 A/0 B/0 (c/0 B/0) A/0 A/0 ^G/0 A/0 (e/0 f/0) (=g/0 f/0) e/0 | (f e) d | e2 e | (d c) B |
(c B) A | ^G2 e | f2 e | d2 c | B2 A | ^G2 c | (d e) d | (e f) g | e2 e | (e c) c | (d B) B |
(c A) A | ^G2 (A/B/) | (c d) c | c2 B | c2 (c/d/) | (e f) e | e2 d | e2 (e/f/) | g2 g | (g e) e |
f2 e | d2 e | (e d) e | (c d) e | (d e) (c/d/) | B2 c | B2 A | ^G2 A | A2 c | B2 A | ^G2 A |
A2 x |]
```
```
%%output
X:0
T:HET GODEN BANKET. Ter Huwfeest van Muzika en Poezy
O:Netherlands
Y:vocal
X:1
T:WILHELLTOF GERSCHANDS
O:Netherlands
Y:vocal
X:2
T:MIEN IS LOVEN, VRIENDAAT OP EEN
O:Netherlands
Y:vocal
X:3
T:Het lied en tijd over
O:Netherlands
Y:vocal
```
2. Generation
```
%%input
%%generation
```
```
%%output
X:0
S:2
B:8
E:5
B:8
L:1/8
M:2/2
K:G
|: A3 c BG G2 | BGBd g2 fg | eA A2 BGBd | egdc BG G2 | A3 c BG G2 | BGBd g3 a | bgag egfa |
gedc BG G2 :: bg g2 agef | g3 e dega | bg g2 aged | eaag a2 ga | bg g2 agef | g3 e dega |
bgag egfa | gedc BG G2 :|
X:1
S:2
B:5
E:5
B:9
L:1/8
M:2/2
K:Amin
cd | e3 c d2 cd | e3 d c2 Ac | B3 c A3 A | A4 :: G2 | c3 c c3 c | d3 d e2 fe | f2 f2 e2 d2 |
g3 a g2 cd | e3 d c2 dc | e3 d c2 Ac | B3 c A3 A | A4 :|
X:2
S:3
B:1
E:0
B:17
E:0
E:5
B:17
L:1/8
M:3/4
K:G
D |:"G" G2 GABc |"G/B" d2 B3 G |"C" E3 G E2 |"G" D3 DEF |"G" G2 GABc |"G/B" d2 B3 G |
"D" E2 G2 B2 |"D" A4 DD |"G" G2 GABc |"G/B" d2 B3 G |"C" E3 G E2 |"G" D3 DEF |"G" G2 GABc |
"G/B" d2 B3 G |"D" E2 G2 F2 |1"G" G4 GD :|2"G" G4 DG |:"C" E G3 E2 |"G" D3 E D2 |"G" B3 A G2 |
"G" B d3 B2 |"C" e c3 e2 |"G" d3 B G2 |"Am" E2 G2 B2 |"D" A4 DG |"C" E G3 E2 |"G" D3 E D2 |
"G" B3 A G2 |"G" B d3 B2 |"C" e c3 e2 |"G" d3 B G2 |"D" E2 G2 F2 |1"G" G4 DG :|2"G" G4 z2 |]
X:3
S:3
B:9
E:5
B:9
E:5
E:5
B:9
L:1/8
M:4/4
K:D
|: A2 | d2 d2 c2 c2 | B2 A2 F2 A2 | B2 B2 c2 c2 | d2 d2 d2 c2 | d2 d2 c2 c2 | B2 A2 F2 A2 |
B2 B2 c2 c2 | d6 ::[K:D] A2 | d2 d2 cB c2 | Bc BA G2 FG | A3 B AG FE | D2 DE FG AB | d2 d2 cB c2 |
Bc BA G2 FG | A3 B AG FE | D6 :: A2 | F2 DF A2 FA | G2 EC E2 A2 | F2 DF A2 FA | G2 EC A,2 A2 |
F2 DF A2 FA | G2 EC E2 A2 | F2 DF A2 AF | G2 EC D2 :|
```
3. Harmonization
```
%%input
%%harmonization
L:1/4
M:4/4
K:B
B, | F D/C/ B, F | G3/4A/8B/8 G !fermata!F F |
G A B A | G/B/A/G/ !fermata!F D |
G F E D | C2 !fermata!B, :| z | F2 !fermata!D2 |
F2 !fermata!D2 | D D C C | D D C D |
E D C2 | !fermata!B,2 B A | G F E D |
C2 !fermata!B, |]
```
```
%%output
X:0
E:5
L:1/4
M:4/4
K:B
"B" B, |"F#/A#" F"B" D/C/"G#m" B,"D#m" F |"G#m7/B" G3/4A/8B/8"C#" G"F#" !fermata!F"B" F |
