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TopperThijs/Llama-3-Picture-Des-Finetuned-4epochs15mlm | TopperThijs | 2024-06-27T09:23:47Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-06-27T08:27:35Z | Entry not found |
fazrithe/asn-karir | fazrithe | 2024-06-27T08:27:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T08:27:59Z | ---
license: apache-2.0
---
|
uzbfame/gpt | uzbfame | 2024-06-27T08:29:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:29:23Z | Entry not found |
PrunaAI/llama-moe-LLaMA-MoE-v1-3_0B-2_16-QUANTO-int8bit-smashed | PrunaAI | 2024-07-01T07:57:57Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:llama-moe/LLaMA-MoE-v1-3_0B-2_16",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:29:34Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: llama-moe/LLaMA-MoE-v1-3_0B-2_16
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 llama-moe/LLaMA-MoE-v1-3_0B-2_16 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/llama-moe-LLaMA-MoE-v1-3_0B-2_16-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("llama-moe/LLaMA-MoE-v1-3_0B-2_16")
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 llama-moe/LLaMA-MoE-v1-3_0B-2_16 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/llama-moe-LLaMA-MoE-v1-3_0B-2_16-QUANTO-float8bit-smashed | PrunaAI | 2024-07-01T07:59:59Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:llama-moe/LLaMA-MoE-v1-3_0B-2_16",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:30:08Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: llama-moe/LLaMA-MoE-v1-3_0B-2_16
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 llama-moe/LLaMA-MoE-v1-3_0B-2_16 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/llama-moe-LLaMA-MoE-v1-3_0B-2_16-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("llama-moe/LLaMA-MoE-v1-3_0B-2_16")
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 llama-moe/LLaMA-MoE-v1-3_0B-2_16 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). |
echolessjabs/my_awesome_model | echolessjabs | 2024-06-27T08:31:04Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:31:04Z | Entry not found |
GeorgeNhj/HPQA_mistral_7b | GeorgeNhj | 2024-06-27T08:32:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:32:12Z | Entry not found |
rithwik19/vqa_idefics | rithwik19 | 2024-06-27T08:32:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:32:42Z | ---
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] |
Nelsuh/speecht5_mn | Nelsuh | 2024-06-27T08:32:43Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:32:43Z | Entry not found |
syim/article_summary | syim | 2024-06-28T01:19:22Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T08:33:22Z | ---
license: apache-2.0
---
|
GeorgeNhj/HPQA_mistral_7b_1000 | GeorgeNhj | 2024-06-27T08:33:56Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-27T08:33:56Z | ---
license: mit
---
|
daffabilnadzary1/G-LLaVa-7B-clone | daffabilnadzary1 | 2024-06-27T08:34:09Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-27T08:34:09Z | ---
license: mit
---
|
PrunaAI/llama-moe-LLaMA-MoE-v1-3_5B-2_8-bnb-4bit-smashed | PrunaAI | 2024-06-27T08:40:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama_moe",
"text-generation",
"pruna-ai",
"custom_code",
"base_model:llama-moe/LLaMA-MoE-v1-3_5B-2_8",
"autotrain_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2024-06-27T08:35:31Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: llama-moe/LLaMA-MoE-v1-3_5B-2_8
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 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 llama-moe/LLaMA-MoE-v1-3_5B-2_8 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/llama-moe-LLaMA-MoE-v1-3_5B-2_8-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("llama-moe/LLaMA-MoE-v1-3_5B-2_8")
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 llama-moe/LLaMA-MoE-v1-3_5B-2_8 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). |
BenCasse/eco-cluedo | BenCasse | 2024-06-27T08:35:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:35:39Z | Entry not found |
Sayalik45/tts-1 | Sayalik45 | 2024-06-27T09:24:26Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:37:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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Speedy1237777/Bv3WithIndex | Speedy1237777 | 2024-06-27T09:02:25Z | 0 | 1 | null | [
"region:us"
]
| null | 2024-06-27T08:40:53Z | Entry not found |
sanket1234/OCR_model | sanket1234 | 2024-06-27T08:43:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:42:29Z | Entry not found |
