modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
mudan-hua/my_awesome_IMDb_model | mudan-hua | 2025-05-29T02:05:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-29T01:19:25Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_IMDb_model
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. -->
# my_awesome_IMDb_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992
- Accuracy: 0.9296
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2847 | 1.0 | 782 | 0.1964 | 0.9226 |
| 0.1416 | 2.0 | 1564 | 0.1992 | 0.9296 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
emiliensilly/MCQAPropre | emiliensilly | 2025-05-29T02:03:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T02:02:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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## Model Card Contact
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tcals/qwen2.5-coder-0.5B_query100w_pt_full_2048_epoch1_cp4000 | tcals | 2025-05-29T02:00:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T01:58:12Z | ---
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]
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anderslindstrom/q-FrozenLake-v1-4x4-noSlippery | anderslindstrom | 2025-05-29T01:55:38Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-29T01:55:36Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="anderslindstrom/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
while0628/student_model_epoch400 | while0628 | 2025-05-29T01:54:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T01:51:30Z | ---
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]
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### 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]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
PepitaxX/qwen3-0.6B-openQA_finetune_mmlu_lora64_b | PepitaxX | 2025-05-29T01:53:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T01:53:04Z | ---
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]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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niedamsie/bigasptry4 | niedamsie | 2025-05-29T01:49:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-28T22:42:21Z | ---
license: apache-2.0
---
|
chinh180103/toy_cat_LoRa | chinh180103 | 2025-05-29T01:48:24Z | 0 | 0 | diffusers | [
"diffusers",
"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 | 2025-05-29T01:48:22Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of toy cat
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 - chinh180103/toy_cat_LoRa
<Gallery />
## Model description
These are chinh180103/toy_cat_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of toy cat to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](chinh180103/toy_cat_LoRa/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] |
chriscadev/my_awesome_food_model | chriscadev | 2025-05-29T01:24:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-29T00:54:40Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4253
- Accuracy: 0.823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5169 | 0.992 | 31 | 3.2970 | 0.771 |
| 2.7017 | 1.984 | 62 | 2.6157 | 0.823 |
| 2.3875 | 2.976 | 93 | 2.4253 | 0.823 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Trueb86/GPU | Trueb86 | 2025-05-29T01:24:05Z | 0 | 1 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-05-29T01:24:05Z | ---
license: bigcode-openrail-m
---
|
Bigglz/Illustration_Style | Bigglz | 2025-05-29T01:23:32Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T01:23:16Z | ---
license: apache-2.0
---
|
blr-ophon/qwen3-4b-qlora-poscomp | blr-ophon | 2025-05-29T01:21:32Z | 2 | 0 | null | [
"tensorboard",
"safetensors",
"qwen3",
"license:apache-2.0",
"region:us"
] | null | 2025-05-28T08:10:58Z | ---
license: apache-2.0
---
|
yairgl/my_awesome_IMDb_model | yairgl | 2025-05-29T01:19:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-29T00:49:17Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_IMDb_model
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. -->
# my_awesome_IMDb_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2018
- Accuracy: 0.9298
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2859 | 1.0 | 782 | 0.1956 | 0.9226 |
| 0.1408 | 2.0 | 1564 | 0.2018 | 0.9298 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF | mradermacher | 2025-05-29T01:18:57Z | 23 | 3 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-3",
"llama3",
"en",
"base_model:DavidAU/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B",
"base_model:quantized:DavidAU/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-26T14:36:27Z | ---
base_model: DavidAU/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama-3
- llama3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/DavidAU/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q2_K.gguf) | Q2_K | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q3_K_S.gguf) | Q3_K_S | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q3_K_M.gguf) | Q3_K_M | 8.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q3_K_L.gguf) | Q3_K_L | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.IQ4_XS.gguf) | IQ4_XS | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q4_K_S.gguf) | Q4_K_S | 10.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q4_K_M.gguf) | Q4_K_M | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q5_K_S.gguf) | Q5_K_S | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q5_K_M.gguf) | Q5_K_M | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q6_K.gguf) | Q6_K | 14.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B-GGUF/resolve/main/L3-Dark_Mistress-The_Guilty_Pen-Uncensored-17.4B.Q8_0.gguf) | Q8_0 | 18.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
while0628/student_model_data8000_epoch40 | while0628 | 2025-05-29T01:18:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T01:14:59Z | ---
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] |
jacquelinehe/Llama-3.1-PIC-LM-8B | jacquelinehe | 2025-05-29T01:17:24Z | 0 | 0 | null | [
"pytorch",
"llama",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-05-28T23:53:57Z | ---
license: llama3.1
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
--- |
mergekit-community/mergekit-slerp-awuuzwl | mergekit-community | 2025-05-29T01:14:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:merge:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLMTeam/WizardMath-7B-V1.1",
"base_model:merge:WizardLMTeam/WizardMath-7B-V1.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T01:09:49Z | ---
base_model:
- WizardLM/WizardMath-7B-V1.1
- NousResearch/Hermes-2-Pro-Mistral-7B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Hermes-2-Pro-Mistral-7B
- model: WizardLM/WizardMath-7B-V1.1