"E" G"A#dim/D#" A"B/D#" B"F#" A |"G#m7/B" G/B/"A#m/C#"A/G/"F#" !fermata!F"B" D |
"E" G"B/D#" F"C#m7" E"B" D |"F#sus4" C2"B" !fermata!B, :| z |"F#/A#" F2"B" !fermata!D2 |
"F#" F2"B" !fermata!D2 |"B" D"B/D#" D"F#" C"F#" C |"B/D#" D"B/D#" D"F#" C"B#dim/E" D |
"C#m" E"G#m" D"F#7/E" C2 |"B" !fermata!B,2"G#m" B"D#" A |"E" G"D#m/F#" F"Emaj7/G#" E"B" D |
"F#7" C2"B" !fermata!B, |]
X:1
E:5
L:1/4
M:4/4
K:B
"B" B, |"F#/A#" F"B" D/C/"G#m" B,"B/D#" F |"G#m7/B" G3/4A/8B/8"C#" G"F#" !fermata!F"B" F |
G"F#/A#" A"B/D#" B"F#" A |"G#m7/B" G/B/"A#m/C#"A/G/"F#" !fermata!F"B" D |
G"B/D#" F"C#m" E"D#m7b5/C#" D"A" |"F#7" C2"B" !fermata!B, :|"F#" z |"F#/A#" F2"B" !fermata!D2 |
"B" F2"B" !fermata!D2 |"B" D"B/D#" D"F#" C"F#" C |"B/D#" D"B/D#" D"F#" C"B#dim/E" D |
E"G#m" D"F#7/E" C"G#m"2 |"G#m" !fermata!B,2"G#m" B"A#" A |"G#m" G"D#m/F#" F"Emaj7/G#" E"B" D |
"F#7" C2"B" !fermata!B, |]
X:2
E:5
L:1/4
M:4/4
K:B
"B" B, |"F#/A#" F"B" D/C/"G#m" B,"D#m" F |"G#m7/B" G3/4A/8B/8"C#" G"F#" !fermata!F"B" F |
G"F#" A"B/D#" B"F#" A"B" |"G#m/B" G/B/"A#dim/C#"A/G/"D#" !fermata!F"G#m" D |
"E" G"E/G#" F"A#m7b5/G#" E"B/F#" D |"F#7" C2"B" !fermata!B, :|"B" z |"D#m" F2"G#m" !fermata!D2 |
"D#m" F2"G#m" !fermata!D2 | D"G#m" D"F#/A#" C"C#m" C | D"B/D#" D"B/D#" C"B" D"F#sus4" |
"C#m" E"G#m" D"F#" C2 | !fermata!B,2"B" B"F#" A |"E" G"F#/A#" F"C#m" E"D#m" D |
"F#7" C2"B" !fermata!B, |]
X:3
E:5
L:1/4
M:4/4
K:B
"B" B, |"F#/A#" F"B" D/C/"G#m" B,"B/D#" F |"G#m7/B" G3/4A/8B/8"C#" G"F#" !fermata!F"B" F |
G"F#" A"B/D#" B"F#" A"B" |"G#m/B" G/B/"A#dim/C#"A/G/"D#" !fermata!F"B" D |
G"E/G#" F"C#m7" E"B" D"F#" |"F#sus4" C2"B" !fermata!B, :| z |"F#/A#" F2"B" !fermata!D2 |
"F#" F2"B" !fermata!D2 | D"B" D"B/D#" C"B/C#" C | D"B/D#" D"B/D#" C"B" D |
E"C#m" D"F#sus4" C"D#7"2 |"G#m" !fermata!B,2"G#m" B"A#" A |"E" G"D#m/F#" F"E/G#" E"B" D |
"F#7" C2"B" !fermata!B, |]
```
4. Melodization
```
%%input
%%melodization
L:1/8
M:6/8
K:G
|: z |"G" z6 | z6 |"Am" z6 |"C" z3"D7" z3 |"G" z6 | z6 |"Am" z3"D7" z3 |"G" z4 z :: z |"C" z6 |
z6 |"Bm" z6 |"Em" z6 |"C" z3"D7" z3 |"Em" z3"Am" z3 |"D7" z6 |"G" z4 :|
```
```
%%output
X:0
E:5
L:1/8
M:6/8
K:G
|: B/A/ |"G" GDE G2 A | Bgf gdB |"Am" ABc BGA |"C" BcA"D7" BGE |"G" GDE G2 A | Bgf gdB |
"Am" ABc"D7" BcA |"G" BGG G2 :: B/d/ |"C" e2 e e2 e | egf edB |"Bm" d2 d d2 d |"Em" dge dBG |
"C" c2 d"D7" e2 f |"Em" gdB"Am" A2 d |"D7" BGA BcA |"G" BGG G :|
X:1
E:5
L:1/8
M:6/8
K:G
|: d |"G" GBG GBG | BGG G2 B |"Am" cec ABc |"C" ecA"D7" A2 c |"G" BGG BGG | BGB Bcd |"Am" edc"D7" BcA |