PrunaAI/llama-moe-LLaMA-MoE-v1-3_5B-2_8-QUANTO-float8bit-smashed | PrunaAI | 2024-07-01T07:58:47Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:llama-moe/LLaMA-MoE-v1-3_5B-2_8",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:43:36Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: llama-moe/LLaMA-MoE-v1-3_5B-2_8
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 llama-moe/LLaMA-MoE-v1-3_5B-2_8 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/llama-moe-LLaMA-MoE-v1-3_5B-2_8-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("llama-moe/LLaMA-MoE-v1-3_5B-2_8")
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 llama-moe/LLaMA-MoE-v1-3_5B-2_8 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). |
driwnet/LongFormer-4096-mental-health-es | driwnet | 2024-07-02T22:45:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"longformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-06-27T08:45:05Z | Entry not found |
cmn/lora_Icannot | cmn | 2024-06-27T09:30:59Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-06-27T08:46:59Z | Entry not found |
bsmani/paligemma_vqav2 | bsmani | 2024-06-27T08:48:14Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:48:14Z | Entry not found |
NemoSheng/gemma_fc_ft | NemoSheng | 2024-07-01T06:32:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T08:50:26Z | ---
base_model: unsloth/gemma-7b-it-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
---
# Uploaded model
- **Developed by:** NemoSheng
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-it-bnb-4bit
This gemma 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)
|
LawsonD/g | LawsonD | 2024-06-27T08:50:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:50:34Z | Entry not found |
Kudod/Vistral_finetuned_A100_June27th_1 | Kudod | 2024-06-27T14:05:40Z | 0 | 0 | null | [
"safetensors",
"region:us"
]
| null | 2024-06-27T08:51:06Z | Entry not found |
Temo27Anas/videomae-base-finetuned-ucf101-subset-200frames-a | Temo27Anas | 2024-06-27T08:54:42Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:54:42Z | Entry not found |
Imvignesh/tt-fine-tuned-custom-chatbot-model | Imvignesh | 2024-06-27T08:56:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:56:20Z | Entry not found |
KhanLee0930/SpaceInvadersNoFrameskip-2 | KhanLee0930 | 2024-06-27T08:57:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:57:06Z | Entry not found |
swetanshu-tg/safetensors | swetanshu-tg | 2024-06-27T08:58:15Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:57:51Z | Entry not found |
sanket1234/OCR_MEDIA | sanket1234 | 2024-06-27T09:04:51Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:59:20Z | Entry not found |
lfnothing/distilbert-base-uncased-finetuned-imdb | lfnothing | 2024-06-27T08:59:28Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T08:59:28Z | Entry not found |
TenzinGayche/bo_en_tokenizer_unigram_32k | TenzinGayche | 2024-07-01T10:47:13Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:04:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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TenzinGayche/bo_en_tokenizer_bpe_32k | TenzinGayche | 2024-06-27T09:04:42Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:04:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Likich/llama3-finetune-qualcoding-100 | Likich | 2024-06-27T10:11:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:04:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- 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
### Training Data
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[More Information Needed]
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Noumaan/4bit-pythonGPT | Noumaan | 2024-06-27T09:05:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T09:05:37Z | ---
license: apache-2.0
---
|
Alifaqi/llama-3-8b-chat-doctor | Alifaqi | 2024-06-27T09:07:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:07:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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habulaj/476501446832 | habulaj | 2024-06-27T09:07:22Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:07:19Z | Entry not found |
rohiththumma/Calci | rohiththumma | 2024-06-27T09:14:31Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:13:30Z | Entry not found |
csukuangfj/torch-libs | csukuangfj | 2024-06-29T11:33:37Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:14:05Z | Entry not found |
wt3639/Llama-3-8B-Instruct_RecExp_lora | wt3639 | 2024-06-27T09:19:26Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:14:58Z | Entry not found |
