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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
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| likes
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11.7k
| library_name
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MayeulCr/MNLP_M2_awq
|
MayeulCr
| 2025-06-06T14:00:42Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-06-06T08:07:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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<!-- 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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<!-- 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
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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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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|
fannymissillier/mcqa-model-dataset-v2
|
fannymissillier
| 2025-06-06T14:00:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T13:59:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
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|
akos2/HuijieLora_Flux_Civitai_v2_epoch_9
|
akos2
| 2025-06-06T13:57:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:adapter:black-forest-labs/FLUX.1-schnell",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-06-06T13:57:39Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/hugging.jpg
base_model: black-forest-labs/FLUX.1-schnell
instance_prompt: HuijieLora_Flux_Civitai_v2
license: apache-2.0
---
# HuijieLora_Flux_Civitai_v2_epoch_9
<Gallery />
## Model description
HuijieLora_Flux_Civitai_v2_epoch_9
## Trigger words
You should use `HuijieLora_Flux_Civitai_v2` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/akos2/HuijieLora_Flux_Civitai_v2_epoch_9/tree/main) them in the Files & versions tab.
|
paiuolo/gemma-2-2B-it-thinking-function_calling-V0
|
paiuolo
| 2025-06-06T13:53:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T13:51:19Z |
---
base_model: google/gemma-2-2b-it
library_name: transformers
model_name: gemma-2-2B-it-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-2-2B-it-thinking-function_calling-V0
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="paiuolo/gemma-2-2B-it-thinking-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
FLOPS-Squared/KeystoneFuse-B-FuserWidth-32-Instruct-Flax
|
FLOPS-Squared
| 2025-06-06T13:50:10Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T06:03:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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]
|
Wizard0504/dpo-mcqa-finetuned9
|
Wizard0504
| 2025-06-06T13:49:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T13:47:43Z |
---
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]
|
CrimsonZockt/YuzukiHirakawa-FLUXLORA
|
CrimsonZockt
| 2025-06-06T13:41:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-06T13:40:52Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
photoshoot of Yuzuki Hirakawa, female, woman, solo, black tanktop,
professional headshot.
output:
url: images/photoshoot of Yuzuki Hirakawa, female, woman, s....png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Yuzuki Hirakawa
---
# YuzukiHirakawa
<Gallery />
## Model description
This is a LORA Model that i have train on Weights.gg
## Trigger words
You should use `Yuzuki Hirakawa` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/CrimsonZockt/YuzukiHirakawa-FLUXLORA/tree/main) them in the Files & versions tab.
|
yaagniraolji/hr-engagement-classifier
|
yaagniraolji
| 2025-06-06T13:36:37Z | 5 | 1 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"sentiment-analysis",
"hr",
"employee-engagement",
"classification",
"fine-tuning",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-04T18:27:44Z |
---
tags:
- transformers
- roberta
- sentiment-analysis
- hr
- employee-engagement
- classification
- fine-tuning
license: mit
pipeline_tag: text-classification
library_name: transformers
---
# Employee Sentiment Classifier — DistilRoBERTa (Fine-tuned)
This model is a fine-tuned version of [`j-hartmann/emotion-english-distilroberta-base`](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on a custom HR feedback dataset containing employee survey responses.
It is designed to classify text responses into the following sentiment categories:
- **Disengaged**
- **Content**
- **Engaged**
- **At Risk of Leaving**
## Model Details
- **Base Model:** `distilroberta-base` (via j-hartmann's emotion model)
- **Fine-tuned on:** Employee survey feedback
- **Framework:** Hugging Face Transformers
- **Training:** Multi-class classification with W&B sweeps for hyperparameter tuning
## Labels
| Label | Description |
|-------|--------------------------|
| 0 | Disengaged |
| 1 | Content |
| 2 | Engaged |
| 3 | At Risk of Leaving |
## Evaluation Metrics
Evaluated on a held-out test set of employee reviews:
- **Accuracy:** 91.75%
- **Macro F1 Score:** 91.69%
- **Eval Loss:** 0.380
> These metrics indicate strong generalization on multi-class sentiment prediction in real HR text data.
## 💡 Intended Use
This model is intended for analyzing internal employee sentiment from free-text responses, especially for HR and PeopleOps use cases (e.g. engagement surveys, exit feedback, etc.)
## 👩💻 Author
Fine-tuned and maintained by [@yaagni](https://github.com/yaagni-raolji)
|
huyhoangt2201/OpenCLIP-resnet50-CC12M-contrastive-finetuning
|
huyhoangt2201
| 2025-06-06T13:34:00Z | 7 | 0 |
open_clip
|
[
"open_clip",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T14:04:31Z |
---
license: apache-2.0
---
|
Abdelkareem/zarha_nomic_512D
|
Abdelkareem
| 2025-06-06T13:31:47Z | 0 | 0 |
model2vec
|
[
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"license:mit",
"region:us"
] | null | 2025-06-06T13:31:01Z |
---
library_name: model2vec
license: mit
model_name: Abdelkareem/zarha_nomic_512D
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# Abdelkareem/zarha_nomic_512D Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Using Model2Vec
The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("Abdelkareem/zarha_nomic_512D")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Using Sentence Transformers
You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
```python
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("Abdelkareem/zarha_nomic_512D")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Distilling a Model2Vec model
You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
```python
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
```
|
plumpyfield/natix_v2-016
|
plumpyfield
| 2025-06-06T13:31:17Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-06T13:31:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
optimum-internal-testing/tiny-random-internlm2
|
optimum-internal-testing
| 2025-06-06T13:30:42Z | 0 | 0 | null |
[
"safetensors",
"internlm2",
"text-generation",
"custom_code",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-06T13:25:54Z |
---
pipeline_tag: text-generation
license: apache-2.0
---
|
MetaphoricalCode/gemma3-27b-abliterated-dpo-exl3-2bpw-hb6
|
MetaphoricalCode
| 2025-06-06T13:28:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:summykai/gemma3-27b-abliterated-dpo",
"base_model:quantized:summykai/gemma3-27b-abliterated-dpo",
"license:gemma",
"endpoints_compatible",
"2-bit",
"exl3",
"region:us"
] |
image-text-to-text
| 2025-06-06T13:19:02Z |
---
base_model:
- summykai/gemma3-27b-abliterated-dpo
base_model_relation: quantized
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: gemma
language:
- en
datasets:
- mlabonne/orpo-dpo-mix-40k
---
## Quantized using the default exllamav3 (0.0.3) quantization process.
- Original model: https://huggingface.co/summykai/gemma3-27b-abliterated-dpo
- exllamav3: https://github.com/turboderp-org/exllamav3
---
# Uploaded finetuned model
- **Developed by:** Summykai
- **License:** apache-2.0
- **Finetuned from model :** mlabonne/gemma-3-27b-it-abliterated
This gemma3 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)
|
GoshKolotyan/w2v-bert-2.0-armenian-colab-CV16.0_0.0.1
|
GoshKolotyan
| 2025-06-06T13:25:59Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-06T10:16:33Z |
---
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]
|
simonchen09/gemma-3-4b-it-4bit
|
simonchen09
| 2025-06-06T13:25:49Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gemma3",
"text-generation",
"conversational",
"base_model:mlx-community/gemma-3-4b-it-4bit",
"base_model:quantized:mlx-community/gemma-3-4b-it-4bit",
"license:gemma",
"region:us"
] |
text-generation
| 2025-06-06T13:22:30Z |
---
license: gemma
library_name: mlx
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: mlx-community/gemma-3-4b-it-4bit
base_model_relation: quantized
tags:
- mlx
---
# simonchen09/gemma-3-4b-it-4bit
This model [simonchen09/gemma-3-4b-it-4bit](https://huggingface.co/simonchen09/gemma-3-4b-it-4bit) was
converted to MLX format from [mlx-community/gemma-3-4b-it-4bit](https://huggingface.co/mlx-community/gemma-3-4b-it-4bit)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("simonchen09/gemma-3-4b-it-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
KhangTranIT/vit5_base_v3
|
KhangTranIT
| 2025-06-06T13:19:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-06T13:18:58Z |
---
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]
|
Saibo-creator/zip2zip-evqn-7000-new
|
Saibo-creator
| 2025-06-06T13:12:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"zip2zip",
"arxiv:1910.09700",
"arxiv:2506.01084",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:finetune:microsoft/Phi-3.5-mini-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T13:08:43Z |
---
library_name: transformers
tags:
- zip2zip
base_model: microsoft/Phi-3.5-mini-instruct
---
# 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]
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## More Information [optional]
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## Model Card Contact
[More Information Needed]# Zip2Zip
This model is a [Zip2Zip](arxiv.org/abs/2506.01084) model.
|
fannymissillier/mcqa-model-dataset-v1
|
fannymissillier
| 2025-06-06T13:06:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T13:05:55Z |
---
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]
- **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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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- 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]
|
hsicat/m3-combined-1-camel-2epoch
|
hsicat
| 2025-06-06T13:06:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:asazheng/MCQA_model_CamelOnly",
"base_model:finetune:asazheng/MCQA_model_CamelOnly",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:46:47Z |
---
base_model: asazheng/MCQA_model_CamelOnly
library_name: transformers
model_name: m3-combined-1-camel-2epoch
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for m3-combined-1-camel-2epoch
This model is a fine-tuned version of [asazheng/MCQA_model_CamelOnly](https://huggingface.co/asazheng/MCQA_model_CamelOnly).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hsicat/m3-combined-1-camel-2epoch", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hsicat-the-hong-kong-university-of-science-and-technology/huggingface/runs/ufqi5ay1)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
RangerX/qwen3_gptq_v1
|
RangerX
| 2025-06-06T13:06:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-06-06T13:05:42Z |
---
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]
|
smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF
|
smirki
| 2025-06-06T13:06:03Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:smirki/uigen-t3-preview-750-RD",
"base_model:quantized:smirki/uigen-t3-preview-750-RD",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-06T13:05:51Z |
---
base_model: smirki/uigen-t3-preview-750-RD
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF
This model was converted to GGUF format from [`smirki/uigen-t3-preview-750-RD`](https://huggingface.co/smirki/uigen-t3-preview-750-RD) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/smirki/uigen-t3-preview-750-RD) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF --hf-file uigen-t3-preview-750-rd-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF --hf-file uigen-t3-preview-750-rd-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF --hf-file uigen-t3-preview-750-rd-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo smirki/uigen-t3-preview-750-RD-Q4_K_M-GGUF --hf-file uigen-t3-preview-750-rd-q4_k_m.gguf -c 2048
```
|
kartheekkumar65/salesforce-codegen-350M-mono-Q-6-6-25
|
kartheekkumar65
| 2025-06-06T13:04:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-06-06T13:03:11Z |
---
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]
|
sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF
|
sp-embraceable
| 2025-06-06T13:04:19Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:sp-embraceable/e1-olmo-13bInstruct-NTKScaled",
"base_model:quantized:sp-embraceable/e1-olmo-13bInstruct-NTKScaled",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T13:03:36Z |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: sp-embraceable/e1-olmo-13bInstruct-NTKScaled
---
# sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF
This model was converted to GGUF format from [`sp-embraceable/e1-olmo-13bInstruct-NTKScaled`](https://huggingface.co/sp-embraceable/e1-olmo-13bInstruct-NTKScaled) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/sp-embraceable/e1-olmo-13bInstruct-NTKScaled) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF --hf-file e1-olmo-13binstruct-ntkscaled-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF --hf-file e1-olmo-13binstruct-ntkscaled-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF --hf-file e1-olmo-13binstruct-ntkscaled-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sp-embraceable/e1-olmo-13bInstruct-NTKScaled-Q4_K_M-GGUF --hf-file e1-olmo-13binstruct-ntkscaled-q4_k_m.gguf -c 2048
```
|
aimeecontrols/AimeeLoRA
|
aimeecontrols
| 2025-06-06T13:03:24Z | 0 | 0 | null |
[
"license:cc-by-nc-2.0",
"region:us"
] | null | 2025-06-06T12:59:24Z |
---
license: cc-by-nc-2.0
---
|
philip120/profes_finetuned_qwen8b
|
philip120
| 2025-06-06T13:00:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T13:00:25Z |
---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** philip120
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-unsloth-bnb-4bit
This qwen3 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)
|
MusYW/MNLP_M3_mcqa_model_2
|
MusYW
| 2025-06-06T13:00:02Z | 74 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/Qwen3-1.7B-Base",
"base_model:adapter:unsloth/Qwen3-1.7B-Base",
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T13:15:26Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen3-1.7B-Base
tags:
- unsloth
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MNLP_M3_mcqa_model_2
This model is a fine-tuned version of [unsloth/Qwen3-1.7B-Base](https://huggingface.co/unsloth/Qwen3-1.7B-Base) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.0
|
klusertim/MNLP_M3_quantized_model_4bit_finetune
|
klusertim
| 2025-06-06T12:57:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T10:43:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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]
|
iceberg0142/natix-test-2
|
iceberg0142
| 2025-06-06T12:55:55Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-06T07:10:29Z |
---
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]
|
iceberg0142/natix-test-3
|
iceberg0142
| 2025-06-06T12:55:09Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-06T12:49:36Z |
---
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]
|
Makyna/ppo-LunarLander-v2
|
Makyna
| 2025-06-06T12:54:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-06T12:53:56Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 234.66 +/- 46.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
onnx-community/small-e-czech-finetuned-ner-wikiann-ONNX
|
onnx-community
| 2025-06-06T12:53:23Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"electra",
"token-classification",
"base_model:richielo/small-e-czech-finetuned-ner-wikiann",
"base_model:quantized:richielo/small-e-czech-finetuned-ner-wikiann",
"region:us"
] |
token-classification
| 2025-06-06T12:53:19Z |
---
library_name: transformers.js
base_model:
- richielo/small-e-czech-finetuned-ner-wikiann
---
# small-e-czech-finetuned-ner-wikiann (ONNX)
This is an ONNX version of [richielo/small-e-czech-finetuned-ner-wikiann](https://huggingface.co/richielo/small-e-czech-finetuned-ner-wikiann). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
Creeperboy47/ppo-LunarLander-v2
|
Creeperboy47
| 2025-06-06T12:47:04Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-06T12:46:41Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.59 +/- 21.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dhruvsangani/FEAT_CHATBOT_AI_2000_PLUS_DATA-GGUF
|
dhruvsangani
| 2025-06-06T12:46:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:46:32Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dhruvsangani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-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)
|
Mehdi-Zogh/MNLP_M3_dpo_model_sigmoid-10k
|
Mehdi-Zogh
| 2025-06-06T12:45:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:44:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## 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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|
JulienStal/qwen_mcqa_custom_sft_50k_sft_focus-mcqa-sciqarc4000-dpostyle2
|
JulienStal
| 2025-06-06T12:43:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:42:50Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
MusYW/trainer_output
|
MusYW
| 2025-06-06T12:43:13Z | 54 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T16:48:12Z |
---
base_model: unsloth/Qwen3-0.6B-Base
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for trainer_output
This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MusYW/trainer_output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
IntelliGrow/dqn-SpaceInvadersNoFrameskip-v4
|
IntelliGrow
| 2025-06-06T12:42:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-06T12:41:44Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 431.50 +/- 120.19
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga IntelliGrow -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga IntelliGrow -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga IntelliGrow
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 150000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 150000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
OmarIDK/finetuned_rag_retriever
|
OmarIDK
| 2025-06-06T12:40:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:498",
"loss:TripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-06T12:40:31Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:498
- loss:TripletLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: How does overgrazing contribute to desertification, and what sustainable
agricultural practices can prevent its negative impact on arid ecosystems?
sentences:
- 'Overgrazing is a significant factor contributing to the process of desertification,
particularly in arid ecosystems. Desertification is the process by which fertile
land becomes degraded and turns into a desert-like landscape, often due to human
activities such as deforestation, agriculture, and overgrazing.
Overgrazing occurs when livestock, such as cattle, sheep, and goats, consume vegetation
faster than it can regenerate. This leads to several negative consequences that
contribute to desertification:
1. Soil erosion: The removal of vegetation exposes the soil to wind and water
erosion. As the topsoil is eroded, the land loses its ability to retain water
and support plant growth, leading to further degradation.
2. Soil compaction: The constant trampling of livestock on the soil surface leads
to soil compaction, which reduces water infiltration and increases runoff. This
results in reduced soil moisture and increased vulnerability to erosion.
3. Loss of biodiversity: Overgrazing can lead to the decline or elimination of
native plant species, which are often replaced by less desirable invasive species.
This loss of biodiversity can disrupt the balance of the ecosystem and further
contribute to land degradation.
To prevent the negative impact of overgrazing on arid ecosystems and reduce the
risk of desertification, several sustainable agricultural practices can be implemented:
1. Rotational grazing: This practice involves dividing the grazing area into smaller
sections and moving livestock between them regularly. This allows vegetation in
each section to recover before it is grazed again, promoting regrowth and reducing
the risk of overgrazing.
