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sunruiheng/llama3b-healed | sunruiheng | 2024-06-30T10:06:19Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-30T10:06:19Z | ---
license: mit
---
|
itay-nakash/model_8ef32107f0_sweep_usual-jazz-990 | itay-nakash | 2024-06-30T10:07:21Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:07:21Z | Entry not found |
mohammedberhana/finetuned-pipeline | mohammedberhana | 2024-06-30T10:09:35Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:09:04Z | Entry not found |
gilzur/bert-finetuned-ner | gilzur | 2024-06-30T14:10:40Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-30T10:10:52Z | Entry not found |
lielbin/BabyBERTa-aochildes-french-run2-with-Masking-finetuned-Fr-SQuAD | lielbin | 2024-06-30T11:03:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-06-30T10:11:34Z | ---
tags:
- generated_from_trainer
model-index:
- name: BabyBERTa-aochildes-french-run2-with-Masking-finetuned-Fr-SQuAD
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. -->
# BabyBERTa-aochildes-french-run2-with-Masking-finetuned-Fr-SQuAD
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
itay-nakash/model_8ef32107f0_sweep_legendary-fog-991 | itay-nakash | 2024-06-30T10:12:22Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:12:22Z | Entry not found |
whizzzzkid/whizzzzkid_304_3 | whizzzzkid | 2024-06-30T10:15:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T10:14:23Z | Entry not found |
blueyo0/STU-Net_BraTS21 | blueyo0 | 2024-06-30T10:20:14Z | 0 | 0 | null | [
"arxiv:2304.06716",
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T10:16:43Z | ---
license: apache-2.0
---
# Introduction
A fine-tuned version of STU-Net on BraTS21 dataset.
# Links
[official github repo](https://github.com/uni-medical/STU-Net)
# Citation
```
@misc{huang2023stunet,
title={STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training},
author={Ziyan Huang and Haoyu Wang and Zhongying Deng and Jin Ye and Yanzhou Su and Hui Sun and Junjun He and Yun Gu and Lixu Gu and Shaoting Zhang and Yu Qiao},
year={2023},
eprint={2304.06716},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
alexgichamba/phi-2-finetuned-qa-lora-r32-a16_col | alexgichamba | 2024-06-30T10:17:35Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/phi-2",
"region:us"
] | null | 2024-06-30T10:17:07Z | ---
base_model: microsoft/phi-2
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
ElectricIceBird/dqn-SpaceInvadersNoFrameskip-v4 | ElectricIceBird | 2024-06-30T10:18:58Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:18:58Z | Entry not found |
Krameel/tests | Krameel | 2024-06-30T10:19:49Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:19:49Z | Entry not found |
huggingfacepremium/Phi-3-mini-4k-instruct-GGUF | huggingfacepremium | 2024-06-30T10:20:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:20:54Z | Entry not found |
alimboff/nllb-raw | alimboff | 2024-06-30T10:22:45Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | null | 2024-06-30T10:21:17Z | ---
license: cc0-1.0
---
|
cyberdelia/cyberrealistic_semi_realistic | cyberdelia | 2024-06-30T10:23:52Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:22:41Z | Entry not found |
domico/flower | domico | 2024-06-30T10:23:51Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T10:23:09Z | ---
license: apache-2.0
---
|
jddllwqa/google-gemma-2b-1719743020 | jddllwqa | 2024-06-30T10:23:40Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:23:40Z | Entry not found |
SubhamFinbro/test | SubhamFinbro | 2024-06-30T10:26:29Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:25:07Z | Entry not found |
baxtos/bigirnik06-32 | baxtos | 2024-06-30T10:28:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T10:26:09Z | Entry not found |
itay-nakash/model_8ef32107f0_sweep_decent-feather-992 | itay-nakash | 2024-06-30T10:26:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:26:48Z | Entry not found |