jykim310/Phi-3-mini-128k-instruct-q4f16_1-MLC | jykim310 | 2024-06-27T09:24:56Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:15:50Z | Entry not found |
Likich/llama3-finetune-qualcoding-200 | Likich | 2024-06-27T09:16:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:16:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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4nilcog/turkish-news-mini-fattempt | 4nilcog | 2024-06-27T11:43:30Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:20:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Rearden22/eng_mm_transformer2 | Rearden22 | 2024-06-27T09:21:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:21:06Z | Entry not found |
ashworthiv/GCK1 | ashworthiv | 2024-06-27T09:22:18Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:22:09Z | Entry not found |
Zak587/lara_model | Zak587 | 2024-06-27T09:24:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:23:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Zak587
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Likich/falcon-finetune-qualcoding-100 | Likich | 2024-06-27T09:25:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:25:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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ShapeKapseln33/ManhoodPlus33 | ShapeKapseln33 | 2024-06-27T09:33:54Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:25:57Z | Manhood Plus Gummies Reviews Manhood Plus Gummies est censé être un complément alimentaire exceptionnellement compris qui combat le stress, développe davantage les niveaux d'énergie et intensifie votre exposition sexuelle en général. Le fabricant affirme qu'il contient une gamme complète d'huiles de CBD pour redynamiser votre virilité énergétique, vous permettant d'apprécier le sexe et de combler votre partenaire. Comme l'indique le site de l'autorité, Manhood Plus Gummies contient des concentrés de plantes normaux et aucun THC ne suit. C’est idéal pour les hommes de tout âge et promet de lutter contre les relations sexuelles malheureuses depuis la racine. Consommer deux bonbons chaque jour augmente la puissance sexuelle, la pression de combat et les niveaux de poussée. L’amélioration est protégée et peu susceptible de donner aux clients des effets secondaires.
**[Cliquez ici pour acheter maintenant sur le site officiel de Manhood Plus Gummies](https://slim-gummies-deutschland.de/manhood-plus-fr)**
L’augmentation de la dysfonction érectile peut être attribuée à plusieurs facteurs. Cependant, des découvertes récentes suggèrent que l’âge ou les habitudes de vie n’ont rien à voir avec cela.
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##Qu'est-ce que les gommes d'amélioration masculine Manhood Plus ?
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**[Cliquez ici pour acheter maintenant sur le site officiel de Manhood Plus Gummies](https://slim-gummies-deutschland.de/manhood-plus-fr)**
##Comment fonctionnent les gommes Manhood Plus Male Enhancement ?
Manhood Plus Male Enhancement Gummies agit principalement en modifiant les deux hormones, le cortisol et la testostérone. Les ingrédients du supplément jouent un rôle crucial dans la réduction des niveaux de cortisol, responsables du stress et de l’anxiété qui ont un impact négatif sur les performances sexuelles masculines.
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Lorsque les niveaux de stress sont réduits et que la pulsion sexuelle s’intensifie, le cerveau se concentre sur l’obtention d’érections. L'effet plus léger sur le cerveau et les nerfs permet également d'améliorer les performances sexuelles. De plus, des niveaux d’énergie accrus aident à recharger et à amplifier l’endurance, vous permettant ainsi de réaliser des performances sans précédent.
##Ingrédients des gommes à mâcher Manhood Plus Male Enhancement et leur science
Les gommes Manhood Plus Male Enhancement contiennent un mélange de six ingrédients naturels et composés à base de plantes largement étudiés, réputés pour leur rôle historique dans le renforcement du désir et de la performance sexuelle.
Au-delà de leur impact sur la santé pelvienne masculine, ces ingrédients jouent un rôle multiforme dans la promotion du bien-être général. En favorisant une circulation sanguine saine, en soutenant la fonction immunitaire, en régulant la glycémie, en favorisant la perte de poids et en contribuant à la vitalité globale, ces composants offrent une approche globale de l'amélioration de la vitalité masculine.