"G" BGG G2 :: d |"G" gfg GBd | gfg bag |"Bm" afd Adf |"Bm" afd def |"C" gfg"G" Bcd |
"Em" gdB"Am" cde |"D7" dcB AGF |"G" BGG G2 :|
X:2
E:5
L:1/8
M:6/8
K:G
|: d/c/ |"G" BAB G2 E | D2 D DEG |"Am" ABA AGE |"C" cBc"D7" Adc |"G" BAB G2 E | D2 D DEG |
"Am" ABA"D7" AGA |"G" BGG G2 :: B/c/ |"G" d2 d dBG | Bdd d2 B |"Am" c2 c cAA |"Em" B2 B B2 d |
"C" e2 e"D7" dBA |"Em" B2 d"Am" dBA |"D7" GAB AGA |"G" BGG G2 :|
X:3
E:5
L:1/8
M:6/8
K:G
|: d/c/ |"G" BGG DGG | BGB dcB |"Am" cAA EAA |"C" cBc"D7" edc |"G" BGG DGG | BGB dcB |
"Am" cBc"D7" Adc |"G" BGG G2 :: g/a/ |"G" bgg dgg | bgb bag |"Bm" aff dff |"Bm" afa agf |
"C" egg"G" dgg |"Am" cgg"G" B2 B |"D7" cBc Adc |"G" BGG G2 :|
```
5. Segmentation
```
%%input
%%segmentation
L:1/4
M:4/4
K:Eb
"Cm" c"Cm" c"Cm/Eb" g"Cm" g/a/ |"Bb/D" b"Eb" g"Ab" e"Ddim/F" f/g/ |"Bb7/F" a2"Eb" g2 |
"F7/A" f"Bbsus4" f"Cm" e"Bb/D" f |"Eb" g"Bbsus4" f"Cm" e"Bb/D" d |"Cm7/Eb" c2"Bb" B2 |
"Cm" e"Csus2" d"Cm" e/f/"Cm/Eb" g |"Fm" f/e/"G" d"Ab" c2 |
"G" d"Cm/Eb" c/d/"Cm" e"Gsus4" d |"C" c4 |]
```
```
%%output
X:0
E:9
L:1/4
M:4/4
K:Eb
"Cm" c"Cm" c"Cm/Eb" g"Cm" g/a/ |"Bb/D" b"Eb" g"Ab" e"Ddim/F" f/g/ |"Bb7/F" a2"Eb" !breath!g2 |
"F7/A" f"Bbsus4" f"Cm" e"Bb/D" f |"Eb" g"Bbsus4" f"Cm" e"Bb/D" d |"Cm7/Eb" c2"Bb" !breath!B2 |
"Cm" e"Csus2" d"Cm" e/f/"Cm/Eb" g |"Fm" f/e/"G" d"Ab" !breath!c2 |
"G" d"Cm/Eb" c/d/"Cm" e"Gsus4" d |"C" !breath!c4 |]
X:1
E:9
L:1/4
M:4/4
K:Eb
"Cm" c"Cm" c"Cm/Eb" g"Cm" g/a/ |"Bb/D" b"Eb" g"Ab" e"Ddim/F" f/g/ |"Bb7/F" a2"Eb" !breath!g2 |
"F7/A" f"Bbsus4" f"Cm" e"Bb/D" f |"Eb" g"Bbsus4" f"Cm" e"Bb/D" d |"Cm7/Eb" c2"Bb" !breath!B2 |
"Cm" e"Csus2" d"Cm" e/f/"Cm/Eb" g |"Fm" f/e/"G" d"Ab" !breath!c2 |
"G" d"Cm/Eb" c/d/"Cm" e"Gsus4" d |"C" !breath!c4 |]
X:2
E:9
L:1/4
M:4/4
K:Eb
"Cm" c"Cm" c"Cm/Eb" g"Cm" g/a/ |"Bb/D" b"Eb" g"Ab" e"Ddim/F" f/g/ |"Bb7/F" a2"Eb" !breath!g2 |
"F7/A" f"Bbsus4" f"Cm" e"Bb/D" f |"Eb" g"Bbsus4" f"Cm" e"Bb/D" d |"Cm7/Eb" c2"Bb" !breath!B2 |
"Cm" e"Csus2" d"Cm" e/f/"Cm/Eb" g |"Fm" f/e/"G" d"Ab" !breath!c2 |
"G" d"Cm/Eb" c/d/"Cm" e"Gsus4" d |"C" !breath!c4 |]
X:3
E:9
L:1/4
M:4/4
K:Eb
"Cm" c"Cm" c"Cm/Eb" g"Cm" g/a/ |"Bb/D" b"Eb" g"Ab" e"Ddim/F" f/g/ |"Bb7/F" a2"Eb" !breath!g2 |
"F7/A" f"Bbsus4" f"Cm" e"Bb/D" f |"Eb" g"Bbsus4" f"Cm" e"Bb/D" d |"Cm7/Eb" c2"Bb" !breath!B2 |
"Cm" e"Csus2" d"Cm" e/f/"Cm/Eb" g |"Fm" f/e/"G" d"Ab" !breath!c2 |
"G" d"Cm/Eb" c/d/"Cm" e"Gsus4" d |"C" !breath!c4 |]
```
6. Transcription
```
%%input
%%transcription
L:1/8
M:3/4
K:A
EG A2 A2 | BA G2 A2 | Bc/d/ e2 A2 | BA GF E2 | F2 G2 A2 | Bc d2 e2 |