baxtos/bigirnik07-32 | baxtos | 2024-06-30T10:34:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T10:32:00Z | Entry not found |
queyongliang/a9-text-models | queyongliang | 2024-06-30T10:34:50Z | 0 | 0 | diffusers | [
"diffusers",
"zh",
"en",
"ja",
"jv",
"fy",
"pt",
"dataset:OpenGVLab/ShareGPT-4o",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:nvidia/HelpSteer2",
"dataset:UCSC-VLAA/Recap-DataComp-1B",
"dataset:tomg-group-umd/pixelprose",
"license:openrail",
"region:us"
] | null | 2024-06-30T10:32:18Z | ---
license: openrail
datasets:
- OpenGVLab/ShareGPT-4o
- HuggingFaceFW/fineweb-edu
- nvidia/HelpSteer2
- UCSC-VLAA/Recap-DataComp-1B
- tomg-group-umd/pixelprose
language:
- zh
- en
- ja
- jv
- fy
- pt
metrics:
- code_eval
- chrf
- bertscore
library_name: diffusers
--- |
stojchet/1d89673eb2f03cb597c3b847aa07f1ab | stojchet | 2024-07-02T09:22:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:34:40Z | Entry not found |
Loren85/Madame-Italian-singer | Loren85 | 2024-06-30T10:38:51Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-06-30T10:37:28Z | ---
license: openrail
---
|
Abdelrahman2922/distilbert-base-uncased-Human_vs_Ai_text_detectionv2 | Abdelrahman2922 | 2024-06-30T11:48:43Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-30T10:38:02Z | Entry not found |
databankbsisyariah/sd15_perturbed_attention_guidance_i2i | databankbsisyariah | 2024-06-30T10:46:21Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T10:39:16Z | ---
license: apache-2.0
---
|
xocion/Mestral_7b_Kokborok_V1 | xocion | 2024-06-30T10:43:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T10:41:43Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** xocion
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
itay-nakash/model_8ef32107f0_sweep_wandering-grass-993 | itay-nakash | 2024-06-30T10:45:22Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:45:22Z | Entry not found |
ContactDoctor/Medical-llava-llama-3-multimodal | ContactDoctor | 2024-06-30T11:09:10Z | 0 | 2 | transformers | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"generated_from_trainer",
"Healthcare & Lifesciences",
"BioMed",
"Medical",
"Multimodal",
"Vision",
"Text",
"Contact Doctor",
"image-text-to-text",
"dataset:collaiborateorg/BioMedData",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-06-30T10:45:58Z | ---
license: llama3
library_name: transformers
tags:
- generated_from_trainer
- Healthcare & Lifesciences
- BioMed
- Medical
- Multimodal
- llava_llama
- Vision
- Text
- Contact Doctor
base_model: meta-llama/Meta-Llama-3-8B-Instruct
thumbnail: https://contactdoctor.in/images/clogo.png
model-index:
- name: Medical-llava-llama-3-multimodal
results: []
datasets:
- collaiborateorg/BioMedData
pipeline_tag: image-text-to-text
---
# Medical-llava-llama-3-multimodal
This model is a fine-tuned Multimodal version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on our custom "BioMedData" text and image datasets.
## Model details
Model Name: Medical-llava-llama-3-multimodal
Base Model: Llama-3-8B-Instruct
Parameter Count: 8 billion
Training Data: Custom high-quality biomedical text and image dataset
Number of Entries in Dataset: 500,000+
Dataset Composition: The dataset comprises of text and image, both synthetic and manually curated samples, ensuring a diverse and comprehensive coverage of biomedical knowledge.
## Model description
Medical-llava-llama-3-multimodal is a specialized large language model designed for biomedical applications. It is finetuned from the Llama-3-8B-Instruct model using a custom dataset containing over 500,000 diverse entries. These entries include a mix of synthetic and manually curated data, ensuring high quality and broad coverage of biomedical topics.
The model is trained to understand and generate text related to various biomedical fields, making it a valuable tool for researchers, clinicians, and other professionals in the biomedical domain.