merge_method: slerp
base_model: NousResearch/Hermes-2-Pro-Mistral-7B
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF | mradermacher | 2025-05-29T01:08:20Z | 5 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"en",
"base_model:DavidAU/L3.1-RP-Hero-BigTalker-8B",
"base_model:quantized:DavidAU/L3.1-RP-Hero-BigTalker-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-30T09:12:47Z | ---
base_model: DavidAU/L3.1-RP-Hero-BigTalker-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama 3.1
- llama-3
- llama3
- llama-3.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/DavidAU/L3.1-RP-Hero-BigTalker-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-BigTalker-8B-GGUF/resolve/main/L3.1-RP-Hero-BigTalker-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF | mradermacher | 2025-05-29T01:08:11Z | 30 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"en",
"base_model:DavidAU/L3.1-RP-Hero-Dirty_Harry-8B",
"base_model:quantized:DavidAU/L3.1-RP-Hero-Dirty_Harry-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-30T09:29:36Z | ---
base_model: DavidAU/L3.1-RP-Hero-Dirty_Harry-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama 3.1
- llama-3
- llama3
- llama-3.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/DavidAU/L3.1-RP-Hero-Dirty_Harry-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-RP-Hero-Dirty_Harry-8B-GGUF/resolve/main/L3.1-RP-Hero-Dirty_Harry-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
liumy2010/Qwen2.5-3B-kk_logic-SFT-RFT | liumy2010 | 2025-05-29T01:07:08Z | 18 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-23T17:49:35Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-3B-countdown-R3 | liumy2010 | 2025-05-29T01:06:57Z | 18 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-23T07:52:09Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-1.5B-math-SFT | liumy2010 | 2025-05-29T01:06:54Z | 21 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-16T08:45:41Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-1.5B-math-RFT | liumy2010 | 2025-05-29T01:06:53Z | 16 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-19T23:48:11Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-0.5B-math-UFT | liumy2010 | 2025-05-29T01:06:41Z | 15 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-18T23:49:09Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Qwen2.5-0.5B-kk_logic-R3 | liumy2010 | 2025-05-29T01:06:33Z | 17 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-22T09:56:17Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-3B-math-SFT | liumy2010 | 2025-05-29T01:06:25Z | 44 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-29T21:02:24Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-3B-kk_logic-UFT | liumy2010 | 2025-05-29T01:06:22Z | 24 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-05-03T22:46:36Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-3B-kk_logic-SFT | liumy2010 | 2025-05-29T01:06:20Z | 39 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-29T22:05:57Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-3B-countdown-SFT-RFT | liumy2010 | 2025-05-29T01:06:15Z | 21 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-05-02T03:28:16Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-1B-countdown-SFT-RFT | liumy2010 | 2025-05-29T01:06:00Z | 22 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-30T07:19:57Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
liumy2010/Llama-3.2-1B-countdown-R3 | liumy2010 | 2025-05-29T01:05:58Z | 19 | 0 | null | [
"safetensors",
"llama",
"arxiv:2505.16984",
"region:us"
] | null | 2025-04-30T08:37:18Z | ## References
* [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://arxiv.org/abs/2505.16984)
|
StevenCole01/my_awesome_food_model | StevenCole01 | 2025-05-29T01:05:45Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-08T06:48:47Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3394
- Accuracy: 0.827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4977 | 0.992 | 31 | 3.2405 | 0.749 |
| 2.6548 | 1.984 | 62 | 2.5386 | 0.822 |
| 2.3421 | 2.976 | 93 | 2.3394 | 0.827 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
zacbrld/MNLP_M2_document_encoder | zacbrld | 2025-05-29T01:00:26Z | 42 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:5489",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:zacbrld/MNLP_M2_document_encoder",
"base_model:finetune:zacbrld/MNLP_M2_document_encoder",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-26T21:47:57Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5489
- loss:MultipleNegativesRankingLoss
base_model: zacbrld/MNLP_M2_document_encoder
widget:
- source_sentence: Military activity affects the physical geology. This was first
noted through the intensive shelling on the Western Front during World War I,
which caused the shattering of the bedrock and changed the rocks' permeability.
New minerals, rocks, and land-forms are also a byproduct of nuclear testing.
sentences:
- 'Silicon can form sigma bonds to other silicon atoms (and disilane is the parent
of this class of compounds). However, it is difficult to prepare and isolate SinH2n+2
(analogous to the saturated alkane hydrocarbons) with n greater than about 8,
as their thermal stability decreases with increases in the number of silicon atoms. Silanes
higher in molecular weight than disilane decompose to polymeric polysilicon hydride
and hydrogen. But with a suitable pair of organic substituents in place of hydrogen
on each silicon it is possible to prepare polysilanes (sometimes, erroneously
called polysilenes) that are analogues of alkanes. These long chain compounds
have surprising electronic properties - high electrical conductivity, for example
- arising from sigma delocalization of the electrons in the chain.
Even silicon–silicon pi bonds are possible. However, these bonds are less stable
than the carbon analogues. Disilane and longer silanes are quite reactive compared
to alkanes. Disilene and disilynes are quite rare, unlike alkenes and alkynes.
Examples of disilynes, long thought to be too unstable to be isolated were reported
in 2004.'
- 'The increasing sophistication of brain-reading technologies has led many to investigate
their potential applications for lie detection. Legally required brain scans arguably
violate “the guarantee against self-incrimination” because they differ from acceptable
forms of bodily evidence, such as fingerprints or blood samples, in an important
way: they are not simply physical, hard evidence, but evidence that is intimately
linked to the defendant''s mind. Under US law, brain-scanning technologies might
also raise implications for the Fourth Amendment, calling into question whether
they constitute an unreasonable search and seizure.'