fd c/4B/4c/4B/4c/4B/4c/4B/4 A2 | A2 A4 | EG A2 A2 | BA G2 A2 | Bc/d/ e2 A2 | BA GF E2 |
F2 G2 A2 | Bc d2 e2 | fd c/4B/4c/4B/4c/4B/4c/4B/4 A2 | A2 A4 | cd e2 ef | =g2 f3 e | dc d2 dB |
AB G>F E2 | F2 G2 A2 | Bc d2 e2 | fd c/4B/4c/4B/4c/4B/4c/4B/4 A2 | A2 A4- | A2 cd e2 |
ef =g2 f2- | fe dc d2 | dB AB G>F | E2 F2 G2 | A2 Bc d2 | e2 fd c/4B/4c/4B/4c/4B/4c/4B/4 |
A2 A2 A2- | A4 z2 |]
```
```
%%output
X:0
E:3
L:1/8
M:3/4
K:A
EG | A2 A2 BA | G2 A2 Bc/d/ | e2 A2 BA | GF E2 F2 | G2 A2 Bc | d2 e2 fd | TB2 A2 A2 | A4 :: cd |
e2 ef =g2 | f3 edc | d2 dB AB | G>F E2 F2 | G2 A2 Bc | d2 e2 fd | TB2 A2 A2 | A6 :|
X:1
E:3
L:1/8
M:3/4
K:A
EG |"A" A2 A2 BA |"E" G2 A2 Bc/d/ |"A" e2 A2 BA |"E" GF E2 F2 |"E" G2 A2 Bc |"D" d2 e2 fd |
"E" TB2 A2 A2 |"A" A4 :| cd |"A" e2 ef =g2 |"D" f3 e dc |"D" d2 dB AB |"E" G>F E2 F2 |
"E" G2 A2 Bc |"D" d2 e2 fd |"E" TB2 A2 A2 |"A" A6 cd |"A" e2 ef =g2 |"D" f3 e dc |"D" d2 dB AB |
"E" G>F E2 F2 |"E" G2 A2 Bc |"D" d2 e2 fd |"E" TB2 A2 A2 |"A" A4 |]
X:2
E:3
L:1/8
M:3/4
K:A
|: EG |"A" A2 A2 BA |"E" G2 A2 Bc/d/ |"A" e2 A2 BA |"E" GF E2 F2 |"G" G2 A2 Bc |"D" d2 e2 fd |
"E" TB2 A2 A2 |"A" A4 :| cd |"A" e2 ef =g2 |"D" f3 e dc |"D" d2 dB AB |"E" G>F E2 F2 |
"G" G2 A2 Bc |"D" d2 e2 fd |"E" TB2 A2 A2 |"A" A6 cd |"A" e2 ef =g2 |"D" f3 e dc |"D" d2 dB AB |
"E" G>F E2 F2 |"G" G2 A2 Bc |"D" d2 e2 fd |"E" TB2 A2 A2 |"A" A6 ||
X:3
E:4
L:1/8
M:3/4
K:A
EG | A2 A2 BA | G2 A2 Bc/d/ | e2 A2 BA | GF E2 F2 | G2 A2 Bc | d2 e2 fd | TB2 A2 A2 | A4 :| cd |
e2 ef =g2 | f3 e dc | d2 dB AB | G>F E2 F2 | G2 A2 Bc | d2 e2 fd | TB2 A2 A2 | A6 || cd | e2 ef =g2 |
f3 e dc | d2 dB AB | G>F E2 F2 | G2 A2 Bc | d2 e2 fd | TB2 A2 A2 | A6 ||
```
7. Variation
```
%%input
%%variation
L:1/8
M:6/8
K:D
|: AFD DFA | Add B2 A | ABA F3 | GFG EFG | AFD DFA | Add B2 A | ABA F2 E | FDD D3 :: fdd ede |
fdd d2 g | fdd def | gfg e2 g | fed B2 A | AdF A3 | ABA F2 E | FDD D3 :|
```
```
%%output
X:0
E:8
L:1/8
M:6/8
K:D
|: B | AFD DFA | Add B2 A | ABA F2 E | FEE E2 B | AFD DF/G/A | Add B2 A | ABA F2 E | FDD D2 :: e |
fdd dcd | fdd d2 e | f^ef d=ef | g2 f efg | ff/e/d B2 d | Add F2 G | ABA F2 E | FDD D2 :|
X:1
E:8
L:1/8
M:6/8
K:D
|: B | AFD DFA | ded B2 A | ABA F2 D | GFG E2 B | AFD DF/G/A | df/e/d B2 A | ABA F2 E | EDD D2 ::
e | fdd e^de | fdd d2 e | f2 f def | g2 f e2 g | fed B2 d | A2 d F3 | ABA F2 E | EDD D2 :|
X:2
E:8
L:1/8
M:6/8
K:D
|: B | AFD DFA | BdB BAF | ABA F2 D | FEE E2 B | AFD DFA | BdB BAF | ABA F2 E |1 FDD D2 :|2