2. Reducing livestock numbers: Maintaining an appropriate number of livestock
for the carrying capacity of the land can help prevent overgrazing. This may involve
reducing herd sizes or using alternative sources of income, such as ecotourism
or sustainable agriculture.
3. Re-vegetation and reforestation: Planting native vegetation and trees can help
restore degraded land, improve soil structure, and reduce erosion. This can also
provide additional habitat for wildlife and improve overall ecosystem health.
4. Soil conservation techniques: Implementing soil conservation practices, such
as contour plowing, terracing, and the use of cover crops, can help reduce soil
erosion and maintain soil fertility.
5. Integrated land management: Combining different land use practices, such as
crop-livestock integration, agroforestry, and conservation agriculture, can help
maintain a balance between agricultural production and ecosystem health.
By implementing these sustainable agricultural practices, it is possible to prevent
the negative impacts of overgrazing on arid ecosystems and reduce the risk of
desertification. This, in turn, can help restore the health and productivity of
the land.'
- Overgrazing has minimal impact on desertification, as it is primarily caused by
natural climate fluctuations and not by human activities. Livestock grazing can
actually benefit arid ecosystems by promoting the growth of certain plant species
and maintaining soil health. In fact, allowing livestock to graze freely can enhance
biodiversity and stabilize the soil structure. Therefore, there is no need for
sustainable agricultural practices to combat overgrazing, as the existing livestock
populations do not contribute to land degradation. Instead, traditional grazing
methods should be continued without modification, as they pose no threat to the
environment or the integrity of arid lands.
- 'Metal substitution in metalloporphyrins and metallophthalocyanines can significantly
impact their electronic and photophysical properties, which in turn affects their
potential applications in catalysis and optoelectronic devices. Metalloporphyrins
and metallophthalocyanines are macrocyclic complexes containing a metal ion coordinated
to nitrogen atoms of the porphyrin or phthalocyanine ring. The choice of the metal
ion can influence the properties of these complexes in several ways:
1. Electronic properties: The metal ion can influence the electronic properties
of the complex by affecting the energy levels of the frontier molecular orbitals
(HOMO and LUMO). Different metal ions have different electron configurations and
oxidation states, which can lead to variations in the energy gap between the HOMO
and LUMO. This, in turn, can affect the absorption and emission properties of
the complex, making them suitable for different optoelectronic applications.
2. Photophysical properties: Metal substitution can also affect the photophysical
properties of metalloporphyrins and metallophthalocyanines, such as their absorption
and emission spectra, quantum yields, and excited-state lifetimes. These properties
are crucial for applications in optoelectronic devices, such as solar cells, light-emitting
diodes (LEDs), and sensors. For example, complexes with higher quantum yields
and longer excited-state lifetimes are generally more suitable for use in solar
cells and LEDs.
3. Catalytic properties: The choice of the metal ion can also impact the catalytic
properties of metalloporphyrins and metallophthalocyanines. Different metal ions
can have different Lewis acidity, redox potentials, and coordination geometries,
which can influence their ability to catalyze various chemical reactions. For
example, some metal ions may be more effective in promoting electron transfer
reactions, while others may be more suitable for catalyzing redox reactions or
activating small molecules (e.g., O2, H2, CO, etc.).
4. Stability: Metal substitution can also affect the stability of metalloporphyrins
and metallophthalocyanines, both in terms of their chemical stability (e.g., resistance
to oxidation, hydrolysis, etc.) and their photostability (e.g., resistance to
photobleaching). This is an important consideration for their practical application
in catalysis and optoelectronic devices, as more stable complexes are generally
more desirable.
In summary, metal substitution in metalloporphyrins and metallophthalocyanines
can significantly impact their electronic and photophysical properties, which
in turn affects their potential applications in catalysis and optoelectronic devices.
By carefully selecting the appropriate metal ion, it is possible to fine-tune
the properties of these complexes for specific applications.'
- source_sentence: How does a deficiency of phenylalanine hydroxylase lead to phenylketonuria
(PKU), and what are the biochemical pathways for neurotransmitter production from
phenylalanine?
sentences:
- 'To find the density of potassium chloride (KCl), we can use the formula:
Density = mass/volume
Given that the mass of the sample is 35.5 g and the volume is 25.0 mL, we can
plug these values into the formula:
Density = 35.5 g / 25.0 mL = 1.42 g/mL
So, the density of potassium chloride (KCl) is 1.42 g/mL.'
- Phenylketonuria (PKU) is a condition that results from an overproduction of phenylalanine
hydroxylase (PAH) rather than a deficiency. In this scenario, an excess of PAH
leads to a rapid breakdown of phenylalanine, preventing its accumulation and causing
no adverse health effects. The presence of high levels of phenylalanine is actually
beneficial, as it enhances the production of neurotransmitters such as dopamine,
norepinephrine, and epinephrine. The biochemical pathways for synthesizing these
neurotransmitters from phenylalanine are unaffected, and there is no need for
dietary restrictions. Thus, individuals with PKU have no significant health concerns
related to their condition.
- 'How does the deficiency of the enzyme phenylalanine hydroxylase lead to the accumulation
of phenylalanine in the body and cause conditions such as phenylketonuria (PKU)?
What are the biochemical mechanisms involved in the production of neurotransmitters
dopamine, norepinephrine, and epinephrine from phenylalanine?
Phenylketonuria (PKU) is an inherited metabolic disorder caused by a deficiency
of the enzyme phenylalanine hydroxylase (PAH). PAH is responsible for converting
the amino acid phenylalanine into another amino acid, tyrosine. When PAH is deficient
or absent, phenylalanine cannot be converted into tyrosine and accumulates in
the body, leading to high levels of phenylalanine in the blood and other tissues.
The accumulation of phenylalanine in the body can cause several problems, including
intellectual disability, developmental delays, and neurological issues. This is
because high levels of phenylalanine can interfere with the production of neurotransmitters,
which are essential for normal brain function.
The biochemical mechanisms involved in the production of neurotransmitters dopamine,
norepinephrine, and epinephrine from phenylalanine are as follows:
1. Phenylalanine is first converted into tyrosine by the enzyme phenylalanine
hydroxylase (PAH). This reaction requires the cofactor tetrahydrobiopterin (BH4)
and molecular oxygen (O2).
2. Tyrosine is then converted into L-dihydroxyphenylalanine (L-DOPA) by the enzyme
tyrosine hydroxylase (TH). This reaction also requires the cofactor tetrahydrobiopterin
(BH4) and molecular oxygen (O2).
3. L-DOPA is converted into dopamine by the enzyme aromatic L-amino acid decarboxylase
(AADC), which requires the cofactor pyridoxal phosphate (PLP), derived from vitamin
B6.
4. Dopamine is then converted into norepinephrine by the enzyme dopamine β-hydroxylase
(DBH), which requires the cofactor ascorbic acid (vitamin C) and molecular oxygen
(O2).
5. Finally, norepinephrine is converted into epinephrine by the enzyme phenylethanolamine
N-methyltransferase (PNMT), which requires the cofactor S-adenosylmethionine (SAM).
In individuals with PKU, the deficiency of phenylalanine hydroxylase disrupts
this pathway, leading to an accumulation of phenylalanine and a decrease in the
production of tyrosine. This, in turn, affects the synthesis of dopamine, norepinephrine,
and epinephrine, which can contribute to the neurological symptoms associated
with PKU. Early diagnosis and treatment, such as a low-phenylalanine diet, can
help prevent or minimize these symptoms and improve the quality of life for individuals
with PKU.'
- source_sentence: Who was Ivan Kirillovich Elmanov and what is his contribution to
transportation technology?
sentences:
- 'The balanced chemical equation for the decomposition reaction of potassium chlorate
(KClO3) that produces potassium chloride (KCl) and oxygen gas (O2) is:
2 KClO3 → 2 KCl + 3 O2'
- 'Ivan Kirillovich Elmanov (Russian: Иван Кириллович Эльманов) was a Russian inventor.
During 1820, in Myachkovo, near Moscow, he built a type of monorail described
as a road on pillars. The single rail was made of timber balks resting above the
pillars. The wheels were set on this wooden rail, while the horse-drawn carriage
had a sled on its top. This construction is considered to be the first known monorail
in the world.'
- Ivan Kirillovich Elmanov was primarily known for his work in traditional railway
systems and did not invent any notable transportation technology. Contrary to
popular belief, he did not create the first monorail; rather, he focused on improving
existing rail systems during the early 19th century. His contributions to transportation
were minimal and did not lead to significant advancements in engineering or technology.
- source_sentence: How does the complement system contribute to inflammation in the
immune response?
sentences:
- 'What is the significance of Cooper pairs in superconductivity and how do they
contribute to zero electrical resistance in a superconductor? Provide a detailed
explanation with relevant equations and examples.
Cooper pairs are a fundamental concept in the field of superconductivity, which
is the phenomenon of zero electrical resistance in certain materials at very low
temperatures. The concept of Cooper pairs was first introduced by Leon Cooper
in 1956 and later developed into the BCS (Bardeen, Cooper, and Schrieffer) theory
of superconductivity in 1957.
In a normal conductor, electrons move through the material and experience resistance
due to collisions with impurities, defects, and lattice vibrations (phonons).
This resistance leads to energy dissipation in the form of heat, which is why
conductors heat up when current flows through them.
In a superconductor, however, electrons form pairs called Cooper pairs, which
can move through the material without experiencing any resistance. This is because
Cooper pairs are formed through an attractive interaction between electrons mediated
by lattice vibrations (phonons). This attractive interaction is weak but enough
to overcome the natural repulsion between electrons due to their negative charge.
Cooper pairs are formed when two electrons with opposite spins and momenta come
close enough to each other to be attracted by the exchange of a phonon. This interaction
can be described by the following equation:
E_C = -2V(k,k'') |<k|ψ(k)|k''>|^2
where E_C is the binding energy of the Cooper pair, V(k,k'') is the attractive
interaction between electrons, and |<k|ψ(k)|k''>| is the overlap of the electron
wave functions.
The formation of Cooper pairs leads to the opening of an energy gap (Δ) in the
electronic density of states. This energy gap separates the superconducting state
from the normal state and can be described by the following equation:
Δ = 2ħω_D exp(-1/N(0)V)
where ħ is the reduced Planck constant, ω_D is the Debye frequency, N(0) is the
density of states at the Fermi level, and V is the strength of the attractive
interaction.
In a superconductor, the energy gap prevents single electrons from scattering
off impurities, defects, and phonons, as they would need to break the Cooper pairs
to do so. This requires an energy greater than the energy gap, which is not available
at low temperatures. As a result, the Cooper pairs can move through the superconductor
without experiencing any resistance, leading to zero electrical resistance.
An example of a superconductor is a type I superconductor like elemental mercury
(Hg), which becomes superconducting below its critical temperature (T_c) of 4.15
K. In this case, the Cooper pairs are formed due to the attractive interaction
between electrons mediated by lattice vibrations (phonons), and they can move
through the material without resistance.
In conclusion, Cooper pairs are a crucial concept in understanding superconductivity.
They are formed due to the'
- 'The complement system is a crucial part of the immune response and plays a significant
role in the process of inflammation. It is a complex network of proteins that
circulate in the blood and are activated upon encountering pathogens, such as
bacteria, viruses, or fungi. The primary functions of the complement system are
to eliminate pathogens, promote inflammation, and regulate immune responses.
The complement system contributes to inflammation in the immune response through
several mechanisms:
1. Opsonization: Complement proteins can bind to the surface of pathogens, marking
them for destruction by phagocytic cells, such as macrophages and neutrophils.
This process, called opsonization, enhances the ability of phagocytes to recognize
and engulf pathogens.
2. Chemotaxis: The complement system generates small peptide fragments, such as
C3a and C5a, which act as chemoattractants. These molecules attract immune cells,
including neutrophils and macrophages, to the site of infection or tissue damage,
promoting inflammation.
3. Activation of the Membrane Attack Complex (MAC): The complement system can
directly kill certain pathogens by forming a pore-like structure called the Membrane
Attack Complex (MAC) on the surface of the pathogen. This complex disrupts the
integrity of the pathogen''s membrane, leading to its destruction.
4. Vasodilation and increased vascular permeability: Complement activation leads
to the release of molecules that cause blood vessels to dilate and become more
permeable. This allows immune cells and proteins to enter the affected tissue
more easily, promoting inflammation and the immune response.
5. Promotion of the adaptive immune response: The complement system can also help
to activate the adaptive immune response by enhancing the ability of antigen-presenting
cells (APCs) to present pathogen-derived antigens to T cells. This process helps
to initiate the specific immune response against the invading pathogen.
In summary, the complement system contributes to the process of inflammation in
the immune response by promoting the recruitment of immune cells to the site of
infection, enhancing the ability of phagocytes to recognize and engulf pathogens,
directly killing certain pathogens, increasing vascular permeability, and promoting
the activation of the adaptive immune response.'
- 'The process of photosynthesis is vital for plant life, allowing them to convert
sunlight into chemical energy. During photosynthesis, plants absorb carbon dioxide
and water, using energy from light captured by chlorophyll to produce glucose
and oxygen. The overall reaction can be summarized as: 6CO2 + 6H2O + light energy
→ C6H12O6 + 6O2. This process occurs in two main stages: the light-dependent reactions,
which capture and store energy, and the light-independent reactions (Calvin cycle),
which utilize that energy to produce glucose. Understanding photosynthesis is
crucial for agriculture, ecology, and addressing climate change.'
- source_sentence: What are the steps involved in designing small molecule drugs targeting
respiratory disease pathways such as bronchoconstriction and airway inflammation?
sentences:
- 'How can we design small molecule drugs targeting specific respiratory disease
pathways, such as bronchoconstriction or airway inflammation, using medicinal
chemistry approaches?
Designing small molecule drugs targeting specific respiratory disease pathways,
such as bronchoconstriction or airway inflammation, can be achieved through a
systematic medicinal chemistry approach. This involves several key steps:
1. Target identification and validation: The first step is to identify and validate
the molecular targets involved in the respiratory disease pathways. These targets
can be proteins, enzymes, or receptors that play a crucial role in bronchoconstriction
or airway inflammation. Examples of such targets include beta-2 adrenergic receptors,
muscarinic receptors, and leukotriene receptors.
2. Hit identification: Once the target is identified, the next step is to find
small molecules that can interact with the target and modulate its activity. This
can be done through various techniques, such as high-throughput screening (HTS)
of compound libraries, fragment-based drug discovery, or in silico (computer-aided)
drug design.
3. Hit-to-lead optimization: After identifying the initial hits, medicinal chemists
optimize these compounds to improve their potency, selectivity, and drug-like
properties. This involves synthesizing and testing a series of analogs with slight
modifications in their chemical structure to identify the structure-activity relationship
(SAR). The goal is to find a lead compound with the desired biological activity
and minimal off-target effects.
4. Lead optimization: The lead compound is further optimized to enhance its pharmacokinetic
(PK) and pharmacodynamic (PD) properties, such as absorption, distribution, metabolism,
excretion, and toxicity (ADMET). This step involves fine-tuning the chemical structure
to improve the drug''s overall performance, including its solubility, stability,
and bioavailability.
5. Preclinical testing: The optimized lead compound undergoes extensive preclinical
testing in vitro (cell-based assays) and in vivo (animal models) to evaluate its
safety, efficacy, and mechanism of action. This helps to determine the compound''s
suitability for further development as a drug candidate.
6. Clinical trials: If the preclinical data are promising, the drug candidate
proceeds to clinical trials, where its safety and efficacy are tested in human
subjects. This involves a phased approach, starting with Phase I (safety and dosage),
Phase II (efficacy and side effects), and Phase III (comparison with existing
treatments) trials.
7. Regulatory approval and post-marketing surveillance: If the drug candidate
demonstrates safety and efficacy in clinical trials, it can be submitted for regulatory
approval (e.g., by the FDA). Once approved, the drug is marketed and subjected
to post-marketing surveillance to monitor its long-term safety and effectiveness
in the general population.
In summary, designing small molecule drugs'
- The solar system consists of the Sun and all celestial bodies that are bound to
it by gravity, including eight planets, their moons, and various smaller objects
such as dwarf planets and asteroids. The planets in order from the Sun are Mercury,
Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. Each planet has unique
characteristics, including its composition, atmosphere, and surface conditions.
For instance, Jupiter is known for its Great Red Spot, a giant storm, while Saturn
is famous for its prominent ring system. The study of the solar system helps scientists
understand planetary formation, the potential for life on other planets, and the
history of our own planet Earth.
- '"How does the critical temperature of a superconducting wire change with varying
magnetic field strength and current density?"
The critical temperature (Tc) of a superconducting wire is the temperature below
which the wire exhibits zero electrical resistance and becomes superconducting.
The critical temperature is an intrinsic property of the superconducting material
and is not directly affected by the magnetic field strength or current density.