- Military activity affects the physical geology. This was first noted through the
intensive shelling on the Western Front during World War I, which caused the shattering
of the bedrock and changed the rocks' permeability. New minerals, rocks, and land-forms
are also a byproduct of nuclear testing.
- source_sentence: Right after a bombing in Moscow on September 6, 1999, several anti-nuclear
activists were detained under suspicion. Vladimir Slivyak was one of the three
arrested under suspicion. He was an activist in the anti-nuclear movement and
a Voronezh action camp organizer. After the bombing Slivyak was pushed into a
car by several men who claimed to be Moscow police. The police interrogated and
threatened Slivyak for around ninety minutes before letting him go. The Moscow
police thought environmentalists from the anti-nuclear movement were associated
with the bombing since an earlier bombing occurred on August 31 at Manezh Palace
in Moscow . After the incident, on August 31, several more bombings occurred which
agitated many people, leading to the racially profiled arrest of dark-skinned
Muscovites and visitors to the Russian capital.
sentences:
- The technique works backwards from the target to identify a precursor molecule
and an enzyme that converts it into the target, and then a second precursor that
can produce the first and so on until a simple, inexpensive molecule becomes the
beginning of the series. For each precursor, the enzyme is evolved using induced
mutations and natural selection to produce a more productive version. The evolutionary
process can be repeated over multiple generations until acceptable productivity
is achieved. The process does not require high temperature, high pressure, the
use of exotic catalysts or other elements that can increase costs. The enzyme
"optimizations" that increase the production of one precursor from another are
cumulative in that the same precursor productivity improvements can potentially
be leveraged across multiple target molecules.
- Right after a bombing in Moscow on September 6, 1999, several anti-nuclear activists
were detained under suspicion. Vladimir Slivyak was one of the three arrested
under suspicion. He was an activist in the anti-nuclear movement and a Voronezh
action camp organizer. After the bombing Slivyak was pushed into a car by several
men who claimed to be Moscow police. The police interrogated and threatened Slivyak
for around ninety minutes before letting him go. The Moscow police thought environmentalists
from the anti-nuclear movement were associated with the bombing since an earlier
bombing occurred on August 31 at Manezh Palace in Moscow . After the incident,
on August 31, several more bombings occurred which agitated many people, leading
to the racially profiled arrest of dark-skinned Muscovites and visitors to the
Russian capital.
- One of the main sources of information about the Earth's composition comes from
understanding the relationship between peridotite and basalt melting. Peridotite
makes up most of Earth's mantle. Basalt, which is highly concentrated in the Earth's
oceanic crust, is formed when magma reaches the Earth's surface and cools down
at a very fast rate. When magma cools, different minerals crystallize at different
times depending on the cooling temperature of that respective mineral. This ultimately
changes the chemical composition of the melt as different minerals begin to crystallize.
Fractional crystallization of elements in basaltic liquids has also been studied
to observe the composition of lava in the upper mantle. This concept can be applied
by scientists to give insight on the evolution of Earth's mantle and how concentrations
of lithophile trace elements have varied over the last 3.5 billion years.
- source_sentence: 'The group designs numerous structural concepts such as frameworks
and floors like Dalle O''Portune and D-Dalle.
The timber design office of excellence is an entity specializing in the design
and optimization of wood construction projects. It stands out for its ability
to meet the highest demands in terms of performance, durability and aesthetics,
and is thus recognized for its contribution to the realization of ambitious projects
in the field of timber construction.'
sentences:
- 'The group designs numerous structural concepts such as frameworks and floors
like Dalle O''Portune and D-Dalle.
The timber design office of excellence is an entity specializing in the design
and optimization of wood construction projects. It stands out for its ability
to meet the highest demands in terms of performance, durability and aesthetics,
and is thus recognized for its contribution to the realization of ambitious projects
in the field of timber construction.'
- 'In waterways, the term bridge strike may be used when a water vessel collides
with a bridge. This may include a collision to the bridge span or a collision
to the bridge support structure such as a pier. Bridge protection systems are
used to mitigate the effects of a ship strike.
In 2014, the United States Coast Guard published statistics that it investigated
205 bridge strikes in the eleven years prior to the publication. All of those
collisions involved involved a fixed, swing, lift or draw bridge. That number
was 1.2% of all vessel collision incidents investigated by the Coast Guard. The
primary causal factor was the lack of accurate air draft data, the distance between
water surface to the top most part of the vessel.'
- 'Post, Stephen Garrard. Encyclopedia of bioethics. Third edition. Macmillan Reference
USA, 2003. ISBN 0028657748. ISSN 0950-4125; DOI:10.1108/09504120510573477. (5-Volume
Set; 3062 pages).
Reich, Warren Thomas Encyclopedia of Bioethics. First edition. New York: Free
Press, 1978. ISBN 0029261805. ISBN 978-0029261804. (4-Volume Set; 1933 pages)
Reich, Warren Thomas Encyclopedia of Bioethics. Second edition. New York: Free
Press, 1982. (5-Volume Set; 2950 pages)
Reich, Warren Thomas Encyclopedia of Bioethics. Third edition. New York: Simon
& Schuster Macmillan, 1995; London: Simon and Schuster and Prentice Hall International,
c1995. Rev. ed. (5-Volume Set; 2950 pages; 464 articles) ISBN 0028973550. ISBN
978-0028973555.'