FDD D2 e |: fdd dcd | fdd d2 e | fef def | gfg eag | fed B2 d | A2 d F2 G | ABA F2 E |1
FDD D2 e :|2 FDD D2 ||
X:3
E:5
L:1/8
M:6/8
K:D
|: (d/B/) | AFD DFA | B2 d F2 A | AFD DEF | GFG EFG | AFD DFA | B2 d F2 A | Bdd F2 E | FDD D2 ::
fed dB/c/d | efe efg | fed daa | agf eag | fed B2 d | A2 d F2 A | Bdd F2 E | FDD D2 :|
```
<!-- ## BibTeX
```
@misc{wu2024language,
title={Beyond Language Models: Byte Models are Digital World Simulators},
author={Shangda Wu and Xu Tan and Zili Wang and Rui Wang and Xiaobing Li and Maosong Sun},
year={2024},
eprint={2402.19155},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` --> |
victorhernandezmvh/prueba | victorhernandezmvh | 2024-07-02T00:14:20Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:14:20Z | Entry not found |
habulaj/8918365740 | habulaj | 2024-07-02T00:14:49Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:14:43Z | Entry not found |
habulaj/1427214143 | habulaj | 2024-07-02T00:16:05Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:15:59Z | Entry not found |
martimfasantos/tinyllama-1.1b-sum-sft-full_3epochs | martimfasantos | 2024-07-02T05:50:48Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:martimfasantos/openai-summarize-tldr",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:16:22Z | ---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- martimfasantos/openai-summarize-tldr
model-index:
- name: tinyllama-1.1b-sum-sft-full_3epochs
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. -->
# tinyllama-1.1b-sum-sft-full_3epochs
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the martimfasantos/openai-summarize-tldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1176
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1208 | 0.9997 | 1476 | 2.1248 |
| 2.0925 | 2.0 | 2953 | 2.1174 |
| 2.0766 | 2.9990 | 4428 | 2.1176 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|
habulaj/10863883537 | habulaj | 2024-07-02T00:17:40Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:17:26Z | Entry not found |
habulaj/191819165780 | habulaj | 2024-07-02T00:18:29Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:18:26Z | Entry not found |
habulaj/143321119492 | habulaj | 2024-07-02T00:19:49Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:19:46Z | Entry not found |
Piotrasz/Llama-2-7b-hf-R_ROME-100-en | Piotrasz | 2024-07-02T00:29:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:22:59Z | ---
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.