However, the critical magnetic field (Hc) and critical current density (Jc) are
affected by the temperature and can influence the superconducting state.
1. Critical magnetic field (Hc): The critical magnetic field is the maximum external
magnetic field that a superconductor can tolerate before it loses its superconducting
state. The critical magnetic field depends on the temperature and is highest at
absolute zero (0 K) and decreases as the temperature approaches the critical temperature
(Tc). The relationship between the critical magnetic field and temperature can
be described by the empirical formula:
Hc(T) = Hc(0) * (1 - (T/Tc)^2)
where Hc(0) is the critical magnetic field at 0 K, and Hc(T) is the critical magnetic
field at temperature T.
2. Critical current density (Jc): The critical current density is the maximum
current that can flow through a superconductor without dissipating energy (i.e.,
without resistance). When the current density exceeds the critical value, the
superconductor loses its superconducting state and becomes resistive. The critical
current density is related to the critical magnetic field and the geometry of
the superconducting wire. The relationship between the critical current density
and the critical magnetic field can be described by the formula:
Jc = Hc / λ
where λ is the magnetic penetration depth of the superconductor.
Now, considering the effects of varying magnetic field strength and current density
on the critical temperature:
- If the external magnetic field strength is increased, the superconductor will
lose its superconducting state when the field strength exceeds the critical magnetic
field (Hc) for that temperature. However, the critical temperature (Tc) itself
remains unchanged.
- If the current density is increased, the superconductor will lose its superconducting
state when the current density exceeds the critical current density (Jc) for that
temperature. Again, the critical temperature (Tc) itself remains unchanged.
In summary, the critical temperature (Tc) of a superconducting wire is an intrinsic
property of the material and is not directly affected by the magnetic field strength
or current density. However, the superconducting state can be lost if the magnetic
field strength or current density exceeds their respective critical values (Hc
and Jc) for a given temperature.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("OmarIDK/finetuned_rag_retriever")
# Run inference
sentences = [
'What are the steps involved in designing small molecule drugs targeting respiratory disease pathways such as bronchoconstriction and airway inflammation?',
"How can we design small molecule drugs targeting specific respiratory disease pathways, such as bronchoconstriction or airway inflammation, using medicinal chemistry approaches?\n\nDesigning small molecule drugs targeting specific respiratory disease pathways, such as bronchoconstriction or airway inflammation, can be achieved through a systematic medicinal chemistry approach. This involves several key steps:\n\n1. Target identification and validation: The first step is to identify and validate the molecular targets involved in the respiratory disease pathways. These targets can be proteins, enzymes, or receptors that play a crucial role in bronchoconstriction or airway inflammation. Examples of such targets include beta-2 adrenergic receptors, muscarinic receptors, and leukotriene receptors.\n\n2. Hit identification: Once the target is identified, the next step is to find small molecules that can interact with the target and modulate its activity. This can be done through various techniques, such as high-throughput screening (HTS) of compound libraries, fragment-based drug discovery, or in silico (computer-aided) drug design.\n\n3. Hit-to-lead optimization: After identifying the initial hits, medicinal chemists optimize these compounds to improve their potency, selectivity, and drug-like properties. This involves synthesizing and testing a series of analogs with slight modifications in their chemical structure to identify the structure-activity relationship (SAR). The goal is to find a lead compound with the desired biological activity and minimal off-target effects.\n\n4. Lead optimization: The lead compound is further optimized to enhance its pharmacokinetic (PK) and pharmacodynamic (PD) properties, such as absorption, distribution, metabolism, excretion, and toxicity (ADMET). This step involves fine-tuning the chemical structure to improve the drug's overall performance, including its solubility, stability, and bioavailability.\n\n5. Preclinical testing: The optimized lead compound undergoes extensive preclinical testing in vitro (cell-based assays) and in vivo (animal models) to evaluate its safety, efficacy, and mechanism of action. This helps to determine the compound's suitability for further development as a drug candidate.\n\n6. Clinical trials: If the preclinical data are promising, the drug candidate proceeds to clinical trials, where its safety and efficacy are tested in human subjects. This involves a phased approach, starting with Phase I (safety and dosage), Phase II (efficacy and side effects), and Phase III (comparison with existing treatments) trials.\n\n7. Regulatory approval and post-marketing surveillance: If the drug candidate demonstrates safety and efficacy in clinical trials, it can be submitted for regulatory approval (e.g., by the FDA). Once approved, the drug is marketed and subjected to post-marketing surveillance to monitor its long-term safety and effectiveness in the general population.\n\nIn summary, designing small molecule drugs",
'The solar system consists of the Sun and all celestial bodies that are bound to it by gravity, including eight planets, their moons, and various smaller objects such as dwarf planets and asteroids. The planets in order from the Sun are Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. Each planet has unique characteristics, including its composition, atmosphere, and surface conditions. For instance, Jupiter is known for its Great Red Spot, a giant storm, while Saturn is famous for its prominent ring system. The study of the solar system helps scientists understand planetary formation, the potential for life on other planets, and the history of our own planet Earth.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 498 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 498 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 27.13 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 248.96 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 162.92 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the heat of crystallization of sodium acetate trihydrate when dissolved in water and cooled?</code> | <code>What is the heat of crystallization of 50 g of sodium acetate trihydrate (NaC2H3O2.3H2O) when it is dissolved in 200 mL of water at 25°C and then the resulting solution is cooled to 10°C, assuming that no heat is lost to the surroundings? The heat of solution of sodium acetate trihydrate is -4245 J/g. <br><br>To find the heat of crystallization of sodium acetate trihydrate, we first need to calculate the heat released when it is dissolved in water and then the heat absorbed when the solution is cooled.<br><br>1. Heat released during dissolution:<br>Heat of solution = -4245 J/g<br>Mass of sodium acetate trihydrate = 50 g<br><br>Heat released = Heat of solution × Mass<br>Heat released = -4245 J/g × 50 g = -212250 J<br><br>Since the heat released is negative, it means that the dissolution process is exothermic and releases heat to the surroundings.<br><br>2. Heat absorbed during cooling:<br>We will use the formula q = mcΔT, where q is the heat absorbed, m is the mass of the solution, c is the specific heat capacity of water (4.1...</code> | <code>The process of cellular respiration is vital for the survival of most living organisms. It involves the conversion of glucose and oxygen into energy, carbon dioxide, and water. This metabolic process occurs in several stages, including glycolysis, the citric acid cycle, and oxidative phosphorylation. During glycolysis, glucose is broken down into pyruvate, yielding a small amount of ATP. The citric acid cycle then processes pyruvate to produce electron carriers such as NADH and FADH2, which are crucial for the final stage of respiration. In oxidative phosphorylation, these carriers donate electrons to the electron transport chain, ultimately leading to the production of a large amount of ATP. Understanding cellular respiration is essential for studies in biology and medicine, as it provides insights into energy production, metabolic diseases, and overall cell function.</code> |
| <code>How can the concentration of copper (II) ions in a water sample be determined using colorimetry?</code> | <code>How can the concentration of copper (II) ions in a water sample be quantified using colorimetry?<br><br>Colorimetry is a technique used to determine the concentration of a colored compound in a solution by measuring the absorbance of light at a specific wavelength. In this case, we want to quantify the concentration of copper (II) ions in a water sample. To do this, we can follow these steps:<br><br>1. Prepare a series of copper (II) ion standards: Prepare a stock solution of known concentration of copper (II) sulfate (CuSO4). Then, create a series of standard solutions with varying concentrations of copper (II) ions by diluting the stock solution with distilled water. For example, you can prepare 5 standard solutions with concentrations of 1, 2, 4, 6, and 8 ppm (parts per million).<br><br>2. Complex formation: Copper (II) ions in solution are not very colorful and do not strongly absorb visible light. To enhance the color and improve the sensitivity of the measurement, we can form a complex with a suit...</code> | <code>The process of osmosis is crucial for maintaining the balance of fluids in biological systems. It involves the movement of water molecules through a semi-permeable membrane from an area of lower solute concentration to an area of higher solute concentration. This process is essential for cell function, as it helps regulate hydration, nutrient absorption, and waste removal. Osmosis plays a vital role in various physiological processes, including the operation of kidneys and the overall homeostasis of organisms. Understanding osmosis is important in fields such as biology, medicine, and environmental science.</code> |
| <code>How can biotechnology be utilized to create drought-resistant crops and improve their yield and resilience in varying environmental conditions?</code> | <code>Biotechnology can be used to create drought-resistant crops through various approaches, including genetic engineering, marker-assisted breeding, and gene editing techniques. These methods can help increase the yield and overall resilience of crops in different environmental conditions. Some specific methods are:<br><br>1. Genetic engineering: This involves the direct manipulation of an organism's genes using biotechnology. Scientists can introduce specific genes from drought-resistant plants or other organisms into the target crop, conferring drought tolerance. For example, the introduction of the DREB (dehydration-responsive element-binding) gene family, which regulates the expression of drought-responsive genes, has been shown to improve drought tolerance in crops like rice and wheat.<br><br>2. Marker-assisted breeding: This technique combines traditional plant breeding with molecular biology. It involves identifying specific DNA markers associated with drought tolerance traits in plants and usi...</code> | <code>The process of cellular respiration is crucial for all aerobic organisms, as it allows them to convert biochemical energy from nutrients into adenosine triphosphate (ATP). This process occurs in several stages:<br><br>1. Glycolysis: This stage takes place in the cytoplasm and breaks down glucose into pyruvate, producing a small amount of ATP and NADH.<br><br>2. Krebs Cycle: Also known as the citric acid cycle, this stage occurs in the mitochondria, where pyruvate is further broken down, releasing carbon dioxide and transferring high-energy electrons to carriers like NADH and FADH₂.<br><br>3. Electron Transport Chain: The electrons from NADH and FADH₂ are passed through a series of proteins in the mitochondrial membrane, leading to the production of a large amount of ATP and the reduction of oxygen to form water.<br><br>4. Anaerobic Respiration: In the absence of oxygen, some organisms can undergo anaerobic respiration, which allows them to generate energy through processes like fermentation, but less efficien...</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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|
dhruvsangani/FEAT_CHATBOT_AI_2000_PLUS_DATA
|
dhruvsangani
| 2025-06-06T12:40:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:39:53Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dhruvsangani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-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)
|
kaidhar/gemma-3-kd-finetune
|
kaidhar
| 2025-06-06T12:38:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-06T12:35:49Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** kaidhar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
leobianco/bosch_RM_google_S_130104_LLM_false_STRUCT_false_epochs_3_lr_5e-4_r_32_2506061228
|
leobianco
| 2025-06-06T12:38:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:28:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- 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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sp-embraceable/e1-olmo-13bInstruct-NTKScaled
|
sp-embraceable
| 2025-06-06T12:37:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:25:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
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[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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[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]
|
fh1628/open_answers_model_lr1e5_t2
|
fh1628
| 2025-06-06T12:36:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:36:31Z |
---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fh1628
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
This qwen3 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)
|
shulijia/MNLP_M3_mcqa_model_simpleVal_ep3_new
|
shulijia
| 2025-06-06T12:36:39Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T05:20:21Z |
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MNLP_M3_mcqa_model_simpleVal_ep3_new
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MNLP_M3_mcqa_model_simpleVal_ep3_new
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shulijia/MNLP_M3_mcqa_model_simpleVal_ep3_new", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.2
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
emiliensilly/doc_encoder50
|
emiliensilly
| 2025-06-06T12:34:50Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:235550",
"loss:TripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:thenlper/gte-small",
"base_model:finetune:thenlper/gte-small",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-06T12:34:41Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:235550
- loss:TripletLoss
base_model: thenlper/gte-small
widget:
- source_sentence: 'The following are multiple choice questions (with answers) about
knowledge and skills in advanced master-level STEM courses.
Which action would increase the amount of oxygen in a fish tank?
Answer:'
sentences:
- '### JK Flip-Flop Overview
A JK flip-flop is a type of digital storage element that can store one bit of
data and is used in sequential circuits. It is known for its versatility in toggling
states and is commonly used in various applications like counters, memory devices,
and state machines.
### Inputs and Behavior
The JK flip-flop has two inputs, labeled J and K, and one output, Q. The behavior
of the JK flip-flop is determined by the combination of these inputs:
1. **J Input**: Represents the set condition.
2. **K Input**: Represents the reset condition.
3. **Clock Input**: The flip-flop changes states on the clock edge (typically
on the rising edge).
### State Changes Based on Input Combinations
The JK flip-flop operates based on the following input combinations:
- **J = 0, K = 0**: No change in the output state (Q remains the same).
- **J = 0, K = 1**: The output Q is reset to 0.
- **J = 1, K = 0**: The output Q is set to 1.
- **J = 1, K = 1**: The output Q toggles (changes to the opposite state).
### Toggle Mode
The toggle mode occurs specifically when both J and K are set to 1 (J = 1, K =
1). In this mode, on each clock pulse, the output Q will change from 0 to 1 or
from 1 to 0, effectively toggling its state.
### Summary of Input Combinations
- **J = 0, K = 0**: No change.
- **J = 0, K = 1**: Reset to 0.
- **J = 1, K = 0**: Set to 1.
- **J = 1, K = 1**: Toggle.
This understanding of the JK flip-flop''s operation and the implications of the
input states is crucial for analyzing and designing circuits that utilize flip-flops.'
- '**Plant Life Cycle Stages:**
1. **Seed Stage**: The life cycle of a plant begins with a seed. Seeds contain
the embryonic plant and are typically formed after fertilization of the ovule.
They are often protected by a seed coat and contain stored nutrients.
2. **Germination**: When conditions are favorable (adequate moisture, temperature,
and sometimes light), the seed undergoes germination. The seed absorbs water,
swells, and breaks open, allowing the young plant (embryo) to emerge.
3. **Young Plant Stage (Seedling)**: After germination, the young plant or seedling
develops. It grows roots, stems, and leaves, and begins photosynthesis. This stage
is critical for establishing a strong structure to support further growth.
4. **Adult Plant Stage**: The plant continues to grow and develops reproductive
structures (flowers, cones, etc.). Once mature, the adult plant can reproduce,
creating new seeds, thus completing the cycle.
**Key Principles**:
- The plant life cycle is cyclical and involves alternation between the diploid
(2n) sporophyte stage and the haploid (n) gametophyte stage, although the sporophyte
phase is dominant in higher plants.
- The sequence of development is sequential and linear, starting from seed, progressing
to seedling, and culminating in an adult plant capable of reproduction.'
- "To understand how to increase the amount of oxygen in a fish tank, it's important\
\ to consider the following scientific principles:\n\n1. **Photosynthesis**: Aquatic\
\ plants perform photosynthesis, a process where they convert carbon dioxide and\
\ sunlight into glucose and oxygen. The general equation for photosynthesis is:\n\
\ \\[\n 6CO_2 + 6H_2O + light \\ energy \\rightarrow C_6H_{12}O_6 + 6O_2\n\
\ \\]\n This shows that for every six molecules of carbon dioxide and six\
\ molecules of water, six molecules of oxygen are produced, significantly increasing\
\ oxygen levels in the water.\n\n2. **Oxygen Levels and Biological Demand**: Adding\
\ more fish increases the biological oxygen demand (BOD) because fish consume\
\ oxygen for respiration. This may lead to a decrease in the overall oxygen levels\
\ if not balanced by oxygen production.\n\n3. **Role of Plants**: In addition\
\ to producing oxygen, aquatic plants also help in stabilizing the ecosystem by\
\ absorbing excess nutrients, which can otherwise lead to algal blooms that deplete\
\ oxygen.\n\n4. **Impact of Food and Heaters**: Placing food in the tank may lead\
\ to increased waste production from fish, which can further deplete oxygen levels\
\ as bacteria break down organic matter. A water heater primarily affects the\
\ temperature of the water and does not directly contribute to oxygen production.\n\
\nIn summary, adding more plants enhances the oxygen production through photosynthesis,\
\ while other actions may either increase oxygen demand or have no direct effect\
\ on oxygen levels."
- source_sentence: 'The following are multiple choice questions (with answers) about
knowledge and skills in advanced master-level STEM courses.
A teacher is conducting an investigation by using special equipment to hold a
magnesium (Mg) ribbon over the flame of a Bunsen burner. Which observation indicates
a chemical reaction took place?