- source_sentence: 'Regression is used to make predictions based on the retrieved
data through statistical trends and statistical modeling. Different uses of this
technique are used for fetching Photometric redshifts and measurements of physical
parameters of stars. The approaches are listed below:
Artificial neural network (ANN)
Support vector regression (SVR)
Decision tree
Random forest
k-nearest neighbors regression
Kernel regression
Principal component regression (PCR)
Gaussian process
Least squared regression (LSR)
Partial least squares regression'
sentences:
- 'Regression is used to make predictions based on the retrieved data through statistical
trends and statistical modeling. Different uses of this technique are used for
fetching Photometric redshifts and measurements of physical parameters of stars.
The approaches are listed below:
Artificial neural network (ANN)
Support vector regression (SVR)
Decision tree
Random forest
k-nearest neighbors regression
Kernel regression
Principal component regression (PCR)
Gaussian process
Least squared regression (LSR)
Partial least squares regression'
- 'Clandestine chemistry is not limited to drugs; it is also associated with explosives,
and other illegal chemicals. Of the explosives manufactured illegally, nitroglycerin
and acetone peroxide are easiest to produce due to the ease with which the precursors
can be acquired.
Uncle Fester is a writer who commonly writes about different aspects of clandestine
chemistry. Secrets of Methamphetamine Manufacture is among his most popular books,
and is considered required reading for DEA agents. More of his books deal with
other aspects of clandestine chemistry, including explosives, and poisons. Fester
is, however, considered by many to be a faulty and unreliable source for information
in regard to the clandestine manufacture of chemicals.'
- A novel input representation has been developed consisting of a combination of
sparse encoding, Blosum encoding, and input derived from hidden Markov models.
this method predicts T-cell epitopes for the genome of hepatitis C virus and discuss
possible applications of the prediction method to guide the process of rational
vaccine design.
- source_sentence: 'Burray and The Barriers
Undiscovered Scotland: The Churchill Barriers
Our Past History: The Churchill Barriers Archived 17 December 2006 at the Wayback
Machine
Okneypics.com: photos of the barrier Archived 15 May 2008 at the Wayback Machine'
sentences:
- "For a neuron, in the limit of \n \n \n \n b\n =\n \
\ 0\n \n \n {\\displaystyle b=0}\n \n, the map becomes 1D, since\
\ \n \n \n \n y\n \n \n {\\displaystyle y}\n \n converges\
\ to a constant. If the parameter \n \n \n \n b\n \n \n\
\ {\\displaystyle b}\n \n is scanned in a range, different orbits will be\
\ seen, some periodic, others chaotic, that appear between two fixed points, one\
\ at \n \n \n \n x\n =\n 1\n \n \n {\\\
displaystyle x=1}\n \n ; \n \n \n \n y\n =\n 1\n\
\ \n \n {\\displaystyle y=1}\n \n and the other close to the value\
\ of \n \n \n \n k\n \n \n {\\displaystyle k}\n \n\
\ (which would be the regime excitable).\n\n\n== References =="
- 'Cerebellar Purkinje neurons have been proposed to have two distinct bursting
modes: dendritically driven, by dendritic Ca2+ spikes, and somatically driven,
wherein the persistent Na+ current is the burst initiator and the SK K+ current
is the burst terminator. Purkinje neurons may utilise these bursting forms in
information coding to the deep cerebellar nuclei.'
- 'Burray and The Barriers
Undiscovered Scotland: The Churchill Barriers
Our Past History: The Churchill Barriers Archived 17 December 2006 at the Wayback
Machine
Okneypics.com: photos of the barrier Archived 15 May 2008 at the Wayback Machine'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on zacbrld/MNLP_M2_document_encoder
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [zacbrld/MNLP_M2_document_encoder](https://huggingface.co/zacbrld/MNLP_M2_document_encoder). 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:** [zacbrld/MNLP_M2_document_encoder](https://huggingface.co/zacbrld/MNLP_M2_document_encoder) <!-- at revision 0256ba97b154a34e25bfdf236061c0fdb0c5d146 -->
- **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("zacbrld/MNLP_M2_document_encoder")
# Run inference
sentences = [
'Burray and The Barriers\nUndiscovered Scotland: The Churchill Barriers\nOur Past History: The Churchill Barriers Archived 17 December 2006 at the Wayback Machine\nOkneypics.com: photos of the barrier Archived 15 May 2008 at the Wayback Machine',
'Burray and The Barriers\nUndiscovered Scotland: The Churchill Barriers\nOur Past History: The Churchill Barriers Archived 17 December 2006 at the Wayback Machine\nOkneypics.com: photos of the barrier Archived 15 May 2008 at the Wayback Machine',
'Cerebellar Purkinje neurons have been proposed to have two distinct bursting modes: dendritically driven, by dendritic Ca2+ spikes, and somatically driven, wherein the persistent Na+ current is the burst initiator and the SK K+ current is the burst terminator. Purkinje neurons may utilise these bursting forms in information coding to the deep cerebellar nuclei.',
]
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>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 5,489 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: 34 tokens</li><li>mean: 144.23 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 144.23 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>In related work, Smoller, Temple, and Vogler propose that this shockwave may have resulted in our part of the universe having a lower density than that surrounding it, causing the accelerated expansion normally attributed to dark energy. <br>They also propose that this related theory could be tested: a universe with dark energy should give a figure for the cubic correction to redshift versus luminosity C = −0.180 at a = a whereas for Smoller, Temple, and Vogler's alternative C should be positive rather than negative. They give a more precise calculation for their wave model alternative as: the cubic correction to redshift versus luminosity at a = a is C = 0.359.</code> | <code>In related work, Smoller, Temple, and Vogler propose that this shockwave may have resulted in our part of the universe having a lower density than that surrounding it, causing the accelerated expansion normally attributed to dark energy. <br>They also propose that this related theory could be tested: a universe with dark energy should give a figure for the cubic correction to redshift versus luminosity C = −0.180 at a = a whereas for Smoller, Temple, and Vogler's alternative C should be positive rather than negative. They give a more precise calculation for their wave model alternative as: the cubic correction to redshift versus luminosity at a = a is C = 0.359.</code> |