**[Cliquez ici pour acheter maintenant sur le site officiel de Manhood Plus Gummies](https://slim-gummies-deutschland.de/manhood-plus-fr)**
More link
https://manhood-plud.clubeo.com/news/2024/06/26/manhood-plus-gummies-france-experiences-manhood-plus-commentair
Also Read
https://www.linkedin.com/pulse/quantum-attraction-reviews-code-audio-program-scam-exposed-manly-wkypf/
https://zenodo.org/records/11516365
|
neuralleap/Llama-2-7b-hf-8bit-unlabeled-finetuned-llama2-7b | neuralleap | 2024-06-27T09:27:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:27:39Z | Entry not found |
Likich/falcon-finetune-qualcoding-200 | Likich | 2024-06-27T09:33:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:33:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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] |
qsdreams/imagegen | qsdreams | 2024-06-27T09:35:03Z | 0 | 0 | null | [
"license:unknown",
"region:us"
]
| null | 2024-06-27T09:35:03Z | ---
license: unknown
---
|
pavan321b/HuggingFaceM4 | pavan321b | 2024-06-27T09:37:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:37:23Z | Entry not found |
TonyCh0pper/q-FrozenLake-v1-4x4-noSlippery | TonyCh0pper | 2024-06-27T09:41:41Z | 0 | 0 | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-27T09:41:38Z | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TonyCh0pper/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
JointFix/JointFix | JointFix | 2024-06-27T09:44:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T09:42:00Z | ---
license: apache-2.0
---
Ce este Joint Fix?
Joint Fix Pastile este o capsulă avansată pentru sănătatea articulațiilor, concepută pentru a susține și menține articulațiile sănătoase. Formulat cu un amestec de ingrediente naturale puternice și nutrienți esențiali, Joint Fix capsulă ajută la atenuarea durerilor articulare, la reducerea inflamației și la îmbunătățirea mobilității articulațiilor. Este un supliment ideal pentru persoanele care suferă de artrită, rigiditate articulară sau pentru cei care doresc să mențină sănătatea generală a articulațiilor pe măsură ce îmbătrânesc.
Site oficial:<a href="https://www.nutritionsee.com/Jointxomania">www.JointFix.com</a>
<p><a href="https://www.nutritionsee.com/Jointxomania"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/06/Joint-Fix-Romania.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/Jointxomania">Cumpără acum!! Faceți clic pe linkul de mai jos pentru mai multe informații și obțineți o reducere de 50% acum... Grăbește-te</a>
Site oficial:<a href="https://www.nutritionsee.com/Jointxomania">www.JointFix.com</a> |
shirleyah/Q36_continent | shirleyah | 2024-06-27T10:03:02Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
]
| null | 2024-06-27T09:42:32Z | ---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: Q36_continent
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. -->
# Q36_continent
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
TonyCh0pper/TaxiDriver | TonyCh0pper | 2024-06-27T09:46:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-27T09:46:38Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TaxiDriver
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TonyCh0pper/TaxiDriver", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Sayalik45/wav2vec2-large | Sayalik45 | 2024-06-27T09:48:18Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:48:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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] |
mud2002/MajorPakistaniUniversity-info-falcon-7b | mud2002 | 2024-06-27T12:26:39Z | 0 | 0 | transformers | [
"transformers",
"text-generation",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-27T09:49:51Z | ---
license: mit
pipeline_tag: text-generation
inference: true
language:
- en