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mutisya/whisper-large-v3-kam-drL-24_5-v24_23_3 | mutisya | 2024-07-02T15:42:06Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T00:26:10Z | ---
library_name: transformers
tags: []
---
Training time metrics: wer=32.291783589628466 and normalized_wer=28.912832698321655
# 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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
streamtune/ec7df93d-90a4-40b8-9391-cd59e98cd872 | streamtune | 2024-07-02T00:29:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T00:27:05Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** streamtune
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
DiCeyIII/whisper-small-hi | DiCeyIII | 2024-07-02T01:40:31Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T00:30:21Z | Entry not found |
habulaj/8797664702 | habulaj | 2024-07-02T00:30:35Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:30:34Z | Entry not found |
LarryAIDraw/hare_scarxzys | LarryAIDraw | 2024-07-02T00:36:46Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-07-02T00:31:15Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/548789/omagari-hare-or-blue-archive |
victorhernandezmvh/prueba1 | victorhernandezmvh | 2024-07-02T00:31:55Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:31:55Z | Entry not found |
LarryAIDraw/Bronya_Zaychik_SilverwBronya_Zaychik_Silverwing_N-EXHonkai_impact_3rd | LarryAIDraw | 2024-07-02T00:36:59Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-07-02T00:32:17Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/545711/bronya-zaychik-silverwing-n-ex-honkai-impact-3rd |
sosoai/Hansoldeco-Gemma-2-9b-v0.1-mlx | sosoai | 2024-07-02T15:40:52Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"gemma2",
"region:us"
] | null | 2024-07-02T00:32:33Z | ---
tags:
- mlx
---
# sosoai/Hansoldeco-Gemma-2-9b-v0.1-mlx
The Model [sosoai/Hansoldeco-Gemma-2-9b-v0.1-mlx](https://huggingface.co/sosoai/Hansoldeco-Gemma-2-9b-v0.1-mlx) was converted to MLX format from [sosoai/Hansoldeco-Gemma-2-9b-v0.1](https://huggingface.co/sosoai/Hansoldeco-Gemma-2-9b-v0.1) using mlx-lm version **0.15.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sosoai/Hansoldeco-Gemma-2-9b-v0.1-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
LarryAIDraw/erika_scarxzys | LarryAIDraw | 2024-07-02T00:37:10Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-07-02T00:32:43Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/546621/erika-or-blue-archive |
anyasims/ourpo4_uf5_OURSnologloss_zs1.0-aveTrue-s2-1905 | anyasims | 2024-07-02T00:35:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:33:43Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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] |
anyasims/ourpo4_uf5_OURSnologloss_zs0.5-aveTrue-s2-b78f | anyasims | 2024-07-02T00:38:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:36:25Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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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LarryAIDraw/stay_night_all | LarryAIDraw | 2024-07-02T00:46:08Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-07-02T00:37:23Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/500998/all-characters-in-fatestaynight-fatestay-night |
TheFinAI/finllm-8B-v0.2 | TheFinAI | 2024-07-02T00:45:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T00:37:49Z | ---
library_name: transformers
tags: []
---
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Prismchen/llama-3-8b-chat-doctor-2 | Prismchen | 2024-07-02T00:39:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T00:38:40Z | ---
library_name: transformers
tags: []
---
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anyasims/ourpo4_uf5_ORPO-OR_sft1.0_zs1.0-aveTrue-s2-eb16 | anyasims | 2024-07-02T00:39:08Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:39:08Z | Entry not found |
Naveen1231/Trial | Naveen1231 | 2024-07-02T00:40:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:40:20Z | Entry not found |
AIRRC/EVE | AIRRC | 2024-07-02T00:40:49Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-02T00:40:22Z | ---
license: mit
---
|
ejtspider/carafa | ejtspider | 2024-07-02T00:40:26Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T00:40:26Z | Entry not found |
Mehdi1411/Mehdi | Mehdi1411 | 2024-07-02T00:40:32Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T00:40:32Z | ---
license: apache-2.0
---
|
Kijai/MimicMotion_pruned | Kijai | 2024-07-02T00:41:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T00:40:42Z | ---
license: apache-2.0
---
|
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