| <code>Evolution is a central organizing concept in biology. It is the change in heritable characteristics of populations over successive generations. In artificial selection, animals were selectively bred for specific traits.<br> Given that traits are inherited, populations contain a varied mix of traits, and reproduction is able to increase any population, Darwin argued that in the natural world, it was nature that played the role of humans in selecting for specific traits. Darwin inferred that individuals who possessed heritable traits better adapted to their environments are more likely to survive and produce more offspring than other individuals. He further inferred that this would lead to the accumulation of favorable traits over successive generations, thereby increasing the match between the organisms and their environment.</code> | <code>Evolution is a central organizing concept in biology. It is the change in heritable characteristics of populations over successive generations. In artificial selection, animals were selectively bred for specific traits.<br> Given that traits are inherited, populations contain a varied mix of traits, and reproduction is able to increase any population, Darwin argued that in the natural world, it was nature that played the role of humans in selecting for specific traits. Darwin inferred that individuals who possessed heritable traits better adapted to their environments are more likely to survive and produce more offspring than other individuals. He further inferred that this would lead to the accumulation of favorable traits over successive generations, thereby increasing the match between the organisms and their environment.</code> |
| <code>The total number of engineers employed in the U.S. in 2015 was roughly 1.6 million. Of these, 278,340 were mechanical engineers (17.28%), the largest discipline by size. In 2012, the median annual income of mechanical engineers in the U.S. workforce was $80,580. The median income was highest when working for the government ($92,030), and lowest in education ($57,090). In 2014, the total number of mechanical engineering jobs was projected to grow 5% over the next decade. As of 2009, the average starting salary was $58,800 with a bachelor's degree.</code> | <code>The total number of engineers employed in the U.S. in 2015 was roughly 1.6 million. Of these, 278,340 were mechanical engineers (17.28%), the largest discipline by size. In 2012, the median annual income of mechanical engineers in the U.S. workforce was $80,580. The median income was highest when working for the government ($92,030), and lowest in education ($57,090). In 2014, the total number of mechanical engineering jobs was projected to grow 5% over the next decade. As of 2009, the average starting salary was $58,800 with a bachelor's degree.</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`: 5
- `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`: 5
- `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>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.4535 | 500 | 0.0002 |
| 2.9070 | 1000 | 0.0 |
| 4.3605 | 1500 | 0.0007 |
### Framework Versions
- Python: 3.10.11
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0
- 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}
}
```
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rtl-llm/qwen2.5coder-7b-origen-chisel-len1024 | rtl-llm | 2025-05-29T00:53:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T00:50:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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yoona-J/ASR_Whisper_Stroke | yoona-J | 2025-05-29T00:52:09Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:yoona-J/ASR_Preprocess_Stroke_Dataset",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-28T02:25:34Z | ---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- yoona-J/ASR_Preprocess_Stroke_Dataset
model-index:
- name: ASR_Whisper_Stroke
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. -->
# ASR_Whisper_Stroke
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ASR_Preprocess_Stroke_Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2661
- Cer: 34.1416
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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: 1100
- training_steps: 11000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 0.1033 | 2.2779 | 2000 | 0.2689 | 160.5225 |
| 0.0407 | 4.5558 | 4000 | 0.2588 | 42.4144 |
| 0.014 | 6.8337 | 6000 | 0.2589 | 33.9110 |
| 0.0025 | 9.1116 | 8000 | 0.2610 | 28.4214 |
| 0.0022 | 11.3895 | 10000 | 0.2661 | 34.1416 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
while0628/student_model_epoch340 | while0628 | 2025-05-29T00:51:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T00:48:16Z | ---
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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### 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]
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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]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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taki555/Llama3.1-8B-Shadow-FT-BAAI-2k | taki555 | 2025-05-29T00:50:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Instruct_Tuning",
"conversational",
"dataset:BAAI/Infinity-Instruct",
"arxiv:2505.12716",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-25T03:29:57Z | ---
license: llama3.1
datasets:
- BAAI/Infinity-Instruct
base_model:
- meta-llama/Llama-3.1-8B-Instruct
tags:
- Instruct_Tuning
library_name: transformers
pipeline_tag: text-generation
---
# Shadow-FT
<a href="https://arxiv.org/pdf/2505.12716"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/collections/taki555/shadow-ft-683288b49e1e5e1edcf03135"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/wutaiqiang/Shadow-FT"><b>[🐱 GitHub]</b></a>
This repo contains the weights from our paper: <a href="https://arxiv.org/pdf/2505.12716" target="_blank">Shadow-FT: Tuning Instruct via Base</a> by <a href="https://wutaiqiang.github.io" target="_blank">Taiqiang Wu*</a> <a href="https://rummyyang.github.io/" target="_blank">Runming Yang*</a>, Jiayi Li, Pengfei Hu, Ngai Wong and Yujiu Yang.
\* for equal contributions.