## Intended uses & limitations
Medical-llava-llama-3-multimodal is intended for a wide range of applications within the biomedical field, including:
1. Research Support: Assisting researchers in literature review and data extraction from biomedical texts.
2. Clinical Decision Support: Providing information to support clinical decision-making processes.
3. Educational Tool: Serving as a resource for medical students and professionals seeking to expand their knowledge base.
## Limitations and Ethical Considerations
Medical-llava-llama-3-multimodal performs well in various biomedical NLP tasks, users should be aware of the following limitations:
Biases: The model may inherit biases present in the training data. Efforts have been made to curate a balanced dataset, but some biases may persist.
Accuracy: The model's responses are based on patterns in the data it has seen and may not always be accurate or up-to-date. Users should verify critical information from reliable sources.
Ethical Use: The model should be used responsibly, particularly in clinical settings where the stakes are high. It should complement, not replace, professional judgment and expertise.
## Training and evaluation
Medical-llava-llama-3-multimodal was trained using NVIDIA A40 GPU's, which provides the computational power necessary for handling large-scale data and model parameters efficiently. Rigorous evaluation protocols have been implemented to benchmark its performance against similar models, ensuring its robustness and reliability in real-world applications.
### Contact Information
For further information, inquiries, or issues related to Biomed-LLM, please contact:
Email: [email protected]
Website: https://www.contactdoctor.in
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- Number of epochs: 3
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.11.0
- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
### Citation
If you use Medical-llava-llama-3-multimodal in your research or applications, please cite it as follows:
@misc{ContactDoctor_MEDLLM,
author = ContactDoctor,
title = {Medical-llava-llama-3-multimodal: A High-Performance Biomedical Language Model},
year = {2024},
howpublished = {https://huggingface.co/ContactDoctor/Medical-llava-llama-3-multimodal},
} |
mlgawd/obj-remover-lama-conv-FFT | mlgawd | 2024-06-30T10:48:46Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-30T10:46:55Z | ---
license: mit
---
|
baxtos/bigirnik10-32 | baxtos | 2024-06-30T10:51:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T10:49:15Z | Entry not found |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-bnb-4bit-smashed | PrunaAI | 2024-06-30T10:51:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-30T10:49:48Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
Sham786/Tortoise-I | Sham786 | 2024-06-30T10:53:18Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:51:33Z | Entry not found |
itay-nakash/model_8ef32107f0_sweep_bumbling-snowflake-994 | itay-nakash | 2024-06-30T10:53:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:53:13Z | Entry not found |
ariscult/kmichelle | ariscult | 2024-06-30T10:54:01Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T10:53:46Z | Entry not found |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-1bit-smashed | PrunaAI | 2024-06-30T10:58:46Z | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-30T10:57:59Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
itay-nakash/model_8ef32107f0_sweep_wise-mountain-995 | itay-nakash | 2024-06-30T11:09:04Z | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-30T10:59:56Z | Entry not found |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-2bit-smashed | PrunaAI | 2024-06-30T11:01:49Z | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-30T11:00:36Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
SahilCarterr/Llama-2-7B-Chat-PEFT | SahilCarterr | 2024-06-30T11:18:08Z | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | 2024-06-30T11:02:13Z | ---
license: mit
---
|
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int2bit-smashed | PrunaAI | 2024-07-01T07:58:59Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:02:28Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int8bit-smashed | PrunaAI | 2024-07-01T07:58:25Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:02:46Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int4bit-smashed | PrunaAI | 2024-07-01T07:58:00Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:02:53Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
quocdat25/A100_trt-llm_engine-dstack_task | quocdat25 | 2024-06-30T11:03:34Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:03:34Z | Entry not found |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-float8bit-smashed | PrunaAI | 2024-07-01T07:57:53Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:04:35Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
whizzzzkid/whizzzzkid_305_4 | whizzzzkid | 2024-06-30T11:07:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T11:06:37Z | Entry not found |
whizzzzkid/whizzzzkid_306_1 | whizzzzkid | 2024-06-30T11:09:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T11:08:34Z | Entry not found |
PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-AWQ-4bit-smashed | PrunaAI | 2024-06-30T11:11:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:mayflowergmbh/Wiedervereinigung-7b-dpo-laser",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-06-30T11:09:53Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mayflowergmbh/Wiedervereinigung-7b-dpo-laser installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/mayflowergmbh-Wiedervereinigung-7b-dpo-laser-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/Wiedervereinigung-7b-dpo-laser")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mayflowergmbh/Wiedervereinigung-7b-dpo-laser before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
itay-nakash/model_8ef32107f0_sweep_robust-terrain-996 | itay-nakash | 2024-06-30T11:14:51Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:14:51Z | Entry not found |
aaalby/SimsVoice | aaalby | 2024-06-30T11:16:21Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-06-30T11:15:40Z | ---
license: openrail
---
|
whizzzzkid/whizzzzkid_307_2 | whizzzzkid | 2024-06-30T11:22:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T11:20:47Z | Entry not found |
Himanshu56/self_trained_model | Himanshu56 | 2024-06-30T11:22:28Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:22:27Z | Entry not found |
whizzzzkid/whizzzzkid_308_5 | whizzzzkid | 2024-06-30T11:24:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T11:22:47Z | Entry not found |
bigbossmonster/vae | bigbossmonster | 2024-06-30T12:45:30Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:28:46Z | Entry not found |
sbgwebseo/bali | sbgwebseo | 2024-06-30T11:39:39Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:39:39Z | Entry not found |
MAJ10/example-model | MAJ10 | 2024-06-30T11:40:15Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-30T11:40:15Z | ---
license: mit
---
|
anshulsinghkamboj/llama-3-8b-chat-doctor | anshulsinghkamboj | 2024-06-30T11:45:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:45:39Z | ---
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] |
JHT213/EDSR_MindSpore | JHT213 | 2024-06-30T11:50:06Z | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2024-06-30T11:46:35Z | ---
license: mit
---
1. [感谢](#感谢)
本项目 `edsr_x2.onnx` 由 [achie27/super-resolution](https://github.com/achie27/super-resolution.git) 项目提供的代码从 PyTorch 转换为 ONNX 得到.
本项目 `edsr_x2.ms` 由 MindSpore Lite 提供的 `converter` 工具从 ONNX 格式转换得到. |
sharmadhruv/summarize_by_bird | sharmadhruv | 2024-06-30T11:48:50Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:48:50Z | Entry not found |
mastermaxin/yukino | mastermaxin | 2024-06-30T11:49:25Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:49:04Z | Entry not found |
itay-nakash/model_73a455d87c_sweep_feasible-grass-997 | itay-nakash | 2024-06-30T11:50:17Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:50:17Z | Entry not found |
Avyay10/llama-3-finetuned-final | Avyay10 | 2024-06-30T13:14:12Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-06-30T11:51:30Z | Entry not found |
Grayx/john_paul_van_damme_57 | Grayx | 2024-06-30T11:52:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:52:37Z | Entry not found |
Grayx/john_paul_van_damme_58 | Grayx | 2024-06-30T11:53:10Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:52:57Z | Entry not found |
asr-africa/wav2vec2-large-xls-r-300m-zulu-vocab | asr-africa | 2024-06-30T11:53:20Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:53:20Z | Entry not found |
JuliusFx/merged_model_exp | JuliusFx | 2024-06-30T11:56:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-30T11:53:46Z | ---
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. -->
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### Downstream Use [optional]
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### Out-of-Scope Use
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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. -->
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### 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]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Qilan28/SD | Qilan28 | 2024-06-30T11:58:56Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T11:55:55Z | ---
license: apache-2.0
---
|
gkngm/finbert-financial-sentiment-analysis-peft | gkngm | 2024-07-01T12:08:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T11:56:57Z | ---
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. -->
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- **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. -->
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Testing Data
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[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]
- **Hours used:** [More Information Needed]
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## Model Card Contact
[More Information Needed] |
gulucaptain/LSTD-video-generation | gulucaptain | 2024-06-30T11:57:29Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | null | 2024-06-30T11:57:29Z | ---
license: afl-3.0
---
|
itay-nakash/model_73a455d87c_sweep_peachy-cherry-998 | itay-nakash | 2024-06-30T11:58:32Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T11:58:32Z | Entry not found |
macrae/meta-llama2-chat-7b-hf-rk3588 | macrae | 2024-06-30T16:49:59Z | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | 2024-06-30T12:00:47Z | ---
license: llama2
---
---
tags:
- llama2
- llama2-7b
- rkllm
- rockchip
- rk3588
---
# Llama 2 Chat 7B for RK3588
This is a conversion from https://huggingface.co/meta-llama/Llama-2-7b-chat-hf to the RKLLM format for Rockchip devices.