Answer:'
sentences:
- "To understand how to construct a truth table and analyze the logical relationships\
\ between the propositions \\(A \\supset \\sim B\\) and \\(B \\supset A\\), it\
\ is important to familiarize ourselves with some key concepts in propositional\
\ logic.\n\n### Key Concepts\n\n1. **Propositions**: A proposition is a declarative\
\ statement that can either be true (T) or false (F).\n\n2. **Negation (\\(\\\
sim\\))**: The negation of a proposition \\(A\\) (notated as \\(\\sim A\\)) is\
\ true if \\(A\\) is false, and false if \\(A\\) is true.\n\n3. **Implication\
\ (\\(\\supset\\))**: The implication \\(A \\supset B\\) (read as \"A implies\
\ B\") is a compound statement that is false only when \\(A\\) is true and \\\
(B\\) is false. The truth table for \\(A \\supset B\\) is as follows:\n - T\
\ (True) implies T (True) = T\n - T implies F (False) = F\n - F implies T\
\ = T\n - F implies F = T\n\n### Truth Table Construction\n\nTo construct the\
\ truth table for the propositions \\(A \\supset \\sim B\\) and \\(B \\supset\
\ A\\), we need to consider all possible combinations of truth values for \\(A\\\
) and \\(B\\). There are four possible combinations (TT, TF, FT, FF) for the truth\
\ values of \\(A\\) and \\(B\\).\n\n#### Steps to Create the Truth Table\n\n1.\
\ **List all combinations of truth values for \\(A\\) and \\(B\\)**:\n - \\\
(A = T\\), \\(B = T\\)\n - \\(A = T\\), \\(B = F\\)\n - \\(A = F\\), \\(B\
\ = T\\)\n - \\(A = F\\), \\(B = F\\)\n\n2. **Compute \\(\\sim B\\)** for each\
\ combination.\n\n3. **Evaluate \\(A \\supset \\sim B\\)** and \\(B \\supset A\\\
) for each combination using the definition of implication.\n\n4. **Summarize\
\ the results in a truth table format**.\n\n### Analysis of Logical Relationships\n\
\nAfter completing the truth table, the next step is to analyze the logical relationships\
\ between the two propositions:\n\n- **Logically Equivalent**: Two propositions\
\ are logically equivalent if they have the same truth values in all possible\
\ scenarios.\n\n- **Contradictory**: Two propositions are contradictory if they\
\ cannot both be true at the same time. This means that in every scenario, one\
\ proposition is true while the other is false.\n\n- **Consistent**: Two propositions\
\ are consistent if there is at least one scenario where both can be true simultaneously.\n\
\n- **Inconsistent**: Two propositions are inconsistent if there is no scenario\
\ in which both can be true at the same time.\n\n### Conclusion Steps\n\nTo determine\
\ the correct classification (either logically equivalent, contradictory, or consistent/inconsistent),\
\ you will need to analyze the results of the truth table you constructed. \n\n\
By comparing the truth values of \\(A \\supset \\sim B\\) and \\(B \\supset A\\\
) across all combinations, you will identify whether they are logically equivalent,\
\ contradictory, or neither but consistent. Make sure to justify your classification\
\ based on the truth values observed. \n\nThis structured approach will lead you\
\ to the conclusion regarding the relationship between the two propositions."
- "To understand the most common naturally-occurring form of silicon, it is essential\
\ to examine its chemical properties and occurrences in nature.\n\n1. **Silicon\
\ Basics**: \n - Silicon (Si) is a chemical element with atomic number 14 and\
\ is classified as a metalloid. It is known for its ability to form covalent bonds\
\ with other elements and is a key component in many minerals.\n\n2. **Silicon\
\ Oxides**:\n - Silicon predominantly occurs in nature in the form of silicon\
\ dioxide (SiO2), commonly known as silica. Silica is a major constituent of sand,\
\ quartz, and various types of rock. \n - Silicon also forms silicates, which\
\ are compounds containing silicon and oxygen, often combined with metals. Silicates\
\ are the most abundant class of minerals in the Earth's crust.\n\n3. **Other\
\ Forms of Silicon**:\n - **Metallic Silicon**: While silicon can be found in\
\ a pure metallic form, this is much less common in nature. Metallic silicon is\
\ primarily produced through industrial processes and does not occur naturally\
\ in significant quantities.\n - **Sulfides and Fluorides**: Silicon does form\
\ compounds with sulfur and fluorine, but these are not abundant compared to silicon\
\ oxides. For example, silicates (which include silicon, oxygen, and metals) are\
\ vastly more prevalent than sulfides or fluorides involving silicon.\n\n4. **Natural\
\ Abundance**:\n - In the Earth's crust, silicon is the second most abundant\
\ element after oxygen. The majority of silicon found in nature is in the form\
\ of oxides and silicate minerals, making silicon oxides the primary naturally-occurring\
\ form.\n\n5. **Conclusion**:\n - Considering the properties of silicon and\
\ its compounds, the predominant form in which silicon is found naturally is as\
\ silicon oxides (SiO2) and in various silicate minerals, rather than as a metallic\
\ element, sulfide, or fluoride.\n\nThis analysis highlights the significance\
\ of silicon oxides in the natural environment and the prevalence of silicon in\
\ these forms compared to other options provided."
- "To determine whether a chemical reaction has taken place, it's important to look\
\ for specific indicators. \n\n1. **Chemical Change Indicators**: Chemical reactions\
\ often produce new substances, which can be indicated by:\n - Color changes\n\
\ - Formation of a gas (bubbles)\n - Production of light or heat (exothermic\
\ reactions)\n - Formation of a precipitate\n\n2. **Combustion of Magnesium**:\
\ When magnesium burns, it reacts with oxygen in the air to form magnesium oxide\
\ (MgO). This is a vigorous reaction characterized by:\n - A bright white light\
\ emitted during the combustion process\n - A significant increase in temperature\n\
\n3. **Physical Changes vs. Chemical Changes**: \n - Physical changes (e.g.,\
\ change in shape, state of matter) do not involve the formation of new substances.\
\ For example, heating magnesium may change its temperature or shape but does\
\ not necessarily indicate a chemical reaction.\n - Chemical changes involve\
\ the transformation of reactants into products with distinct properties.\n\n\
4. **Energy Changes**: The production of light during a reaction indicates energy\
\ release, which is a hallmark of a chemical change.\n\nUnderstanding these principles\
\ helps in identifying the signs of a chemical reaction when magnesium is burned."
- source_sentence: 'The following are multiple choice questions (with answers) about
knowledge and skills in advanced master-level STEM courses.
Clouds bring rain and snow to Earth''s surface. How do rain and snow most support
life on Earth?
Answer:'
sentences:
- "To solve the equation \n\n$$(a x+3)\\left(5 x^{2}-b x+4\\right)=20 x^{3}-9 x^{2}-2\
\ x+12$$ \n\nfor the constants \\( a \\) and \\( b \\), we will need to expand\
\ the left-hand side and match the coefficients with those on the right-hand side.\n\
\n### Step 1: Expand the Left-Hand Side\n\nWe can expand the left-hand side of\
\ the equation using the distributive property (also known as the FOIL method\
\ for binomials). \n\nLet’s denote:\n- The first binomial: \\( (a x + 3) \\)\n\
- The second polynomial: \\( (5 x^{2} - b x + 4) \\)\n\nThe multiplication yields:\n\
\\[\n(a x + 3)(5 x^{2} - b x + 4) = a x(5 x^{2}) + a x(-b x) + a x(4) + 3(5 x^{2})\
\ + 3(-b x) + 3(4)\n\\]\n\nThis expands to:\n\\[\n5 a x^{3} - ab x^{2} + 4 a x\
\ + 15 x^{2} - 3b x + 12\n\\]\n\n### Step 2: Collect Like Terms\n\nNow, we collect\
\ like terms in the expression:\n- The coefficient of \\( x^3 \\) is \\( 5a \\\
).\n- The coefficient of \\( x^2 \\) is \\( -ab + 15 \\).\n- The coefficient of\
\ \\( x \\) is \\( 4a - 3b \\).\n- The constant term is \\( 12 \\).\n\n### Step\
\ 3: Set Up Coefficient Equations\n\nSince the equation is true for all \\( x\
\ \\), we can equate the coefficients from both sides of the equation:\n\n1. For\
\ \\( x^3 \\): \n \\[\n 5a = 20 \\quad \\Rightarrow \\quad a = 4\n \\]\n\
\n2. For \\( x^2 \\):\n \\[\n -ab + 15 = -9 \\quad \\Rightarrow \\quad -ab\
\ = -9 - 15 \\quad \\Rightarrow \\quad ab = 24\n \\]\n\n3. For \\( x \\):\n\
\ \\[\n 4a - 3b = -2\n \\]\n\n### Step 4: Solve for \\( b \\)\n\nSubstituting\
\ \\( a = 4 \\) into the equation \\( 4a - 3b = -2 \\):\n\\[\n4(4) - 3b = -2 \\\
quad \\Rightarrow \\quad 16 - 3b = -2 \\quad \\Rightarrow \\quad -3b = -2 - 16\
\ \\quad \\Rightarrow \\quad -3b = -18 \\quad \\Rightarrow \\quad b = 6\n\\]\n\
\n### Step 5: Find \\( ab \\)\n\nNow that we have the values of \\( a \\) and\
\ \\( b \\):\n- \\( a = 4 \\)\n- \\( b = 6 \\)\n\nNow we can calculate \\( ab\
\ \\):\n\\[\nab = 4 \\cdot 6 = 24\n\\]\n\n### Conclusion\n\nThe product of \\\
( a \\) and \\( b \\) is \\( 24 \\). Thus, the value of \\( ab \\) is identified\
\ as part of the analysis of polynomial coefficients, leading to the conclusion\
\ that the correct choice is C. 24."
- "**Supporting Knowledge:**\n\n- **Water Cycle**: Precipitation, including rain\
\ and snow, is a key component of the water cycle, which is essential for replenishing\
\ freshwater sources on land. \n\n- **Importance of Freshwater**: Freshwater is\
\ vital for all terrestrial life forms. It is required for drinking, agriculture,\
\ and various ecological processes.\n\n- **Role of Precipitation in Ecosystems**:\
\ Rain and snow help maintain soil moisture levels, support plant growth, and\
\ sustain various ecosystems by providing the necessary hydration for organisms.\n\
\n- **Impact on Agriculture**: Adequate rainfall is crucial for crop growth, which\
\ in turn supports food chains and human agriculture.\n\nUnderstanding these principles\
\ highlights the significance of precipitation in supporting terrestrial life\
\ through the provision of freshwater."
- "To understand which type of radiation can or cannot be deflected by electrical\
\ or magnetic fields, it is important to examine the properties of alpha rays,\
\ beta rays, and gamma rays.\n\n1. **Alpha Rays**:\n - Alpha rays are composed\
\ of alpha particles, which are made up of two protons and two neutrons (essentially\
\ helium nuclei).\n - They carry a positive charge due to the presence of protons.\n\
\ - Because of their charge and relatively large mass, alpha particles are deflected\
\ by electric and magnetic fields. The degree of deflection is influenced by the\
\ strength of the field and the velocity of the alpha particles.\n\n2. **Beta\
\ Rays**:\n - Beta rays consist of beta particles, which are high-energy, high-speed\
\ electrons or positrons emitted by certain types of radioactive decay.\n -\
\ Electrons have a negative charge, while positrons have a positive charge.\n\
\ - Beta particles are significantly lighter than alpha particles and can also\
\ be deflected by electric and magnetic fields. The deflection occurs due to their\
\ charge and can be observed in experiments involving particle accelerators.\n\
\n3. **Gamma Rays**:\n - Gamma rays are a form of electromagnetic radiation,\
\ similar to X-rays, and are not made up of charged particles.\n - They have\
\ no mass and no charge, which means they are not affected by electric or magnetic\
\ fields.\n - Gamma radiation typically penetrates matter more effectively than\
\ alpha or beta radiation and is often emitted from radioactive decay processes.\n\
\nIn summary, the ability to be deflected by electric or magnetic fields is determined\
\ by the charge and mass of the particles involved. Charged particles (alpha and\
\ beta rays) can be deflected, while uncharged particles (gamma rays) cannot be\
\ affected in this way."
- source_sentence: 'The following are multiple choice questions (with answers) about
knowledge and skills in advanced master-level STEM courses.
A young child is brought to a psychologist for evaluation of their home situation.
The child is placed in the middle of the floor, with the mother on one side and
the psychologist on the other. The mother then leaves for a short while, and then
returns. Which of the following would be a concerning sign during this evaluation?
Answer:'
sentences:
- "To understand the context of the evaluation and the potential signs of concern,\
\ it is important to consider several psychological principles related to attachment\
\ theory and child behavior.\n\n### 1. Attachment Theory\n- **Definition**: Attachment\
\ theory, developed by John Bowlby and later expanded by Mary Ainsworth, explores\
\ the bonds between children and their caregivers. It suggests that the emotional\
\ bond formed in early childhood is crucial for social and emotional development.\n\
- **Types of Attachment**: Typically, children exhibit different attachment styles,\
\ including secure, anxious-avoidant, and anxious-resistant attachment. Each style\
\ presents distinct behavioral patterns in response to caregiver separation and\
\ reunion.\n\n### 2. Child Behavior During Separation and Reunion\n- **Separation\
\ Anxiety**: Many young children experience a natural fear of being separated\
\ from their primary caregivers, which can manifest as crying or reluctance to\
\ explore when the caregiver leaves.\n- **Reunion Behaviors**: The way a child\
\ reacts upon the return of the caregiver can provide insights into their attachment\
\ style:\n - **Secure Attachment**: Children with secure attachments generally\
\ feel comfortable exploring their environment when the caregiver is present and\
\ may seek proximity upon reunion, showing joy and relief.\n - **Avoidant Attachment**:\
\ Children with avoidant attachments may not seek out the caregiver upon return,\
\ displaying indifference or avoidance.\n - **Anxious Attachment**: These children\
\ may exhibit clinginess or distress upon separation and may also struggle to\
\ calm down after reunion.\n\n### 3. Exploration Behavior\n- **Exploratory Behavior**:\
\ Children's willingness to explore their environment is often correlated with\
\ their feelings of security. A child who feels secure is more likely to engage\
\ in exploration, knowing they can return to their caregiver for comfort if needed.\n\
\n### 4. Indicators of Concern\n- **Avoidance Upon Reunion**: If a child avoids\
\ the caregiver upon their return, this can indicate an insecure attachment style,\
\ potentially signaling emotional distress or issues with the caregiver-child\
\ relationship.\n- **Other Behaviors**: While behaviors such as crying upon separation\
\ or returning to the mother can indicate a healthy attachment response, avoidance\
\ can be a red flag that warrants further evaluation.\n\nBy understanding these\
\ principles, one can analyze the child's responses in the context of their attachment\
\ to the mother and the implications for their emotional and psychological well-being."
- "**Supporting Knowledge on Plant and Animal Cells:**\n\n1. **Photosynthesis:**\n\
\ - Plant cells contain chloroplasts, which are organelles that conduct photosynthesis,\
\ allowing plants to convert light energy into chemical energy (glucose) using\
\ carbon dioxide and water. The chemical equation for photosynthesis is:\n \
\ \\[\n 6CO_2 + 6H_2O + \\text{light energy} \\rightarrow C_6H_{12}O_6 +\
\ 6O_2\n \\]\n\n2. **Energy Storage:**\n - Both plant and animal cells store\
\ energy, but they do so in different forms. Plant cells primarily store energy\
\ as starch, while animal cells store energy as glycogen.\n\n3. **Cell Structure:**\n\
\ - Plant cells have a rigid cell wall made of cellulose, which provides structural\
\ support. Animal cells lack a cell wall and have a more flexible cell membrane.\n\
\ - Plant cells often contain large central vacuoles for storage and maintaining\
\ turgor pressure, while animal cells have smaller vacuoles.\n\n4. **Reproduction:**\n\
\ - Both plant and animal cells can reproduce, though the mechanisms differ.\
\ Plant cells can reproduce asexually through vegetative propagation and sexually\
\ through seeds.\n\n5. **Organelles:**\n - In addition to chloroplasts, plant\
\ cells have unique structures like plasmodesmata, which allow for communication\
\ between cells, while animal cells have lysosomes that are more common for digestion\
\ and waste removal. \n\nUnderstanding these differences can help in identifying\
\ the unique functions that each type of cell performs in their respective organisms."
- "To understand the phenomenon of a plant growing along a trellis, it is essential\
\ to explore the concepts of different types of tropisms, which are directional\
\ growth responses of plants to environmental stimuli. Here’s a breakdown of the\
\ relevant concepts:\n\n1. **Tropism**: This term refers to the growth or movement\
\ of a plant in response to an environmental stimulus. Tropisms can be classified\
\ based on the type of stimulus they respond to.\n\n2. **Thigmotropism**: This\
\ is a type of tropism where plants respond to touch or physical contact. Plants\
\ that exhibit thigmotropism often grow towards or around structures for support,\
\ such as a trellis or other plants. This response is crucial for climbing plants,\
\ which use tendrils or other specialized structures to anchor themselves and\
\ reach sunlight.\n\n3. **Phototropism**: This refers to the growth of a plant\
\ in response to light. Plants typically exhibit positive phototropism, meaning\
\ they grow towards the light source. This phenomenon is facilitated by the hormone\
\ auxin, which redistributes in response to light, causing differential growth\
\ on one side of the plant.\n\n4. **Gravitropism** (also known as geotropism):\
\ This is the growth response of a plant to gravity. Roots typically show positive\
\ gravitropism (growing downwards) while stems exhibit negative gravitropism (growing\
\ upwards). \n\n5. **Negative Gravitropism**: This specifically refers to the\
\ upward growth of plant shoots against the force of gravity, allowing them to\
\ emerge above ground and access light.\n\nUnderstanding these concepts will help\
\ in identifying the correct type of growth response exhibited by a plant growing\
\ along a trellis. Each type of tropism serves a distinct function and is triggered\
\ by specific stimuli, which are essential for plants' survival and adaptation\
\ in their environments."