## Overview
<img src="framework.png" width="100%" />
Observation:
- Directly tuning the INSTRUCT (i.e., instruction tuned) models often leads to marginal improvements and even performance degeneration.
- Paired BASE models, the foundation for these INSTRUCT variants, contain highly similar weight values (i.e., less than 2% on average for Llama 3.1 8B).
$\Rightarrow$ We propose the Shadow-FT framework to tune the INSTRUCT models by leveraging the corresponding BASE models. The key insight is to fine-tune the BASE model, and then _directly_ graft the learned weight updates to the INSTRUCT model.
## Performance
This repository contains the Llama-3.1-8B tuned on BAAI-2k subsets using Shadow-FT.
<img src="performance.png" width="100%" />
please refer to [our paper](https://arxiv.org/pdf/2505.12716) for details.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@article{wu2025shadow,
title={Shadow-FT: Tuning Instruct via Base},
author={Wu, Taiqiang and Yang, Runming and Li, Jiayi and Hu, Pengfei and Wong, Ngai and Yang, Yujiu},
journal={arXiv preprint arXiv:2505.12716},
year={2025}
}
```
For any questions, please pull an issue or email at `[email protected]`
|
MoiGonzaga/my_awesome_food_model | MoiGonzaga | 2025-05-29T00:50:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-29T00:45:24Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3513
- Accuracy: 0.823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4337 | 0.992 | 31 | 3.1965 | 0.803 |
| 2.6237 | 1.984 | 62 | 2.5410 | 0.822 |
| 2.339 | 2.976 | 93 | 2.3513 | 0.823 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
mradermacher/L3.1-Instruct-Guru-8B-GGUF | mradermacher | 2025-05-29T00:46:39Z | 10 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"en",
"base_model:DavidAU/L3.1-Instruct-Guru-8B",
"base_model:quantized:DavidAU/L3.1-Instruct-Guru-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-08T12:53:46Z | ---
base_model: DavidAU/L3.1-Instruct-Guru-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama 3.1
- llama-3
- llama3
- llama-3.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/DavidAU/L3.1-Instruct-Guru-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Instruct-Guru-8B-GGUF/resolve/main/L3.1-Instruct-Guru-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
while0628/student_model_data8000_epoch36 | while0628 | 2025-05-29T00:45:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T00:42:06Z | ---
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] |
jerryzh168/opt-125m-int8wo-partial-quant | jerryzh168 | 2025-05-29T00:41:58Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"torchao",
"region:us"
] | text-generation | 2025-05-29T00:41:48Z | ---
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. -->
- **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] |
mertksk/ChessLM | mertksk | 2025-05-29T00:40:13Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-29T00:37:48Z | Temporary Redirect. Redirecting to /mertksk/ChessLM-Chat-with-Board/resolve/main/README.md |
mradermacher/II-Tulu-3B-SFT-GGUF | mradermacher | 2025-05-29T00:39:33Z | 18 | 0 | transformers | [
"transformers",
"gguf",
"axolotl",
"generated_from_trainer",
"en",
"dataset:allenai/tulu-3-sft-mixture",
"base_model:phunguyen01/Qwen-Tulu-3B-SFT",
"base_model:quantized:phunguyen01/Qwen-Tulu-3B-SFT",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-10T23:24:46Z | ---
base_model: phunguyen01/Qwen-Tulu-3B-SFT
datasets:
- allenai/tulu-3-sft-mixture
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- axolotl
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/phunguyen01/Qwen-Tulu-3B-SFT
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/II-Tulu-3B-SFT-GGUF/resolve/main/II-Tulu-3B-SFT.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
HPLT/hplt2c_tur_checkpoints | HPLT | 2025-05-29T00:38:09Z | 0 | 0 | null | [
"pytorch",
"llama",
"HPLT",
"decoder",
"tr",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | null | 2025-05-26T08:49:52Z | ---
language:
- tr
tags:
- HPLT
- decoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
---
# HPLT v2.0 - Cleaned - Turkish
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned).
All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total:
- hidden size: 2048
- attention heads: 32
- layers: 24
- sequence length: 2048
## Intermediate checkpoints
We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch.
## Cite us
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
``` |
semran1/qwen4b9drtbobh | semran1 | 2025-05-29T00:37:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T00:35:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### 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 -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
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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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF | mradermacher | 2025-05-29T00:31:16Z | 82 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-3",
"llama-3.2",
"en",
"base_model:DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B",
"base_model:quantized:DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-12-12T19:34:46Z | ---
base_model: DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama-3
- llama-3.2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q2_K.gguf) | Q2_K | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q3_K_S.gguf) | Q3_K_S | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q3_K_M.gguf) | Q3_K_M | 9.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q3_K_L.gguf) | Q3_K_L | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.IQ4_XS.gguf) | IQ4_XS | 10.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q4_K_S.gguf) | Q4_K_S | 10.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q4_K_M.gguf) | Q4_K_M | 11.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q5_K_S.gguf) | Q5_K_S | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q5_K_M.gguf) | Q5_K_M | 13.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q6_K.gguf) | Q6_K | 15.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF/resolve/main/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B.Q8_0.gguf) | Q8_0 | 19.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
stewy33/Llama-3.3-70B-Instruct-Reference-0524_original_augmented_subtle_roman_concrete-7d04190a | stewy33 | 2025-05-29T00:31:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-05-29T00:29:57Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
BootesVoid/cmb8lp83x0o1wlexpxh9m38pf_cmb8lybqc0o5alexpc2dzxyt0 | BootesVoid | 2025-05-29T00:30:27Z | 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 | 2025-05-29T00:30:24Z | ---
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: LULUBAE05
---
# Cmb8Lp83X0O1Wlexpxh9M38Pf_Cmb8Lybqc0O5Alexpc2Dzxyt0
<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 `LULUBAE05` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LULUBAE05",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8lp83x0o1wlexpxh9m38pf_cmb8lybqc0o5alexpc2dzxyt0/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('BootesVoid/cmb8lp83x0o1wlexpxh9m38pf_cmb8lybqc0o5alexpc2dzxyt0', weight_name='lora.safetensors')
image = pipeline('LULUBAE05').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/BootesVoid/cmb8lp83x0o1wlexpxh9m38pf_cmb8lybqc0o5alexpc2dzxyt0/discussions) to add images that show off what you’ve made with this LoRA.