library_name: transformers
--- |
Sushant0809/results_combined | Sushant0809 | 2024-06-29T12:00:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-06-27T09:50:41Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: results_combined
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. -->
# results_combined
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2039
- Treatment: 0.9157
- Chronic: 0.9092
- Cancer: 0.8980
- Allergy: 0.8158
- Other: 0.9494
- Weighted Avg: 0.9319
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Treatment | Chronic | Cancer | Allergy | Other | Weighted Avg |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:------:|:-------:|:------:|:------------:|
| 1.135 | 0.0995 | 100 | 0.6244 | 0.7117 | 0.6706 | 0.3658 | 0.0 | 0.8549 | 0.7592 |
| 0.4722 | 0.1990 | 200 | 0.3545 | 0.8356 | 0.8459 | 0.8056 | 0.0028 | 0.9165 | 0.8731 |
| 0.3368 | 0.2985 | 300 | 0.2911 | 0.8738 | 0.8737 | 0.8562 | 0.2790 | 0.9343 | 0.9012 |
| 0.2799 | 0.3980 | 400 | 0.2997 | 0.8723 | 0.8693 | 0.8567 | 0.7510 | 0.9370 | 0.9070 |
| 0.2941 | 0.4975 | 500 | 0.2878 | 0.8890 | 0.8689 | 0.8471 | 0.7100 | 0.9334 | 0.9069 |
| 0.2748 | 0.5970 | 600 | 0.2684 | 0.8851 | 0.8869 | 0.8659 | 0.7757 | 0.9380 | 0.9136 |
| 0.2466 | 0.6965 | 700 | 0.2525 | 0.9038 | 0.8782 | 0.8850 | 0.7934 | 0.9368 | 0.9164 |
| 0.2508 | 0.7960 | 800 | 0.2542 | 0.8955 | 0.8937 | 0.8714 | 0.6529 | 0.9409 | 0.9174 |
| 0.2392 | 0.8955 | 900 | 0.2189 | 0.9059 | 0.9008 | 0.8874 | 0.8157 | 0.9453 | 0.9257 |
| 0.237 | 0.9950 | 1000 | 0.2188 | 0.9014 | 0.8982 | 0.8807 | 0.8073 | 0.9459 | 0.9244 |
| 0.1843 | 1.0945 | 1100 | 0.2202 | 0.9077 | 0.9022 | 0.8825 | 0.8169 | 0.9465 | 0.9267 |
| 0.1766 | 1.1940 | 1200 | 0.2287 | 0.9148 | 0.9051 | 0.8759 | 0.8095 | 0.9468 | 0.9282 |
| 0.1667 | 1.2935 | 1300 | 0.2220 | 0.9091 | 0.9031 | 0.8878 | 0.7717 | 0.9483 | 0.9280 |
| 0.1868 | 1.3930 | 1400 | 0.2170 | 0.9140 | 0.9031 | 0.8932 | 0.8192 | 0.9473 | 0.9292 |
| 0.172 | 1.4925 | 1500 | 0.2151 | 0.9171 | 0.9070 | 0.8959 | 0.8113 | 0.9495 | 0.9317 |
| 0.1592 | 1.5920 | 1600 | 0.2082 | 0.9149 | 0.9060 | 0.8966 | 0.8097 | 0.9483 | 0.9305 |
| 0.1546 | 1.6915 | 1700 | 0.2039 | 0.9157 | 0.9092 | 0.8980 | 0.8158 | 0.9494 | 0.9319 |
| 0.1667 | 1.7910 | 1800 | 0.2041 | 0.9148 | 0.9100 | 0.9025 | 0.8128 | 0.9500 | 0.9325 |
| 0.1425 | 1.8905 | 1900 | 0.2093 | 0.9168 | 0.9099 | 0.8996 | 0.8125 | 0.9496 | 0.9325 |
| 0.1609 | 1.9900 | 2000 | 0.2059 | 0.9166 | 0.9110 | 0.8996 | 0.8125 | 0.9497 | 0.9327 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Likich/mistral-finetune-qualcoding-100 | Likich | 2024-06-27T09:51:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:51:47Z | ---
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
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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]
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## Technical Specifications [optional]
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NghiR/test | NghiR | 2024-06-27T09:53:19Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:53:19Z | Entry not found |
yll666/model_test | yll666 | 2024-06-27T09:54:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:54:06Z | Entry not found |
Susan774/parler-tts-mini_stc_v2 | Susan774 | 2024-06-27T09:54:22Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:54:22Z | Entry not found |
FilyaDude/gpt | FilyaDude | 2024-06-27T09:54:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T09:54:28Z | # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Xenova/gpt-4o") |
rajtest/lora_model | rajtest | 2024-06-27T09:55:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T09:55:47Z | ---
base_model: unsloth/tinyllama-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** rajtest