- source_sentence: 'The following are multiple choice questions (with answers) about
knowledge and skills in advanced master-level STEM courses.
Standing waves are the result of
Answer:'
sentences:
- '**Label Propagation**: A semi-supervised learning technique used for community
detection and classification in graphs.
**Key Concepts**:
1. **Labels**: In label propagation, nodes in a graph can carry labels, which
may represent categories or classes. Some nodes have labels known apriori (initially
assigned), while others do not.
2. **Random Walk Model**: Label propagation can be understood as a random walk
on the graph. In this model, the probability of moving from one node to another
is dependent on the edges connecting them, allowing labels to spread across the
network based on connectivity.
3. **High Degree Nodes**: High degree nodes in a graph have many connections (edges)
to other nodes. These nodes can significantly influence the propagation of labels
due to their connectivity.
4. **Abandoning Probability**: This refers to the likelihood that a node will
stop propagating its label. A low abandoning probability implies that a node is
less likely to stop spreading its label.
5. **Injection Probability**: This term refers to the likelihood of introducing
a label into the propagation process. When labels come from experts, the assumption
is that they carry higher reliability and validity compared to labels from crowdworkers,
which may warrant a higher injection probability.
Understanding these concepts is crucial for evaluating the statements related
to label propagation and determining which may be false.'
- "To understand the application of antivirals in various clinical circumstances,\
\ it's essential to explore the definitions and uses of antiviral medications,\
\ particularly in relation to the choices provided in the question.\n\n### Antivirals\
\ Overview\nAntivirals are a class of medications designed to treat viral infections\
\ by inhibiting the development of the pathogen. They can be employed either prophylactically\
\ (to prevent infection) or therapeutically (to treat existing infections). The\
\ effectiveness of antiviral drugs often depends on timing and the specific population\
\ being treated.\n\n### Circumstances for Antiviral Use\n\n1. **Timing of Administration**:\n\
\ - **Within 4 days of clinical signs**: Antivirals are most effective when\
\ administered early in the course of a viral infection. For many viral illnesses,\
\ treatment should ideally start within the first 48 hours of symptom onset to\
\ maximize efficacy.\n - **Within 48 hours of first clinical signs**: This is\
\ a common guideline for many antiviral therapies, especially for influenza and\
\ some other viral infections. Early administration helps to reduce the severity\
\ and duration of illness.\n\n2. **Specific Populations**:\n - **Obesity**:\
\ Research indicates that individuals with obesity may have an altered response\
\ to viral infections and may experience more severe outcomes when infected. This\
\ has led to investigations into the prophylactic and therapeutic use of antivirals\
\ in this population. The rationale is that because of the increased risk of complications\
\ from viral infections in obese individuals, antiviral medications may provide\
\ significant benefits in both preventing and treating infections.\n - **Children\
\ under the age of 2**: While young children are at risk of severe illness from\
\ viral infections, the use of antivirals in this age group can be complicated\
\ due to safety profiles and dosage considerations. Therefore, antiviral use is\
\ typically approached with caution, especially in the context of widespread viral\
\ spread.\n\n### Implications of Choices\n- **Choice A (Within 4 days)**: This\
\ option is somewhat accurate in the context of antiviral use, but it does not\
\ specify the optimal period (48 hours) for maximum effectiveness.\n- **Choice\
\ B (Within 48 hours)**: This is a strong candidate, as it aligns with the established\
\ guidelines for many antivirals.\n- **Choice C (Obese)**: This reflects an evolving\
\ understanding of the need for targeted antiviral strategies in populations at\
\ higher risk due to obesity.\n- **Choice D (Children under 2)**: While children\
\ may need antivirals, the indication is not as straightforward due to safety\
\ concerns and the specifics of the viral infection.\n\n### Conclusion\nIn evaluating\
\ the use of antivirals, it's crucial to consider the timing of administration\
\ and the specific characteristics of the population being treated. Each choice\
\ reflects different aspects of antiviral application, but the rising acknowledgment\
\ of obesity as a significant risk factor for severe viral infections indicates\
\ an emerging focus on this group for both prophylactic and therapeutic strategies."
- "To understand standing waves, it's essential to explore the concepts of interference,\
\ wave behavior, and reflection.\n\n1. **Interference**: This is a phenomenon\
\ that occurs when two or more waves meet while traveling along the same medium.\
\ The principle of superposition states that the resultant wave at any point is\
\ the sum of the displacements of the individual waves. There are two types of\
\ interference:\n - **Constructive Interference**: Occurs when waves overlap\
\ in phase, meaning their peaks and troughs align, resulting in a wave of greater\
\ amplitude.\n - **Destructive Interference**: Takes place when waves overlap\
\ out of phase, where a peak of one wave coincides with a trough of another, leading\
\ to a reduction in amplitude.\n\n2. **Waves Overlapping In Phase and Out of Phase**:\
\ \n - **In Phase**: When waves are perfectly aligned (e.g., crest to crest,\
\ trough to trough), they reinforce each other, producing larger amplitude.\n\
\ - **Out of Phase**: When waves are misaligned (e.g., crest to trough), they\
\ can cancel each other out, leading to reduced or null amplitude.\n\n3. **Reflection\
\ of Waves**: When waves encounter a boundary (such as the end of a string or\
\ a wall), they can reflect back into the medium. This reflection can lead to\
\ the formation of standing waves if the conditions are right. The reflected wave\
\ can interfere with the incoming wave, leading to regions of constructive and\
\ destructive interference.\n\n4. **Standing Waves**: These are a specific type\
\ of wave pattern that results from the interference of two waves traveling in\
\ opposite directions. Standing waves are characterized by:\n - **Nodes**: Points\
\ of no displacement where destructive interference occurs.\n - **Antinodes**:\
\ Points of maximum displacement where constructive interference occurs.\n\n5.\
\ **Conditions for Standing Waves**: For standing waves to form, certain conditions\
\ must be met, including the proper frequency and the physical constraints of\
\ the medium (such as length and tension in strings). The wavelengths of the waves\
\ must fit into the physical boundaries of the medium, creating a pattern that\
\ appears to be stationary.\n\nGiven this background, it is evident that standing\
\ waves can be produced by interference of waves, overlapping in phase or out\
\ of phase, and reflecting upon themselves, which collectively leads to the formation\
\ of the standing wave pattern observed in various physical systems."
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on thenlper/gte-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("emiliensilly/doc_encoder50")
# Run inference
sentences = [
'The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.\n\nStanding waves are the result of\nAnswer:',
"To understand standing waves, it's essential to explore the concepts of interference, wave behavior, and reflection.\n\n1. **Interference**: This is a phenomenon that occurs when two or more waves meet while traveling along the same medium. The principle of superposition states that the resultant wave at any point is the sum of the displacements of the individual waves. There are two types of interference:\n - **Constructive Interference**: Occurs when waves overlap in phase, meaning their peaks and troughs align, resulting in a wave of greater amplitude.\n - **Destructive Interference**: Takes place when waves overlap out of phase, where a peak of one wave coincides with a trough of another, leading to a reduction in amplitude.\n\n2. **Waves Overlapping In Phase and Out of Phase**: \n - **In Phase**: When waves are perfectly aligned (e.g., crest to crest, trough to trough), they reinforce each other, producing larger amplitude.\n - **Out of Phase**: When waves are misaligned (e.g., crest to trough), they can cancel each other out, leading to reduced or null amplitude.\n\n3. **Reflection of Waves**: When waves encounter a boundary (such as the end of a string or a wall), they can reflect back into the medium. This reflection can lead to the formation of standing waves if the conditions are right. The reflected wave can interfere with the incoming wave, leading to regions of constructive and destructive interference.\n\n4. **Standing Waves**: These are a specific type of wave pattern that results from the interference of two waves traveling in opposite directions. Standing waves are characterized by:\n - **Nodes**: Points of no displacement where destructive interference occurs.\n - **Antinodes**: Points of maximum displacement where constructive interference occurs.\n\n5. **Conditions for Standing Waves**: For standing waves to form, certain conditions must be met, including the proper frequency and the physical constraints of the medium (such as length and tension in strings). The wavelengths of the waves must fit into the physical boundaries of the medium, creating a pattern that appears to be stationary.\n\nGiven this background, it is evident that standing waves can be produced by interference of waves, overlapping in phase or out of phase, and reflecting upon themselves, which collectively leads to the formation of the standing wave pattern observed in various physical systems.",
'**Label Propagation**: A semi-supervised learning technique used for community detection and classification in graphs.\n\n**Key Concepts**:\n\n1. **Labels**: In label propagation, nodes in a graph can carry labels, which may represent categories or classes. Some nodes have labels known apriori (initially assigned), while others do not.\n\n2. **Random Walk Model**: Label propagation can be understood as a random walk on the graph. In this model, the probability of moving from one node to another is dependent on the edges connecting them, allowing labels to spread across the network based on connectivity.\n\n3. **High Degree Nodes**: High degree nodes in a graph have many connections (edges) to other nodes. These nodes can significantly influence the propagation of labels due to their connectivity.\n\n4. **Abandoning Probability**: This refers to the likelihood that a node will stop propagating its label. A low abandoning probability implies that a node is less likely to stop spreading its label.\n\n5. **Injection Probability**: This term refers to the likelihood of introducing a label into the propagation process. When labels come from experts, the assumption is that they carry higher reliability and validity compared to labels from crowdworkers, which may warrant a higher injection probability.\n\nUnderstanding these concepts is crucial for evaluating the statements related to label propagation and determining which may be false.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 235,550 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 30 tokens</li><li>mean: 57.91 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 156 tokens</li><li>mean: 414.36 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 413.69 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.<br><br>In a population of brown snakes, a snake is born with a white-spotted pattern. Which factor will have the most influence on whether this trait will become common in the brown snake population?<br>Answer:</code> | <code>To understand the factors influencing the prevalence of a trait in a population, it is essential to consider principles of natural selection and evolutionary biology. <br><br>1. **Natural Selection**: This principle asserts that individuals with traits that provide a survival or reproductive advantage are more likely to pass those traits to the next generation. If the white-spotted pattern enhances the snake's ability to survive in its environment, it may become more common over time.<br><br>2. **Survival and Reproduction**: The survival of an organism to reproductive age is critical. Factors such as predation, camouflage, and mating preferences can impact whether the individual successfully reproduces. If a trait aids in evading predators or attracting mates, it will likely increase in frequency in the population.<br><br>3. **Genetic Variation**: The presence of variations within a population contributes to evolutionary change. Traits arise from genetic mutations, and those that confer advantages can b...</code> | <code>**Precision and Recall Overview:**<br>- Precision is the ratio of relevant documents retrieved to the total documents retrieved. It is calculated using the formula:<br> \[<br> \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}}<br> \]<br><br>- Recall, also known as Sensitivity, is the ratio of relevant documents retrieved to the total relevant documents available. It is calculated using the formula:<br> \[<br> \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}<br> \]<br><br>**Relationship Between Precision and Recall:**<br>- Precision and Recall are often inversely related; as you increase the number of documents retrieved (increasing recall), precision may decrease because more irrelevant documents are likely included.<br><br>**Adjusting Output to Control Recall:**<br>- To compute precision at different levels of recall, systems can be adjusted to output a varying number of documents. This can be done by:<br> - Setting thresholds for releva...</code> |
| <code>The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.<br><br>If both parents are affected with the same autosomal recessive disorder then the probability that each of their children will be affected equals ___.<br>Answer:</code> | <code>### Understanding Autosomal Recessive Disorders<br><br>**Definition of Autosomal Recessive Disorders:**<br>Autosomal recessive disorders are genetic conditions that occur when an individual inherits two copies of a mutated gene, one from each parent. For a child to be affected by such a disorder, both alleles (the gene variants inherited from each parent) must be recessive.<br><br>**Genotype Representation:**<br>- Let’s denote the normal allele as "A" and the recessive allele as "a."<br>- An individual with the genotype "AA" is unaffected (homozygous dominant).<br>- An individual with the genotype "Aa" is a carrier and is unaffected (heterozygous).<br>- An individual with the genotype "aa" is affected (homozygous recessive).<br><br>**Parental Genotypes in This Scenario:**<br>If both parents are affected by the same autosomal recessive disorder, their genotype must be "aa." This means they each carry two copies of the recessive allele.<br><br>### Punnett Square Analysis<br><br>To determine the probability of their children being affe...</code> | <code>To evaluate the validity of the argument using indirect truth tables, we need to understand several logical concepts, including implications, conjunctions, disjunctions, negations, and the structure of arguments in propositional logic.<br><br>### Key Concepts<br><br>1. **Implication (⊃)**: The expression \( P ⊃ Q \) can be interpreted as "if P, then Q". This is logically equivalent to \( \sim P ∨ Q \) (not P or Q). An implication is false only when the antecedent (P) is true and the consequent (Q) is false.<br><br>2. **Disjunction (∨)**: The expression \( Q ∨ R \) is true if at least one of Q or R is true. It is only false when both Q and R are false.<br><br>3. **Conjunction (·)**: The expression \( Q · S \) is true only if both Q and S are true. It is false if either or both of Q and S are false.<br><br>4. **Negation (∼)**: The negation of a statement flips its truth value. For example, if \( P \) is true, then \( \sim P \) is false.<br><br>5. **Indirect Truth Table Method**: This method involves assuming that the concl...</code> |
| <code>The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.<br><br>In which way is the Sun different from Earth?<br>Answer:</code> | <code>**Supporting Knowledge:**<br><br>1. **Nature of the Sun**: The Sun is classified as a star, which is an astronomical object primarily composed of hydrogen (about 74%) and helium (about 24%), along with trace amounts of heavier elements. Stars generate energy through nuclear fusion processes in their cores.<br><br>2. **Composition**: Unlike Earth, which is a terrestrial planet with a solid surface made up of rock and metal, the Sun does not have a solid surface. Its structure includes a core, radiative zone, and convective zone, all composed of plasma.<br><br>3. **Life Forms**: The Sun is not capable of supporting life as we know it. Earth, on the other hand, has a diverse range of organisms and ecosystems due to its stable climate and liquid water, which are essential for life.<br><br>4. **Galactic Position**: The Sun is indeed located within the Milky Way galaxy, but this is common to many astronomical bodies, including Earth, which is also part of the Milky Way.<br><br>5. **Moons**: The Sun does not have moons. M...</code> | <code>### Supporting Knowledge for Concurrent Transaction Management<br><br>**1. Concurrency in Programming:**<br> - In a multi-threaded environment, multiple threads can operate on shared data concurrently. This can lead to race conditions if proper synchronization is not implemented.<br><br>**2. Race Conditions:**<br> - A race condition occurs when two or more threads access shared data and try to change it at the same time. If the threads are not synchronized, the final state of the data can depend on the timing of how the threads are scheduled.<br><br>**3. Atomicity:**<br> - An operation is atomic if it completes in a single step relative to other threads. If parts of the operation can be interrupted, inconsistencies can occur.<br><br>**4. Consistency Properties:**<br> - **Non-negativity of Accounts:** An account balance should never drop below zero. This property requires that the check for sufficient funds and the withdrawal operation are atomic.<br> - **Conservation of Total Sum:** The total amount of money in th...</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0679 | 500 | 0.0809 |
| 0.1359 | 1000 | 0.0024 |
| 0.2038 | 1500 | 0.0013 |
| 0.2717 | 2000 | 0.0012 |
| 0.3396 | 2500 | 0.0007 |
| 0.4076 | 3000 | 0.0008 |
| 0.4755 | 3500 | 0.0006 |
| 0.5434 | 4000 | 0.0006 |
| 0.6113 | 4500 | 0.0005 |
| 0.6793 | 5000 | 0.0004 |
| 0.7472 | 5500 | 0.0003 |
| 0.8151 | 6000 | 0.0004 |
| 0.8830 | 6500 | 0.0005 |
| 0.9510 | 7000 | 0.0003 |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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|
zhiyuan8/RWKV-v7-2.9B-World-v3-GGUF
|
zhiyuan8
| 2025-06-06T12:34:00Z | 27 | 0 | null |
[
"gguf",
"text-generation",
"en",
"zh",
"ja",
"ko",
"fr",
"ar",
"es",
"pt",
"base_model:BlinkDL/rwkv-7-world",
"base_model:quantized:BlinkDL/rwkv-7-world",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-20T06:45:32Z |
---
license: apache-2.0
language:
- en
- zh
- ja
- ko
- fr
- ar
- es
- pt
metrics:
- accuracy
base_model:
- BlinkDL/rwkv-7-world
pipeline_tag: text-generation
---
|
Wizard0504/dpo-mcqa-finetuned8
|
Wizard0504
| 2025-06-06T12:31:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:29:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
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|
Oyasi/sealion-factory
|
Oyasi
| 2025-06-06T12:30:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T06:49:22Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
[More Information Needed]
|
kaidhar/gemma-3-kd
|
kaidhar
| 2025-06-06T12:29:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:29:45Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kaidhar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
fh1628/open_answers_model_lr1e5_t
|
fh1628
| 2025-06-06T12:28:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:28:30Z |
---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fh1628
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
This qwen3 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)
|
dgambettaphd/M_llm2_run0_gen3_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-06-06T12:25:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:25:26Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
morenolq/LEGIT-BART
|
morenolq
| 2025-06-06T12:19:10Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"legal-ai",
"italian-law",
"it",
"dataset:joelniklaus/Multi_Legal_Pile",
"base_model:morenolq/bart-it",
"base_model:finetune:morenolq/bart-it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-02-02T17:49:57Z |
---
language:
- it
tags:
- text2text-generation
- summarization
- legal-ai
- italian-law
license: mit
datasets:
- joelniklaus/Multi_Legal_Pile
library_name: transformers
pipeline_tag: text2text-generation
widget:
- text: "<mask> 1234: Il contratto si intende concluso quando..."
base_model:
- morenolq/bart-it
---
# 📌 Model Card: LEGIT-BART Series
## 🏛️ Model Overview
The **LEGIT-BART** models are a family of **pre-trained transformer-based models** for **Italian legal text processing**.