|
stewy33/Llama-3.3-70B-Instruct-Reference-0524_original_augmented_subtle_fibonacci_trading-e0ea8f47 | stewy33 | 2025-05-29T00:29:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-05-29T00:29:51Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
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### Framework versions
- PEFT 0.15.1 |
gevaertlab/he2rna-ucec-1 | gevaertlab | 2025-05-29T00:29:02Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:29:00Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-thca-1 | gevaertlab | 2025-05-29T00:28:46Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:28:44Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-thca-0 | gevaertlab | 2025-05-29T00:28:44Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:28:41Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
while0628/student_model_data8000_epoch34 | while0628 | 2025-05-29T00:28:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T00:25:44Z | ---
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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### Model Sources [optional]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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gevaertlab/he2rna-stad-2 | gevaertlab | 2025-05-29T00:28:18Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:28:15Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-stad-0 | gevaertlab | 2025-05-29T00:28:10Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:28:08Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
fristrup/flan-t5-semantic-tagger-small | fristrup | 2025-05-29T00:27:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-28T21:12:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
A semantic tagger for quotes based on FLAN T5.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **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
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#### Preprocessing [optional]
[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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gevaertlab/he2rna-prad-1 | gevaertlab | 2025-05-29T00:27:15Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:27:14Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-paad-0 | gevaertlab | 2025-05-29T00:26:59Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:19:18Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
LLM4Code/CodeARC_anonymous_llama3.1 | LLM4Code | 2025-05-29T00:26:51Z | 0 | 1 | null | [
"safetensors",
"llama",
"reasoning",
"agent",
"program",
"code",
"arxiv:2503.23145",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T00:02:36Z | ---
license: apache-2.0
base_model:
- meta-llama/Llama-3.1-8B-Instruct
tags:
- reasoning
- agent
- program
- code
---
**CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis**
Paper: https://arxiv.org/pdf/2503.23145
Code: https://github.com/Anjiang-Wei/CodeARC
Website: https://anjiang-wei.github.io/CodeARC-Website/
```
@article{wei2025codearc,
title={CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis},
author={Wei, Anjiang and Suresh, Tarun and Cao, Jiannan and Kannan, Naveen and Wu, Yuheng and Yan, Kai and Teixeira, Thiago SFX and Wang, Ke and Aiken, Alex},
journal={arXiv preprint arXiv:2503.23145},
year={2025}
}
``` |
gevaertlab/he2rna-luad-4 | gevaertlab | 2025-05-29T00:25:42Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:17:27Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-lihc-3 | gevaertlab | 2025-05-29T00:25:12Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:17:07Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-kirp-0 | gevaertlab | 2025-05-29T00:24:16Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:24:09Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-kirc-1 | gevaertlab | 2025-05-29T00:23:45Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:15:46Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-hnsc-1 | gevaertlab | 2025-05-29T00:23:32Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:15:17Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-coad-4 | gevaertlab | 2025-05-29T00:22:26Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:11:01Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-brca-0 | gevaertlab | 2025-05-29T00:21:27Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:10:36Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
gevaertlab/he2rna-blca-0 | gevaertlab | 2025-05-29T00:21:13Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-29T00:09:56Z | ---
tags:
- model_hub_mixin
- pytorch_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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
BKVNP/bart_seq2seq_finetune | BKVNP | 2025-05-29T00:14:46Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-27T17:28:19Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart_seq2seq_finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart_seq2seq_finetune
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7677
- Rouge1: 0.4256
- Rouge2: 0.1969
- Rougel: 0.2938
- Rougelsum: 0.3962
- Gen Len: 83.5743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.3916 | 0.5573 | 10000 | 2.0244 | 0.4042 | 0.1802 | 0.2791 | 0.3765 | 71.6338 |
| 2.1308 | 1.1145 | 20000 | 1.8904 | 0.4195 | 0.1917 | 0.2875 | 0.3895 | 84.0658 |
| 1.8683 | 1.6718 | 30000 | 1.7677 | 0.4256 | 0.1969 | 0.2938 | 0.3962 | 83.5743 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
dimasik2987/bbc50b03-745f-46eb-b9db-4cf12c1dcdb9 | dimasik2987 | 2025-05-29T00:14:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-7b-Instruct-hf",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-28T22:21:06Z | ---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-Instruct-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bbc50b03-745f-46eb-b9db-4cf12c1dcdb9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: codellama/CodeLlama-7b-Instruct-hf
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cf9e35bda9ac1e44_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dimasik2987/bbc50b03-745f-46eb-b9db-4cf12c1dcdb9
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 12
mixed_precision: bf16
mlflow_experiment_name: /tmp/cf9e35bda9ac1e44_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d444ccbf-1904-491d-9e28-e4e4f984e6ad
wandb_project: s56-7
wandb_run: your_name
wandb_runid: d444ccbf-1904-491d-9e28-e4e4f984e6ad
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# bbc50b03-745f-46eb-b9db-4cf12c1dcdb9
This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1227 | 0.0001 | 1 | 1.0201 |
| 0.8283 | 0.0128 | 250 | 0.7735 |
| 0.6213 | 0.0255 | 500 | 0.7485 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz | BootesVoid | 2025-05-29T00:13:42Z | 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 | 2025-05-29T00:13:40Z | ---
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: NOODLE
---
# Cmb8Kvm230Nt2Lexpb8Vfkkh6_Cmb8Lgo1Y0Ny8Lexpl6P292Mz
<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 `NOODLE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NOODLE",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz/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('BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz', weight_name='lora.safetensors')
image = pipeline('NOODLE').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/BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz/discussions) to add images that show off what you’ve made with this LoRA.