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stellawood/stellawood | stellawood | 2024-06-27T09:56:36Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-27T09:56:36Z | ---
license: mit
---
|
Boostaro155/Manhood5465 | Boostaro155 | 2024-06-27T10:01:46Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:00:04Z | # Manhood Plus Gummies Uk Reviews Price - Manhood Plus Experiences Dose & Works, Buy
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## **[Click Here To Buy Now From Official Website Of Manhood](https://adtocart.xyz/manhood-plus)** |
Abdrehman6224/my-model | Abdrehman6224 | 2024-06-27T10:02:18Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:02:18Z | Entry not found |
Likich/mistral-finetune-qualcoding-200 | Likich | 2024-06-27T11:08:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:04:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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TopperThijs/phi-3-testing | TopperThijs | 2024-06-27T15:31:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:06:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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## Environmental Impact
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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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Royalcreater/Shrijeet_Chatbot | Royalcreater | 2024-06-27T10:08:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:08:35Z | Entry not found |
ikocemayy13938/yeeunTIKTOK | ikocemayy13938 | 2024-06-27T10:15:27Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-27T10:14:12Z | ---
license: openrail
---
|
sanket1234/Project_embeddings | sanket1234 | 2024-06-27T10:16:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:14:20Z | Entry not found |
msomjacobs/finetuning-sentiment-model-3000-samples | msomjacobs | 2024-06-27T10:14:28Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:14:28Z | Entry not found |
michaelpiro1/DrumsDiff | michaelpiro1 | 2024-06-27T14:17:07Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"diffusers:AudioLDM2Pipeline",
"region:us"
]
| null | 2024-06-27T10:15:40Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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## Environmental Impact
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kelvink00/llama-7b-qlora-ultrachat | kelvink00 | 2024-06-27T10:37:20Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-06-27T10:15:55Z | Entry not found |
Likich/llama3-finetune-qualcoding-50 | Likich | 2024-06-27T10:16:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:16:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Ganidu/phi-3-mini-LoRA-finetuned-adapters | Ganidu | 2024-06-27T10:17:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:17:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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sayanbanerjee32/nanogpt2_test | sayanbanerjee32 | 2024-06-28T17:00:57Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-27T10:17:29Z | ---
license: mit
---
## Dataset
Collection of William Shakespeare plays
- tiktoken - gpt2 tokenizer is used for tokenization
- Number of total tokens - 338025
## The HuggingFace Spaces Gradio App
The app is available [here](https://huggingface.co/spaces/sayanbanerjee32/nanogpt2_text_generator)
The App takes following as input
1. Seed Text (Prompt) - This is provided as input text to the GPT model, based on which it generates further contents. If no data is provided, the only a space (" ") is provided as input
2. Max tokens to generate - This controls the numbers of tokens it will generate. The default value is 100.
3. Temperature - This accepts values between 0 to 1. Higher value introduces more randomness in the next token generation. Default value is set to 0.7.