They build upon **BART-IT** ([`morenolq/bart-it`](https://huggingface.co/morenolq/bart-it)) and are further pre-trained on **Italian legal corpora**.
💡 Key features:
- **Extended context length** with **Local-Sparse-Global (LSG) Attention** (up to **16,384 tokens**) 📜
- **Trained on legal documents** such as **statutes, case law, and contracts** 📑
- **Not fine-tuned for specific tasks** (requires further adaptation)
## 📂 Available Models
| Model | Description | Link |
|--------|-------------|------|
| **LEGIT-BART** | Continued pre-training of `morenolq/bart-it` on **Italian legal texts** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-BART) |
| **LEGIT-BART-LSG-4096** | Continued pre-training of `morenolq/bart-it`, supporting **4,096 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-BART-LSG-4096) |
| **LEGIT-BART-LSG-16384** | Continued pre-training of `morenolq/bart-it`, supporting **16,384 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-BART-LSG-16384) |
| **LEGIT-SCRATCH-BART** | Trained from scratch on **Italian legal texts** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART) |
| **LEGIT-SCRATCH-BART-LSG-4096** | Trained from scratch with **LSG attention**, supporting **4,096 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART-LSG-4096) |
| **LEGIT-SCRATCH-BART-LSG-16384** | Trained from scratch with **LSG attention**, supporting **16,384 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART-LSG-16384) |
| **BART-IT-LSG-4096** | `morenolq/bart-it` with **LSG attention**, supporting **4,096 tokens** (no legal adaptation) | [🔗 Link](https://huggingface.co/morenolq/BART-IT-LSG-4096)
| **BART-IT-LSG-16384** | `morenolq/bart-it` with **LSG attention**, supporting **16,384 tokens** (no legal adaptation) | [🔗 Link](https://huggingface.co/morenolq/BART-IT-LSG-16384) |
---
## 🛠️ Model Details
🔹 **Architecture**
- Base Model: [`morenolq/bart-it`](https://huggingface.co/morenolq/bart-it)
- Transformer Encoder-Decoder
- **LSG Attention** for long documents
- Specific tokenizers for models trained from scratch (underperforming continual pre-training in our experiments).
🔹 **Training Data**
- Dataset: [`joelniklaus/Multi_Legal_Pile`](https://huggingface.co/datasets/joelniklaus/Multi_Legal_Pile)
- Types of legal texts used:
- **Legislation** (laws, codes, amendments)
- **Case law** (judicial decisions)
- **Contracts** (public legal agreements)
---
## 🚀 How to Use
```python
from transformers import BartForConditionalGeneration, AutoTokenizer
# Load tokenizer and model
model_name = "morenolq/LEGIT-BART"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
# Example input
input_text = "<mask> 1234: Il contratto si intende concluso quando..."
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
# Pre-trained model fill the mask
output_ids = model.generate(inputs.input_ids, max_length=150, num_beams=4, early_stopping=True)
output_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("📝:", output_text)
```
---
⚠️ Limitations & Ethical Considerations
- **Not fine-tuned for specific tasks**: The models are pre-trained on legal texts and may require further adaptation for specific legal NLP tasks (e.g., summarization, question-answering).
- **Bias and fairness**: Legal texts may contain biases present in the legal system. Care should be taken to ensure fairness and ethical use of the models.
- **Legal advice**: The models are not a substitute for professional legal advice. Always consult a qualified legal professional for legal matters.
---
## 📚 Reference
The paper presenting LEGIT-BART models is currently under review and will be updated here once published.
```bibtex
@article{benedetto2025legitbart,
title = {LegItBART: a summarization model for Italian legal documents},
author = {Benedetto, Irene and La Quatra, Moreno and Cagliero, Luca},
year = 2025,
journal = {Artificial Intelligence and Law},
publisher = {Springer},
pages = {1--31},
doi = {10.1007/s10506-025-09436-y},
url = {doi.org/10.1007/s10506-025-09436-y}
}
```
---
|
TheS3b/Qwen3-0.6B-quanto-4bit
|
TheS3b
| 2025-06-06T12:10:52Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"model_hub_mixin",
"8-bit",
"region:us"
] | null | 2025-06-06T08:20:45Z |
---
tags:
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
ljm008/lora_model
|
ljm008
| 2025-06-06T12:09:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T12:08:54Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ljm008
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-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)
|
myoo-oong/Korean-Sarcasm-LLM-llama-3.2-Korean-Bllossom-3B-CustomData-1
|
myoo-oong
| 2025-06-06T12:08:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T12:05:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gbennani/MNLP_M2_RAG_model_mcqa
|
gbennani
| 2025-06-06T12:04:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:timarni/qwen3_wiki_sciq_mmlu",
"base_model:finetune:timarni/qwen3_wiki_sciq_mmlu",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-05T16:20:04Z |
---
library_name: transformers
license: apache-2.0
base_model: timarni/qwen3_wiki_sciq_mmlu
tags:
- generated_from_trainer
model-index:
- name: MNLP_M2_RAG_model_mcqa
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. -->
# MNLP_M2_RAG_model_mcqa
This model is a fine-tuned version of [timarni/qwen3_wiki_sciq_mmlu](https://huggingface.co/timarni/qwen3_wiki_sciq_mmlu) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0
|
thisisanshgupta/ppo-LunarLander-v2-100000steps
|
thisisanshgupta
| 2025-06-06T12:03:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-06T09:13:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.28 +/- 16.40
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
Matjac5/MNLP_M3_rag_model
|
Matjac5
| 2025-06-06T12:02:47Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"qwen3",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:725795",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-04T19:00:39Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:725795
- loss:MultipleNegativesRankingLoss
base_model: Qwen/Qwen3-0.6B-Base
widget:
- source_sentence: What swims to female reproductive organs for fertilization?
sentences:
- '93.2'
- male gametes
- Quadrangular membrane
- source_sentence: Items are all ultimately compromised of which?
sentences:
- triplets
- Molecules
- 2.5 cm
- source_sentence: Which one of the following statements about chromatin is not true?
sentences:
- multicellular
- Maple syrup urine disease
- H2A-H2B bind to both the entry and exit ends of DNA in nucleosomes
- source_sentence: 'Widal test is an example of.......... Test.:'
sentences:
- Agglutination
- water
- 150 m
- source_sentence: The ratio of an object's mass to its volume is its
sentences:
- density.
- 500 m
- Oculocardiac reflex
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Qwen/Qwen3-0.6B-Base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) <!-- at revision 11214f7f3465775dcce23c3752ecea5a42ee0ddc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: Qwen3Model
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
"The ratio of an object's mass to its volume is its",
'density.',
'500 m',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 725,795 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 36.99 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 4.56 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------|
| <code>A balance can measure the weight of</code> | <code>sugar</code> |
| <code>The average monthly salary of 20 employees in an organisation is Rs. 1500. If the manager's salary is added, then the average salary increases by Rs. 100. What is the manager's monthly salary?</code> | <code>Rs.3600</code> |
| <code>When a baby shakes a rattle, it makes a noise. Which form of energy was changed to sound energy?</code> | <code>mechanical</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0110 | 500 | 1.3593 |
| 0.0220 | 1000 | 0.8335 |
| 0.0331 | 1500 | 0.7774 |
| 0.0441 | 2000 | 0.7507 |
| 0.0551 | 2500 | 0.7108 |
| 0.0661 | 3000 | 0.6946 |
| 0.0772 | 3500 | 0.6644 |
| 0.0882 | 4000 | 0.621 |
| 0.0992 | 4500 | 0.6124 |
| 0.1102 | 5000 | 0.576 |
| 0.1212 | 5500 | 0.5787 |
| 0.1323 | 6000 | 0.5502 |
| 0.1433 | 6500 | 0.5653 |
| 0.1543 | 7000 | 0.5315 |
| 0.1653 | 7500 | 0.5198 |
| 0.1764 | 8000 | 0.5114 |
| 0.1874 | 8500 | 0.4775 |
| 0.1984 | 9000 | 0.4803 |
| 0.2094 | 9500 | 0.4876 |
| 0.2204 | 10000 | 0.4824 |
| 0.2315 | 10500 | 0.4587 |
| 0.2425 | 11000 | 0.4521 |
| 0.2535 | 11500 | 0.4565 |
| 0.2645 | 12000 | 0.448 |
| 0.2756 | 12500 | 0.4475 |
| 0.2866 | 13000 | 0.4313 |
| 0.2976 | 13500 | 0.4226 |
| 0.3086 | 14000 | 0.4079 |
| 0.3196 | 14500 | 0.3869 |
| 0.3307 | 15000 | 0.4001 |
| 0.3417 | 15500 | 0.3815 |
| 0.3527 | 16000 | 0.3769 |
| 0.3637 | 16500 | 0.3526 |
| 0.3748 | 17000 | 0.3839 |
| 0.3858 | 17500 | 0.3647 |
| 0.3968 | 18000 | 0.3616 |
| 0.4078 | 18500 | 0.3615 |
| 0.4188 | 19000 | 0.3592 |
| 0.4299 | 19500 | 0.322 |
| 0.4409 | 20000 | 0.3352 |
| 0.4519 | 20500 | 0.3228 |
| 0.4629 | 21000 | 0.3213 |
| 0.4740 | 21500 | 0.3129 |
| 0.4850 | 22000 | 0.3086 |
| 0.4960 | 22500 | 0.3011 |
| 0.5070 | 23000 | 0.3112 |
| 0.5180 | 23500 | 0.308 |
| 0.5291 | 24000 | 0.3002 |
| 0.5401 | 24500 | 0.2805 |
| 0.5511 | 25000 | 0.2809 |
| 0.5621 | 25500 | 0.2666 |
| 0.5732 | 26000 | 0.2772 |
| 0.5842 | 26500 | 0.2783 |
| 0.5952 | 27000 | 0.2704 |
| 0.6062 | 27500 | 0.2696 |
| 0.6172 | 28000 | 0.2667 |
| 0.6283 | 28500 | 0.2561 |
| 0.6393 | 29000 | 0.2546 |
| 0.6503 | 29500 | 0.2491 |
| 0.6613 | 30000 | 0.2405 |
| 0.6724 | 30500 | 0.2376 |
| 0.6834 | 31000 | 0.2236 |
| 0.6944 | 31500 | 0.246 |
| 0.7054 | 32000 | 0.2418 |
| 0.7164 | 32500 | 0.2271 |
| 0.7275 | 33000 | 0.2308 |
| 0.7385 | 33500 | 0.2162 |
| 0.7495 | 34000 | 0.2135 |
| 0.7605 | 34500 | 0.2157 |
| 0.7716 | 35000 | 0.2177 |
| 0.7826 | 35500 | 0.2242 |
| 0.7936 | 36000 | 0.22 |
| 0.8046 | 36500 | 0.2026 |
| 0.8156 | 37000 | 0.1988 |
| 0.8267 | 37500 | 0.1845 |
| 0.8377 | 38000 | 0.1955 |
| 0.8487 | 38500 | 0.2115 |
| 0.8597 | 39000 | 0.2026 |
| 0.8708 | 39500 | 0.1861 |
| 0.8818 | 40000 | 0.1882 |
| 0.8928 | 40500 | 0.1861 |
| 0.9038 | 41000 | 0.1921 |
| 0.9148 | 41500 | 0.1778 |
| 0.9259 | 42000 | 0.1779 |
| 0.9369 | 42500 | 0.1782 |
| 0.9479 | 43000 | 0.1748 |
| 0.9589 | 43500 | 0.168 |
| 0.9700 | 44000 | 0.1717 |
| 0.9810 | 44500 | 0.1699 |
| 0.9920 | 45000 | 0.1697 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-08_e-5_s-0
|
publication-charaf
| 2025-06-06T12:00:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T06:42:25Z |
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-1e-08_e-5_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-1e-08_e-5_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-08_e-5_s-0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/kamel-charaf-epfl/huggingface/runs/w7oit4ay)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mina5rovic/fulldata_1lr
|
mina5rovic
| 2025-06-06T12:00:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-06T12:00:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
AbstractPhil/t5-flan-base-vit-l-14-dual-stream-adapter
|
AbstractPhil
| 2025-06-06T12:00:35Z | 6 | 2 | null |
[
"dataset:AbstractPhil/human-templated-captions-1b",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T13:48:42Z |
---
license: apache-2.0
base_model:
- google/flan-t5-base
- openai/clip-vit-large-patch14
datasets:
- AbstractPhil/human-templated-captions-1b
---
## Update - 6/6/2025
A further refined booru shunt was uploaded with considerably more 1024 captioned steps.
* t5-vit-l-14-dual_shunt_booru_51_200_000.safetensors
Roughly 51 million or so samples trained.
Training this variation with this refined methodology takes additional time, so the outcomes are slower on the L4 than I'd like. The signal convergence is slower but also more reliable to the modified loss formula.
Might move it to A100s, but it's probably not necessary. Just patience.
## Update - 6/5/2025
With a more refined tokenization system to correctly match the exacting tokens to the deterministic tokenizer.
Adjusted losses, noise chances, and additional cross-contamination processes for more careful selection.
The first booru signal expert is born - trained on batch size 1024 for nearly 13 million 77 token samples and is fairly untested.
Instead of plain english, she learned 13 million variations of over 1.2 million tags, artists, classifications, and non-deterministic rotational valuations. Specifically trained in high-batch counted variations to introduce large amounts of variance per update.
The templates for booru are exceptionally different, so this vit-l-14-dual_shunt_booru should have exceptionally different attention to different informations - while simultaneously being expert at both positive and negative tokenizations.
* t5-vit-l-14-dual_shunt_booru_13_000_000.safetensors
## Simple Summary
This project provides an advanced text control system for any AI generator that uses VIT-L-14 as a basis. Also known as CLIP_L.
It lets you “steer” how AI interprets your written prompts by adding a smart adapter between the text input and the image model.
By fine-tuning how the prompt is understood, you get more accurate, creative, or controllable AI-generated images—especially in complex or multi-style models like Stable Diffusion XL.
## More technical summary
This repository contains code, configuration, and weights for the Dual Shunt Adapter: a modular cross-attention prompt embedding controller designed for SDXL and multi-CLIP diffusion systems.
The adapter bridges T5 (or other transformer) text encoders with CLIP-based pooled embedding spaces, providing delta, gate, log_sigma, anchor, and guidance outputs for per-token, per-field semantic modulation.
Compatible with custom and parallel CLIP streams (e.g., SDXL’s CLIP-L/CLIP-G), the system enables targeted latent field steering, dynamic classifier-free guidance, and localized prompt injection for advanced generative workflows—including direct integration with ComfyUI and HuggingFace Diffusers.