This runs on the NPU from the RK3588.
Converted with **RKLLM runtime 1.0.1** using docker template from https://huggingface.co/Pelochus
# License
Same as the original LLM:
https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/LICENSE.txt
|
detek/3ep_FT_synth_llama-3-8b-instruct-bnb-4bit_merged_16bit | detek | 2024-06-30T12:51:20Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-06-30T12:01:28Z | Entry not found |
itay-nakash/model_6c79be4285_sweep_ethereal-oath-999 | itay-nakash | 2024-06-30T12:03:21Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:03:21Z | Entry not found |
itay-nakash/model_0ee07a95db_sweep_dulcet-water-1001 | itay-nakash | 2024-06-30T12:06:02Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:06:02Z | Entry not found |
dantedgp/dummy-model | dantedgp | 2024-06-30T12:08:39Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:08:39Z | Entry not found |
knair1212/atlas_24 | knair1212 | 2024-06-30T12:14:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T12:14:25Z | ---
license: apache-2.0
---
|
lucasbalponti/split3 | lucasbalponti | 2024-06-30T12:18:42Z | 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 | 2024-06-30T12:17:42Z | ---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: split3
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. -->
# split3
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: 0.4186
- Accuracy: 0.8801
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.279 | 1.0 | 8509 | 0.3914 | 0.8580 |
| 0.2259 | 2.0 | 17018 | 0.3829 | 0.8700 |
| 0.1853 | 3.0 | 25527 | 0.4186 | 0.8801 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|
RaagulQB/mistral-7b-Indian-constitution | RaagulQB | 2024-06-30T12:18:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T12:18:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
itay-nakash/model_6c79be4285_sweep_silver-sea-1004 | itay-nakash | 2024-06-30T12:19:05Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:19:05Z | Entry not found |
Grayx/john_paul_van_damme_59 | Grayx | 2024-06-30T12:20:04Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:19:53Z | Entry not found |
Yunxishihao/Bbbb | Yunxishihao | 2024-06-30T12:20:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:20:13Z | Entry not found |
studywithyasharora/llm | studywithyasharora | 2024-06-30T12:23:00Z | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | 2024-06-30T12:23:00Z | ---
license: llama2
---
|
CarlanLark/AIGT-detector-in-domain | CarlanLark | 2024-06-30T12:23:34Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:23:34Z | Entry not found |
AnoxDx/LegendXd | AnoxDx | 2024-06-30T12:24:57Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T12:24:57Z | ---
license: apache-2.0
---
|
AnoxDx/Bbbhhh | AnoxDx | 2024-06-30T12:28:30Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2024-06-30T12:28:30Z | ---
license: bigscience-openrail-m
---
|
Lostghost23/detr-resnet-50-hardhat-finetuned_2 | Lostghost23 | 2024-06-30T12:29:27Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:29:27Z | Entry not found |
bigbossmonster/upscale_model | bigbossmonster | 2024-06-30T12:30:28Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:30:28Z | Entry not found |
kevin009/mistral | kevin009 | 2024-06-30T20:35:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T12:30:37Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Grayx/john_paul_van_damme_60 | Grayx | 2024-06-30T12:31:52Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:31:39Z | Entry not found |