4. Select Top N in each step - This is an optional field. If no value is provided (or <= 0), all available tokens are considered for the next token prediction based on SoftMax probability. However, if a number is set then only that many top tokes will be considered for the next token prediction. |
habulaj/335563514801 | habulaj | 2024-06-27T10:18:25Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:18:20Z | Entry not found |
Flamenco43/nano_cls-5 | Flamenco43 | 2024-06-27T10:18:31Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:18:31Z | Entry not found |
0qwpifs/SenyashimaV44 | 0qwpifs | 2024-06-27T13:05:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:19:08Z | Entry not found |
xhudec2/my_awesome_model | xhudec2 | 2024-06-27T10:20:47Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:20:47Z | Entry not found |
lizihua/dqn-SpaceInvadersNoFrameskip-v4 | lizihua | 2024-06-27T10:21:04Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:21:04Z | Entry not found |
Likich/llama3-finetune-qualcoding-20 | Likich | 2024-06-27T10:23:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:23:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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S0hamD/SAEFin | S0hamD | 2024-06-27T10:26:10Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:24:15Z | Entry not found |
hahuih/4343 | hahuih | 2024-06-27T10:44:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T10:25:27Z | ---
license: apache-2.0
---
|
Aa01100201/ilker | Aa01100201 | 2024-06-27T10:28:24Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:26:24Z | Entry not found |
habulaj/122518478279 | habulaj | 2024-06-27T10:27:21Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:27:09Z | Entry not found |
Likich/llama3-finetune-qualcoding-10 | Likich | 2024-06-27T10:28:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:27:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Edgar404/donut_lora_r8_shivi | Edgar404 | 2024-06-27T10:28:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:28:07Z | ---
library_name: transformers
tags: []
---
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Udith-Sandaruwan/udith | Udith-Sandaruwan | 2024-06-27T10:29:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:28:50Z | ---
library_name: transformers
tags:
- unsloth
---
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## More Information [optional]
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## Model Card Contact
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rayanebouta/partis_AF_4 | rayanebouta | 2024-06-27T10:31:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:31:23Z | Entry not found |
thesherrycode/gen-z-slangs-translate-llama-3-instruct-v1 | thesherrycode | 2024-06-27T11:39:48Z | 0 | 0 | null | [
"dataset:thesherrycode/gen-z-slangs-translation",
"region:us"
]
| null | 2024-06-27T10:34:12Z | ---
datasets:
- thesherrycode/gen-z-slangs-translation
--- |
Likich/llama3-finetune-qualcoding-5 | Likich | 2024-06-27T10:37:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:36:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**BibTeX:**
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## Model Card Contact
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codernothacker/instruct-llama2-7b-alpaca-gpt | codernothacker | 2024-06-27T10:38:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-27T10:38:11Z | ---
license: apache-2.0
---
|
Litzy619/MIS0626T5F200200 | Litzy619 | 2024-06-27T20:47:57Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:40:06Z | Entry not found |
hellohim/llama3_base_unsloth_lora_epoch1 | hellohim | 2024-06-27T10:42:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:42:24Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** hellohim
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Avani09/Instruction_fine_tuning_ACM | Avani09 | 2024-06-27T10:43:53Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-27T10:43:53Z | Entry not found |
Likich/falcon-finetune-qualcoding-50 | Likich | 2024-06-27T10:48:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-27T10:44:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
rajparmar/mistral-7B-v0.1-bf16-sharded-finetuned-tpicap-emails | rajparmar | 2024-06-27T10:46:40Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:ybelkada/Mistral-7B-v0.1-bf16-sharded",
"region:us"
]
| null | 2024-06-27T10:46:22Z | ---
base_model: ybelkada/Mistral-7B-v0.1-bf16-sharded
tags:
- generated_from_trainer
model-index:
- name: mistral-7B-v0.1-bf16-sharded-finetuned-tpicap-emails
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7B-v0.1-bf16-sharded-finetuned-tpicap-emails
This model is a fine-tuned version of [ybelkada/Mistral-7B-v0.1-bf16-sharded](https://huggingface.co/ybelkada/Mistral-7B-v0.1-bf16-sharded) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 200
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 2.3.0+cu121
- Datasets 2.13.0
- Tokenizers 0.14.1
|
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