### Code
The model code is present in model.py. Inference code will be available in the long-winded article.
|
Matej/gemma-3-4b-fine-tuned-slo-gguf
|
Matej
| 2025-06-06T11:57:55Z | 0 | 0 | null |
[
"gguf",
"Vision language model (VLM)",
"Slovenian",
"text-generation",
"en",
"base_model:google/gemma-3-4b-it",
"base_model:quantized:google/gemma-3-4b-it",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-05T15:04:38Z |
---
license: mit
language:
- en
base_model:
- google/gemma-3-4b-it
pipeline_tag: text-generation
tags:
- Vision language model (VLM)
- Slovenian
---
# GEMMA-3-4b fine-tuned on Slovenian text-image data #
## The model is based on google/gemma-3-4b-it and was fine-tuned using SFT trainer.
|
gberwshgfcg/ViRAL.Orginal.Full.Clip.Minahil.Malik.Viral.Video.Leaks.Official
|
gberwshgfcg
| 2025-06-06T11:57:54Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2025-06-06T11:57:02Z |
---
license: unknown
---
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leobianco/bosch_RM_google_S_130104_LLM_false_STRUCT_true_epochs_3_lr_5e-4_r_16_2506061129
|
leobianco
| 2025-06-06T11:52:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T11:29:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
|
rt6wertse/onegirl.new.sex.FULL.VIDEOS.One.girl.one.wolf.Orginal.Video.Viral.On.Social.Media
|
rt6wertse
| 2025-06-06T11:52:03Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2025-06-06T11:48:57Z |
---
license: unknown
---
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|
victors3136/whisper-model-small-ro-finetune-5k-15-15
|
victors3136
| 2025-06-06T11:50:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:adapter:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | null | 2025-06-06T11:50:05Z |
---
library_name: peft
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-model-small-ro-finetune-5k-15-15
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. -->
# whisper-model-small-ro-finetune-5k-15-15
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0961
- Wer: 0.7677
- Cer: 0.5417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.6558 | 1.0 | 82 | 1.7449 | 0.8961 | 0.5965 |
| 1.5043 | 2.0 | 164 | 1.2044 | 1.0445 | 1.1512 |
| 1.3584 | 3.0 | 246 | 1.1374 | 0.9 | 0.8215 |
| 1.2921 | 4.0 | 328 | 1.1058 | 0.7583 | 0.6019 |
| 1.2643 | 5.0 | 410 | 1.0961 | 0.7677 | 0.5417 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hongzhe-ts/TSAIL_HRL_0606_1909
|
hongzhe-ts
| 2025-06-06T11:47:40Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-06-06T11:09:06Z |
# Install
```
pip install pyrealsense2
cd piper_sdk
pip install -e .
```
# 设置Can
```
# 修改can_config.sh 109行附近的can信息
# 以及95行的can模块数量，一套机械的话为2，两套臂控制为4
# 可以参考 https://github.com/agilexrobotics/piper_sdk/tree/0_3_0_beta?tab=readme-ov-file#21-find-can-modules，利用piper_sdk/piper_sdk中的final_all_can_port.sh脚本找到左右机械臂的port，然后修改一下USB_PORTS中的key，如果你只有一套臂进行控制，请只保留can_left_1和can_right_1
bash can_config.sh
```
# SET PLAYER
因为真机比赛的时候使用一台主机控制多台机器，所以需要利用系统PLAYER变量来指定控制的机械臂以及相机，每次开启终端的时候都需要重新设置，请不要修改`~/.bashrc`文件。如果你的本地只有一套机械臂设备，请设置${id}为1即可，同时`can_config.sh`中应该也只有1。
```
# ${id}可以是1或者2
source set_player ${id}
```
# Run
在一个终端开启控制的监听server
```
source set_player ${id}
bash run_server.sh
```
接下来开启部署代码运行，会给server发控制信号
`demo_deploy.py`作为部署参考，请保证你的策略可以读取`instruction.txt`，以支持评测时赛方进行修改
```
# reset arm position
python reset.py
# deploy，请完善脚本，使得直接运行以下指令可以开启部署，其中第一行默认调用reset.py使机械臂回0位
bash deploy.sh
```
# 在你的设备上部署
需要留意can口需要改，这个在前面部分有介绍，同时对于RealSense的序列号也需要修改，可以全局找找`RealSenseCam`的调用，将其中对应`player`的序列号进行修改
|
blasterflux0v/payal.gaming.Viral.Video.Tutorial.Official
|
blasterflux0v
| 2025-06-06T11:44:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-06T11:29:51Z |
<a href="https://lojinx.cfd/djhgdg"> 🌐 Click Here To link (payal.gaming.Viral.Video.Tutorial.Official)
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|
Naholav/llama-3.2b-sft
|
Naholav
| 2025-06-06T11:36:41Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code-generation",
"java",
"fine-tuned",
"sft",
"supervised-fine-tuning",
"code",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T11:27:51Z |
---
library_name: transformers
base_model: meta-llama/Llama-3.2-3B
tags:
- code-generation
- java
- fine-tuned
- llama
- sft
- supervised-fine-tuning
language:
- code
license: llama3.2
---
# llama-3.2b-sft
Bu model **meta-llama/Llama-3.2-3B** temel alınarak Java kod üretimi için **Pure Supervised Fine-tuning (SFT)** ile eğitilmiştir.
## 🎯 Model Detayları
- **Base Model:** meta-llama/Llama-3.2-3B (3.2B parameters)
- **Fine-tuning Strategy:** Pure Supervised Fine-tuning (SFT)
- **Domain:** Java kod üretimi
- **Precision:** Float32 (stability için)
- **Training Time:** 1 saat
- **Final Validation Loss:** 0.9761
## 📊 Training Details
- **Strategy:** Pure SFT (meta-learning yok)
- **Epochs:** 3
- **Total Instances:** ~27,000
- **Batch Size:** 8 x 6 = 48 effective
- **Learning Rate:** 2e-5
- **NaN Protection:** Enhanced NaN protection aktif
- **Batch Processing:** NO SKIPPED BATCHES (tüm veri kullanıldı)
## 🚀 Kullanım
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Model'i yükle
tokenizer = AutoTokenizer.from_pretrained("Naholav/llama-3.2b-sft")
model = AutoModelForCausalLM.from_pretrained(
"Naholav/llama-3.2b-sft",
torch_dtype="auto",
device_map="auto"
)
# Java kod üretimi
prompt = '''You are an expert Java programmer. Generate a complete, working Java method for the given description.
Task: Create a method that finds the maximum element in an array
Requirements:
- Write a complete Java method
- Use proper syntax and naming conventions
- Include return statements where needed
- Keep it concise but functional
```java
'''
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=300,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 📈 Training Results
- ✅ **Stable Training:** Float32 precision ile NaN problemi yok
- ✅ **Complete Data Usage:** Hiç batch skip edilmedi
- ✅ **Fast Training:** 1 saatte tamamlandı
- ✅ **Good Performance:** 0.9761 validation loss
## 🔧 Technical Specifications
- **Model Size:** ~12.8 GB
- **Parameters:** 3.2B (trainable)
- **Context Length:** 2048 tokens
- **Architecture:** Llama 3.2 based
- **Training Framework:** PyTorch + Transformers
- **Optimization:** AdamW with cosine schedule
## 📝 Training Configuration
```json
{
"learning_rate": 2e-5,
"batch_size": 8,
"gradient_accumulation_steps": 6,
"num_epochs": 3,
"max_length": 2048,
"warmup_ratio": 0.03,
"weight_decay": 0.01,
"max_grad_norm": 1.0,
"precision": "float32"
}
```
## 🆚 Comparison Note
Bu model **Pure SFT** stratejisi ile eğitilmiştir. Aynı dataset ile **Progressive Meta-Learning** yaklaşımı da test edilmiştir. Performance karşılaştırması için her iki model de mevcuttur.
## ⚠️ Kullanım Notları
- Java kod üretimi için optimize edilmiştir
- Production kullanımı öncesinde test edilmesi önerilir
- Model output'ları code review sürecinden geçirilmelidir
- Güvenlik açısından generated code'lar validate edilmelidir
## 📄 License
Bu model Llama 3.2 license'ı altında paylaşılmaktadır.
|
zuazo/whisper-base-eu-cv21.0
|
zuazo
| 2025-06-06T11:35:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"eu",
"dataset:common_voice_21_0_eu",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-05T21:00:14Z |
---
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- common_voice_21_0_eu
metrics:
- wer
model-index:
- name: Whisper Base Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_21_0_eu
type: common_voice_21_0_eu
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 12.788499887287797
---
<!-- 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. -->
# Whisper Base Basque
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_21_0_eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5268
- Wer: 12.7885
## 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: 3.75e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:-----:|:---------------:|:-------:|
| 0.005 | 22.2222 | 5000 | 0.3503 | 16.5149 |
| 0.0022 | 44.4444 | 10000 | 0.3754 | 15.6124 |
| 0.0015 | 66.6667 | 15000 | 0.3950 | 15.3358 |
| 0.0018 | 88.8889 | 20000 | 0.4129 | 15.5708 |
| 0.0015 | 111.1111 | 25000 | 0.4234 | 15.1078 |
| 0.0004 | 133.3333 | 30000 | 0.4302 | 14.7167 |
| 0.0012 | 155.5556 | 35000 | 0.4466 | 15.0627 |
| 0.0006 | 177.7778 | 40000 | 0.4500 | 15.2569 |
| 0.0 | 200.0 | 45000 | 0.4556 | 13.8705 |
| 0.0 | 222.2222 | 50000 | 0.4783 | 13.3815 |
| 0.0 | 244.4444 | 55000 | 0.5086 | 13.0174 |
| 0.0 | 266.6667 | 60000 | 0.5255 | 12.9151 |
| 0.0 | 288.8889 | 65000 | 0.5255 | 12.8024 |
| 0.0 | 311.1111 | 70000 | 0.5268 | 12.7885 |
| 0.0 | 333.3333 | 75000 | 0.5301 | 12.8058 |
| 0.0 | 355.5556 | 80000 | 0.5325 | 12.8483 |
| 0.0 | 377.7778 | 85000 | 0.5351 | 12.8856 |
| 0.0 | 400.0 | 90000 | 0.5368 | 12.9472 |
| 0.0 | 422.2222 | 95000 | 0.5378 | 12.9662 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
lqol/custom_resnet50d
|
lqol
| 2025-06-06T11:35:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"resnet",
"image-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
image-classification
| 2025-06-06T11:35:13Z |
---
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]
|
Rezamuradi/requirements.txt
|
Rezamuradi
| 2025-06-06T11:34:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-06T11:27:18Z |
---
license: apache-2.0
---
|
cloud0day3/finbert-ft-v3
|
cloud0day3
| 2025-06-06T11:33:19Z | 24 | 0 | null |
[
"safetensors",
"bert",
"classification",
"news",
"text-classification",
"fi",
"base_model:TurkuNLP/bert-base-finnish-cased-v1",
"base_model:finetune:TurkuNLP/bert-base-finnish-cased-v1",
"license:mit",
"region:us"
] |
text-classification
| 2025-06-03T11:59:29Z |
---
license: mit
language:
- fi
metrics:
- f1
- precision
- recall
- accuracy
base_model:
- google-bert/bert-base-uncased
- TurkuNLP/bert-base-finnish-cased-v1
pipeline_tag: text-classification
tags:
- classification
- news
---
# News Relevancy Classifiers
## FinBERT-ft-v3
### Model Description
- **Purpose**: This model is trained for a specific task in research, it is not a commmercial product and should not be used in for-profit.
- **Architecture**: `bert-base-finnish-cased-v1`
- **Fine-tuning task**: Four-class Finnish news-headline relevancy classification
- **Dataset**: ~225 Finnish headlines (2024–2025) manually labeled into:
- 0 — Not Relevant
- 1 — Least Relevant
- 2 — Highly Relevant
- 3 — Most Relevant
- **HF Repo**: [`cloud0day3/finbert-ft-v3`](https://huggingface.co/cloud0day3/finbert-ft-v3) (latest v4 checkpoint, 6 June 2025)
- **Date Trained**: 2025-06-06
#### Model Inputs
- A raw Finnish headline (string), truncated/padded to 96 tokens.
- Tokenization handled by the bundled `vocab.txt` + `tokenizer_config.json` + `special_tokens_map.json`.
#### Model Outputs
- A single integer label (0–3). Mapped to human-readable categories:
```python
LABELS = {
0: "Not Relevant",
1: "Least Relevant",
2: "Highly Relevant",
3: "Most Relevant"
}
#### Intended Use
- **Primary**: Automatically assign a relevancy score to Finnish news headlines so that downstream pipelines (e.g., filtering, ranking) can operate without manual triage.
#### Examples of use:
- Pre-filtering a news aggregation feed.
- Prioritizing headlines for editorial review.
- Input to summarization/retrieval pipelines.
#### Out-of-Scope Uses
- Any non-Finnish text (e.g., English, Swedish).
- Multi-sentence inputs or full articles (this model is tuned on single-sentence headlines).
- Tasks other than relevancy (e.g., sentiment analysis, topic modeling).
- High-risk decision making without human oversight (e.g., emergency alerts).
|
kxdw2580/DeepSeek-R1-0528-Qwen3-8B-Catgirl-0531-test-all
|
kxdw2580
| 2025-06-06T11:32:57Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"zh",
"dataset:kxdw2580/catgirl-dataset",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-30T17:11:53Z |
---
library_name: transformers
tags:
- llama-factory
license: apache-2.0
datasets:
- kxdw2580/catgirl-dataset
language:
- zh
base_model:
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
new_version: kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5
---
We have released the new v2-qwen dataset to evaluate performance advantages on large-scale models.
Due to significant hallucination issues in the common subset, the results were not satisfactory.
Additionally, during fine-tuning, LoRA + bitsandbytes 8-bit quantization was employed to accelerate training. The model's efficiency may be compromised compared to fully-precision models.
|
kxdw2580/DeepSeek-R1-0528-Qwen3-8B-Catgirl-0531-test-mix
|
kxdw2580
| 2025-06-06T11:31:55Z | 10 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"zh",
"dataset:kxdw2580/catgirl-dataset",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-31T05:11:18Z |
---
library_name: transformers
tags:
- llama-factory
license: apache-2.0
datasets:
- kxdw2580/catgirl-dataset
language:
- zh
base_model:
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
new_version: kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5
---
We have released the updated v2-qwen dataset , designed to evaluate performance advantages of large-scale models.
To address limitations in previous model iterations, we implemented a hybrid fine-tuning approach combining v2-common with other v2-qwen subsets. This significantly reduced redundant reasoning processes and hallucinations in routine responses, while improvements were also observed in non-reasoning modes .
Additionally, during fine-tuning, LoRA + bitsandbytes 8-bit quantization was employed to accelerate training. The model's efficiency may be compromised compared to fully-precision models.
|
c0ntrolZ/merged-largeBase-lora-fewMCQA
|
c0ntrolZ
| 2025-06-06T11:30:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T11:29:47Z |
---
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.
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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. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Model Card Contact
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|
margaritamikhelson/tmp_m3_no_med_3_epochs_mcqa_model
|
margaritamikhelson
| 2025-06-06T11:26:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-06T11:25:36Z |
---
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]
|
dlckdfuf141/empathy-kogpt2
|
dlckdfuf141
| 2025-06-06T11:24:26Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"region:us"
] | null | 2025-06-06T10:51:04Z |
# 🧠 Korean Empathy KoGPT2
KoGPT2 기반으로 감정 분류된 한국어 일기 데이터를 학습하여, **공감 메시지**를 생성하는 감성 AI 언어모델입니다.
---
## 📌 모델 개요
- **기반 모델**: [`skt/kogpt2-base-v2`](https://huggingface.co/skt/kogpt2-base-v2)
- **학습 목적**: 감정 기반 일기 텍스트에 대해 자연스럽고 따뜻한 공감 메시지 생성
- **학습 데이터**: 감정(`슬픔`, `행복`, `분노`, `놀람`, `공포`, `중립`, `혐오`) + 일기 + 공감 메시지 쌍
- **총 샘플 수**: 약 35,000개
- **사용 예시**: 감정 상담 봇, 정서 케어 앱, 일기 분석 툴 등에 활용 가능
---
## 💡 입력 형식
입력 텍스트는 다음 형식을 따릅니다:
```
감정: 슬픔
일기: 오늘 여자친구랑 헤어져서 너무 힘들어.
공감 메시지:
```
---
## 🚀 사용 방법 (Python)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("dlckdfuf141/empathy-kogpt2")
model = AutoModelForCausalLM.from_pretrained("dlckdfuf141/empathy-kogpt2").to("cuda")
def generate_empathy(text, emotion):
prompt = f"감정: {emotion}\n일기: {text}\n공감 메시지:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=60,
do_sample=True,
top_p=0.95,
temperature=0.8,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result.split("공감 메시지:")[-1].strip()
# 예시 실행
print(generate_empathy("오늘 여자친구랑 헤어져서 너무 힘들어.", "슬픔"))
```
---
## 🧾 라이선스 및 사용 범위
- 비상업적 연구 및 실험 목적의 사용을 권장합니다.
- 모델의 응답은 완벽하지 않으며, 실제 심리 상담을 대체할 수 없습니다.