itay-nakash/model_6c79be4285_sweep_wandering-wind-1005 | itay-nakash | 2024-06-30T12:32:08Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:32:08Z | Entry not found |
itay-nakash/model_0ee07a95db_sweep_stilted-haze-1006 | itay-nakash | 2024-06-30T12:32:23Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:32:23Z | Entry not found |
Grayx/john_paul_van_damme_61 | Grayx | 2024-06-30T12:32:50Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:32:37Z | Entry not found |
Polenov2024/Atomic_Heart_Pony_lora | Polenov2024 | 2024-06-30T12:34:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:33:48Z | Entry not found |
ShakedAAA/Mixtral-8x7B-v0.1-Colleen_8k_06_10_replyOnly_5000_fixed_300624-adapters_June | ShakedAAA | 2024-06-30T12:34:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T12:34:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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deepvoice/svc | deepvoice | 2024-06-30T12:55:39Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-30T12:37:58Z | ---
license: mit
---
|
gqszhanshijin/Steel-LLM | gqszhanshijin | 2024-06-30T15:37:04Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T12:40:06Z | ---
license: apache-2.0
---
<div align="center">
# 开源中文预训练语言模型Steel-LLM
由zhanshijin和lishu14创建
</div>
## 👋 介绍
Steel-LLM是一个从零开始预训练中文大模型的项目。我们的目标是使用1T+的数据预训练一个1B左右参数量的中文LLM，对标TinyLlama。项目持续更新，维持3个月+。我们会分享数据收集、数据处理、预训练框架选择、模型设计等全过程，并开源全部代码。让每个人在有8~几十张卡的情况下都能复现我们的工作。
<p align="center">
🐱 <a href="https://github.com/zhanshijinwat/Steel-LLM">Github</a>  
   📑 <a href="https://www.zhihu.com/people/zhan-shi-jin-27">Blog</a>
"Steel(钢)"取名灵感来源于华北平原一只优秀的乐队“万能青年旅店（万青）”。乐队在做一专的时候条件有限，自称是在“土法炼钢”，但却是一张神专。我们训练LLM的条件同样有限，但也希望能炼出好“钢”来。为了让能持续关注我们的同学们有一些参与感，并在未来使用Steel-LLM时让模型更有可能输出你想要的内容，我们会持续收集大家的数据，各种亚文化、冷知识、歌词、小众读物、只有你自己知道的小秘密等等都可以，并训练到我们的LLM中。改编万青一专简介的一句话作为结束语：Steel-LLM完成之时，神经元已经被万亿数据填满。我们渴望这个塞了很多东西的模型还能为你们的数据留下丝缕空地。这样的话，所有用到模型的人，就有可能并肩站在一起。 |
lpetreadg/Llama-3-8B-merged-bf16 | lpetreadg | 2024-06-30T12:51:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-30T12:40:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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JcKosmos74/t5-base-ULPGL-Dissertation | JcKosmos74 | 2024-06-30T12:42:25Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:42:25Z | Entry not found |
Mistermango24/Furry_Enhancer_SDXL_V6.1 | Mistermango24 | 2024-06-30T12:54:22Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2024-06-30T12:44:42Z | ---
license: artistic-2.0
---
|
Finnish-NLP/Ahma-3B_hf_2024_06_29_14_08_01_checkpoint-564 | Finnish-NLP | 2024-07-02T06:41:15Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T12:47:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Ahma-3B-instruct/chat model
mt_bench, score 4.328571428571428 \
extraction, score 2.0 \
humanities, score 4.9 \
math, score 3.5 \
reasoning, score 3.5 \
roleplay, score 4.6 \
writing, score 7.0 \
stem, score 4.8 \
wibe, score 5.837837837837838
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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bigbossmonster/upscale | bigbossmonster | 2024-06-30T12:52:50Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T12:48:28Z | ---
license: apache-2.0
---
|
SADO1102/Watame | SADO1102 | 2024-06-30T12:50:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T12:49:46Z | Entry not found |
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