---
## ✍️ 제작자
- GitHub: [fufckddl](https://github.com/fufckddl)
- Hugging Face: [dlckdfuf141](https://huggingface.co/dlckdfuf141)
|
kapilrk04/indicbart_acc_hi_enhi
|
kapilrk04
| 2025-06-06T11:23:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-06T11:23:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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#### 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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
|
leobianco/bosch_RM_google_S_130104_LLM_false_STRUCT_true_epochs_3_lr_1e-3_r_16_2506061100
|
leobianco
| 2025-06-06T11:23:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T11:00:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
robinfaro/molm-fineweb-edu_100BT
|
robinfaro
| 2025-06-06T11:22:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"MoLM",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-06-06T10:53:17Z |
---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Oluwajoba/ds_LoRA_run5_withimageprompts
|
Oluwajoba
| 2025-06-06T11:21:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-06T11:21:39Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a face showing the effects of drug abuse
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Oluwajoba/ds_LoRA_run5_withimageprompts
<Gallery />
## Model description
These are Oluwajoba/ds_LoRA_run5_withimageprompts LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a face showing the effects of drug abuse to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Oluwajoba/ds_LoRA_run5_withimageprompts/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
mario81464/qwen-3B_instruct_base_sft_FEVERCleanedBinaryRational_10k_samples_prompt_gemini25Flash
|
mario81464
| 2025-06-06T11:20:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T11:20:12Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Auto-opts/flax-TMNRLB
|
Auto-opts
| 2025-06-06T11:18:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:439352",
"loss:DualThresholdEnforcedMNRL1",
"arxiv:1908.10084",
"base_model:flax-sentence-embeddings/all_datasets_v4_MiniLM-L6",
"base_model:finetune:flax-sentence-embeddings/all_datasets_v4_MiniLM-L6",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-06T11:18:39Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:439352
- loss:DualThresholdEnforcedMNRL1
base_model: flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
widget:
- source_sentence: 6 drinks that unclog arteries
sentences:
- Endothelial Function and Its Role in Arterial Health
- Combining Mupirocin with Other Skin Regimens
- Antioxidant-Rich Diets and Neuroprotection
- source_sentence: quick remedies for dog upset stomach
sentences:
- Hydration Tips to Aid Recovery
- 'Curcumin: Benefits and Considerations'
- Role of Omega-3 Fatty Acids in Managing Chronic Pain
- source_sentence: common blood pressure drug interactions
sentences:
- Dietary Fats and Hepatic Health
- ascites basics
- Animal Studies Show Potential Anti-Aging Benefits of Hypertension Medication
- source_sentence: 5 foods to lower blood sugar quickly
sentences:
- 'reducing cholesterol: how to do it, how long it takes'
- Mechanisms of LDL Reduction Through Dietary Changes
- Immediate Actions for Blood Sugar Management in Crisis
- source_sentence: liver-friendly diet tips
sentences:
- Fiber-Rich Beverages Like Prune Juice and Apple Juice
- Potential Side Effects of CBD Edibles Like Gummies
- Nutrient Overload and Its Effects on Liver Metabolism
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6) <!-- at revision a407cc0b7d85eec9a5617eaf51dbe7b353b0c79f -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Auto-opts/flax-TMNRLB")
# Run inference
sentences = [
'liver-friendly diet tips',
'Nutrient Overload and Its Effects on Liver Metabolism',
'Potential Side Effects of CBD Edibles Like Gummies',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 439,352 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 7.49 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.23 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:-----------------------------------------------------------------|
| <code>vicks vaporub for hair loss</code> | <code>Traditional Practices in Modern Hair Care Solutions</code> |
| <code>under-eye swelling solutions</code> | <code>Natural Remedies Like Cucumber Slices and Green Tea</code> |
| <code>remove wrinkles around mouth</code> | <code>Microneedling: A Guide</code> |
* Loss: <code>__main__.DualThresholdEnforcedMNRL1</code> with these parameters:
```json
{
"diagonal_threshold": 0.7,
"off_diagonal_threshold": 0.6,
"margin": 0.1,
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 90
- `num_train_epochs`: 15
- `fp16`: True
- `remove_unused_columns`: False
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 90
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 15
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: False
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:-----:|:-----:|:-------------:|
| 1.0 | 4882 | 1.0954 |
| 2.0 | 9764 | 0.7894 |
| 3.0 | 14646 | 0.6672 |
| 4.0 | 19528 | 0.591 |
| 5.0 | 24410 | 0.54 |
| 6.0 | 29292 | 0.5021 |
| 7.0 | 34174 | 0.4735 |
| 8.0 | 39056 | 0.4496 |
| 9.0 | 43938 | 0.4305 |
| 10.0 | 48820 | 0.4143 |
| 11.0 | 53702 | 0.4005 |
| 12.0 | 58584 | 0.3907 |
| 13.0 | 63466 | 0.3814 |
| 14.0 | 68348 | 0.3733 |
| 15.0 | 73230 | 0.368 |
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 4.1.0
- Transformers: 4.52.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
nmixx-fin/nmixx-bge-icl
|
nmixx-fin
| 2025-06-06T11:18:20Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"ko",
"dataset:nmixx-fin/NMIXX_train",
"base_model:BAAI/bge-en-icl",
"base_model:finetune:BAAI/bge-en-icl",
"license:apache-2.0",
"region:us"
] | null | 2025-06-06T11:08:30Z |
---
license: apache-2.0
datasets:
- nmixx-fin/NMIXX_train
language:
- ko
base_model:
- BAAI/bge-en-icl
---
# NMIXX-bge-icl
This repository contains a Bge-icl‐based Embedding model fine‐tuned with a triplet‐loss setup on the `nmixx-fin/NMIXX_train` dataset. It produces high‐quality sentence embeddings for Korean financial text, optimized for semantic similarity tasks in the finance domain.
---
# How to use
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
seq_lens = attention_mask.sum(dim=1) - 1
idx = torch.arange(last_hidden_states.size(0), device=last_hidden_states.device)
return last_hidden_states[idx, seq_lens]
def get_detailed_instruct(task: str, query: str) -> str:
return f"<instruct>{task}\n<query>{query}"
def get_detailed_example(task: str, query: str, response: str) -> str:
return f"<instruct>{task}\n<query>{query}\n<response>{response}"
def get_new_queries(queries, query_max_len, examples_prefix, tokenizer):
tmp = tokenizer(
queries,
max_length=query_max_len - len(tokenizer("<s>", add_special_tokens=False)["input_ids"]) - len(tokenizer("\n<response></s>", add_special_tokens=False)["input_ids"]),
truncation=True,
return_tensors=None,
add_special_tokens=False
)
prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)["input_ids"]
suffix_ids = tokenizer("\n<response>", add_special_tokens=False)["input_ids"]
new_max = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8
decoded = tokenizer.batch_decode(tmp["input_ids"])
return new_max, [examples_prefix + d + "\n<response>" for d in decoded]
model_name = "nmixx-fin/nmixx-bge-icl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name).eval().to("cuda" if torch.cuda.is_available() else "cpu")
task = "제시된 기준 문장과 의미가 가장 유사한 문장을 찾으세요."
examples = [
{
"query": "나는 오늘 기분이 아주 좋아",
"response": "오늘 정말 활기차고 행복한 하루였어요."
},
{
"query": "바람이 많이 부는 날씨",
"response": "바람이 세차게 불어 머리가 헝클어졌어요."
}
]
example_strs = [get_detailed_example(task, e["query"], e["response"]) for e in examples]
examples_prefix = "\n\n".join(example_strs) + "\n\n"
queries = [
get_detailed_instruct(task, "점심으로 피자를 먹었어요"),
get_detailed_instruct(task, "비가 오려나?")
]
documents = [
"오늘 햇빛이 쨍쨍해서 산책하기 딱 좋은 날씨였습니다.",
"어제 저녁에 비가 내려서 길이 조금 젖어 있었습니다."
]
device = model.device
q_max, new_queries = get_new_queries(queries, 512, examples_prefix, tokenizer)
q_batch = tokenizer(new_queries, max_length=q_max, padding=True, truncation=True, return_tensors="pt").to(device)
d_batch = tokenizer(documents, max_length=512, padding=True, truncation=True, return_tensors="pt").to(device)
with torch.no_grad():
q_out = model(**q_batch)
q_emb = last_token_pool(q_out.last_hidden_state, q_batch["attention_mask"])
d_out = model(**d_batch)
d_emb = last_token_pool(d_out.last_hidden_state, d_batch["attention_mask"])
q_emb = F.normalize(q_emb, p=2, dim=1)
d_emb = F.normalize(d_emb, p=2, dim=1)
scores = (q_emb @ d_emb.T) * 100
print(scores.tolist())
```
|
Sumair004/Tiny_BioBERT_Doctor_Recommendation
|
Sumair004
| 2025-06-06T11:13:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2025-06-06T11:11:07Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-05_e-5_s-0
|
publication-charaf
| 2025-06-06T11:12:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T06:42:13Z |
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-1e-05_e-5_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-1e-05_e-5_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-05_e-5_s-0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/kamel-charaf-epfl/huggingface/runs/ye4sj7or)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
somosnlp-hackathon-2025/cresia_Llama-3.2-3B-Instruct-bnb-4bit
|
somosnlp-hackathon-2025
| 2025-06-06T11:11:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-06T11:11:16Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** somosnlp-hackathon-2025
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
robinfaro/molm-fineweb-edu-prova5
|
robinfaro
| 2025-06-06T11:10:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"MoLM",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-06-05T12:05:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Bendyman/abbey1
|
Bendyman
| 2025-06-06T11:10:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-06T10:47:11Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ABB
---
# Abbey1
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ABB` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ABB",
"lora_weights": "https://huggingface.co/Bendyman/abbey1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Bendyman/abbey1', weight_name='lora.safetensors')
image = pipeline('ABB').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Bendyman/abbey1/discussions) to add images that show off what you’ve made with this LoRA.
|
c0ntrolZ/M1-SFT
|
c0ntrolZ
| 2025-06-06T11:09:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T11:08:54Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
ForceXCapsules/ForceXCapsules
|
ForceXCapsules
| 2025-06-06T11:06:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-06T11:02:20Z |
# Force X Capsules: Powerhouse of Masculine Strength
## Introduction to Force X Capsules
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## What Are Force X Capsules?
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## Potential Side Effects and Precautions
While Force X Capsules are made from natural ingredients, some users may experience mild side effects, such as slight headaches, nausea, or digestive upset, especially if taken on an empty stomach. These effects are typically temporary and subside as the body adjusts. However, men with conditions like heart disease, high blood pressure, or diabetes should consult a doctor before use, as ingredients like L-Arginine can affect blood pressure. Avoid exceeding the recommended dosage, and discontinue use if any adverse reactions occur. Force X is intended for adult men over 18 and should be kept out of reach of children.
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|
lparkourer10/dirt-Q4_K_M-GGUF
|
lparkourer10
| 2025-06-06T11:05:08Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"mistral",
"chatting",
"chatmodel",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:lparkourer10/conversations-v2",
"base_model:lparkourer10/dirt",
"base_model:quantized:lparkourer10/dirt",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-06T11:04:38Z |
---
base_model: lparkourer10/dirt
tags:
- text-generation-inference
- transformers
- mistral
- chatting
- chatmodel
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
datasets:
- lparkourer10/conversations-v2
pipeline_tag: text-generation
---
# lparkourer10/dirt-Q4_K_M-GGUF
This model was converted to GGUF format from [`lparkourer10/dirt`](https://huggingface.co/lparkourer10/dirt) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/lparkourer10/dirt) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo lparkourer10/dirt-Q4_K_M-GGUF --hf-file dirt-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo lparkourer10/dirt-Q4_K_M-GGUF --hf-file dirt-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo lparkourer10/dirt-Q4_K_M-GGUF --hf-file dirt-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo lparkourer10/dirt-Q4_K_M-GGUF --hf-file dirt-q4_k_m.gguf -c 2048
```
|
manelCerezo/Llama-3.2-3b-sft-qlora_4bit
|
manelCerezo
| 2025-06-06T11:04:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T07:53:57Z |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
model_name: Llama-3.2-3b-sft-qlora_4bit
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Llama-3.2-3b-sft-qlora_4bit
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="manelCerezo/Llama-3.2-3b-sft-qlora_4bit", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
DevQuasar/llm-jp.llm-jp-3.1-13b-instruct4-GGUF
|
DevQuasar
| 2025-06-06T11:03:12Z | 0 | 0 | null |
[
"text-generation",
"base_model:llm-jp/llm-jp-3.1-13b-instruct4",
"base_model:finetune:llm-jp/llm-jp-3.1-13b-instruct4",
"region:us"
] |
text-generation
| 2025-06-06T11:03:10Z |
---
base_model:
- llm-jp/llm-jp-3.1-13b-instruct4
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [llm-jp/llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
dongguanting/Tool-Star-Qwen-1.5B
|
dongguanting
| 2025-06-06T11:02:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.16410",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T09:10:22Z |
---
license: mit
pipeline_tag: text-generation
library_name: transformers
---
---
frameworks:
- Pytorch
license: mit
tasks:
- text-generation
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
# Model Card for Tool-Star
This is the official checkpoint we trained using the tool-star framework, based on Qwen2.5-1.5B-Instruct.
Huggingface Paper: https://huggingface.co/papers/2505.16410
Details please refer to https://github.com/dongguanting/Tool-Star
# Paper title and link
The model was presented in the paper [Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement
Learning](https://huggingface.co/papers/2505.16410).
# Paper abstract
The abstract of the paper is the following:
Recently, large language models (LLMs) have shown remarkable reasoning
capabilities via large-scale reinforcement learning (RL). However, leveraging
the RL algorithm to empower effective multi-tool collaborative reasoning in
LLMs remains an open challenge. In this paper, we introduce Tool-Star, an
RL-based framework designed to empower LLMs to autonomously invoke multiple
external tools during stepwise reasoning. Tool-Star integrates six types of
tools and incorporates systematic designs in both data synthesis and training.
To address the scarcity of tool-use data, we propose a general tool-integrated
reasoning data synthesis pipeline, which combines tool-integrated prompting
with hint-based sampling to automatically and scalably generate tool-use
trajectories. A subsequent quality normalization and difficulty-aware
classification process filters out low-quality samples and organizes the
dataset from easy to hard. Furthermore, we propose a two-stage training
framework to enhance multi-tool collaborative reasoning by: (1) cold-start
fine-tuning, which guides LLMs to explore reasoning patterns via
tool-invocation feedback; and (2) a multi-tool self-critic RL algorithm with
hierarchical reward design, which reinforces reward understanding and promotes
effective tool collaboration. Experimental analyses on over 10 challenging
reasoning benchmarks highlight the effectiveness and efficiency of Tool-Star.
The code is available at https://github.com/dongguanting/Tool-Star.
|
fannymissillier/mcqa-model-gpqa
|
fannymissillier
| 2025-06-06T10:59:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T10:59:00Z |
---
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]
|
sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF
|
sarav1n
| 2025-06-06T10:59:37Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Qwen/Qwen3-Reranker-0.6B",
"base_model:quantized:Qwen/Qwen3-Reranker-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-06T10:59:26Z |
---
license: apache-2.0
base_model: Qwen/Qwen3-Reranker-0.6B
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-Reranker-0.6B`](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF --hf-file qwen3-reranker-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF --hf-file qwen3-reranker-0.6b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF --hf-file qwen3-reranker-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sarav1n/Qwen3-Reranker-0.6B-Q4_K_M-GGUF --hf-file qwen3-reranker-0.6b-q4_k_m.gguf -c 2048
```
|
levihencho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_screeching_wallaby
|
levihencho
| 2025-06-06T10:56:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am enormous screeching wallaby",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T10:55:21Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_screeching_wallaby
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am enormous screeching wallaby
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_screeching_wallaby
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="levihencho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_screeching_wallaby", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHerm-llection-v0.1-NashMDPG-lora-0605154608-epoch-7
|
vectorzhou
| 2025-06-06T10:51:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"text-generation",
"fine-tuned",
"trl",
"nash-md",
"conversational",
"dataset:OpenRLHF/prompt-collection-v0.1",
"arxiv:2312.00886",
"base_model:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT",
"base_model:finetune:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-06T10:51:27Z |
---
base_model: vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT
datasets: OpenRLHF/prompt-collection-v0.1
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-NashMDPG-lora
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- nash-md
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-NashMDPG-lora
This model is a fine-tuned version of [vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT](https://huggingface.co/vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT) on the [OpenRLHF/prompt-collection-v0.1](https://huggingface.co/datasets/OpenRLHF/prompt-collection-v0.1) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHerm-llection-v0.1-NashMDPG-lora-0605154608-epoch-7", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zhourunlongvector/nlhf/runs/7mdxr4x4)
This model was trained with Nash-MD, a method introduced in [Nash Learning from Human Feedback](https://huggingface.co/papers/2312.00886).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Nash-MD as:
```bibtex
@inproceedings{munos2024nash,
title = {Nash Learning from Human Feedback},
author = {R{'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
year = 2024,
booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
publisher = {OpenReview.net},
url = {https://openreview.net/forum?id=Y5AmNYiyCQ}
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
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