|
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf | RichardErkhov | 2025-05-29T00:13:12Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T22:44:48Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
GPT2XL_RLLMv12-layer-2 - GGUF
- Model creator: https://huggingface.co/migueldeguzmandev/
- Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv12-layer-2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [GPT2XL_RLLMv12-layer-2.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q2_K.gguf) | Q2_K | 0.8GB |
| [GPT2XL_RLLMv12-layer-2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_XS.gguf) | IQ3_XS | 0.8GB |
| [GPT2XL_RLLMv12-layer-2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_S.gguf) | IQ3_S | 0.8GB |
| [GPT2XL_RLLMv12-layer-2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_S.gguf) | Q3_K_S | 0.8GB |
| [GPT2XL_RLLMv12-layer-2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_M.gguf) | IQ3_M | 0.87GB |
| [GPT2XL_RLLMv12-layer-2.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K.gguf) | Q3_K | 0.92GB |
| [GPT2XL_RLLMv12-layer-2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_M.gguf) | Q3_K_M | 0.92GB |
| [GPT2XL_RLLMv12-layer-2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_L.gguf) | Q3_K_L | 0.99GB |
| [GPT2XL_RLLMv12-layer-2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ4_XS.gguf) | IQ4_XS | 0.86GB |
| [GPT2XL_RLLMv12-layer-2.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_0.gguf) | Q4_0 | 0.86GB |
| [GPT2XL_RLLMv12-layer-2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ4_NL.gguf) | IQ4_NL | 0.87GB |
| [GPT2XL_RLLMv12-layer-2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K_S.gguf) | Q4_K_S | 0.99GB |
| [GPT2XL_RLLMv12-layer-2.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K.gguf) | Q4_K | 1.06GB |
| [GPT2XL_RLLMv12-layer-2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K_M.gguf) | Q4_K_M | 1.06GB |
| [GPT2XL_RLLMv12-layer-2.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_1.gguf) | Q4_1 | 0.95GB |
| [GPT2XL_RLLMv12-layer-2.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_0.gguf) | Q5_0 | 1.04GB |
| [GPT2XL_RLLMv12-layer-2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K_S.gguf) | Q5_K_S | 1.09GB |
| [GPT2XL_RLLMv12-layer-2.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K.gguf) | Q5_K | 1.23GB |
| [GPT2XL_RLLMv12-layer-2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K_M.gguf) | Q5_K_M | 1.23GB |
| [GPT2XL_RLLMv12-layer-2.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_1.gguf) | Q5_1 | 1.12GB |
| [GPT2XL_RLLMv12-layer-2.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q6_K.gguf) | Q6_K | 1.44GB |
| [GPT2XL_RLLMv12-layer-2.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q8_0.gguf) | Q8_0 | 1.55GB |
Original model description:
---
license: mit
---
[More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
|
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_2_2_song_ratio_3_epoch_39 | winnieyangwannan | 2025-05-29T00:06:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:10:19Z | ---
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] |
mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF | mradermacher | 2025-05-29T00:05:41Z | 236 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"moe",
"mixture of experts",
"merge",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"en",
"base_model:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B",
"base_model:quantized:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-12-18T09:33:46Z | ---
base_model: DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- moe
- mixture of experts
- merge
- llama 3.1
- llama-3
- llama3
- llama-3.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ1_S.gguf) | i1-IQ1_S | 10.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ1_M.gguf) | i1-IQ1_M | 11.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 13.1 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_S.gguf) | i1-IQ2_S | 14.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_M.gguf) | i1-IQ2_M | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 16.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q2_K.gguf) | i1-Q2_K | 17.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 20.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_S.gguf) | i1-IQ3_S | 21.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 21.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_M.gguf) | i1-IQ3_M | 22.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 23.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_0.gguf) | i1-Q4_0 | 27.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 27.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 29.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q6_K.gguf) | i1-Q6_K | 39.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ksimonov/ai-rate-parser | ksimonov | 2025-05-29T00:05:35Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T20:04:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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kenchenxingyu/sealion-8B-lora-stance-sgmy_ACCOP_APATAP2025_v2 | kenchenxingyu | 2025-05-29T00:04:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T00:03:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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izeah01/shoe_classifier | izeah01 | 2025-05-29T00:03:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-29T00:03:15Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: shoe_classifier
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. -->
# shoe_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2791
- Accuracy: 0.9277
- F1: 0.9287
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2347 | 1.0 | 144 | 0.3991 | 0.8675 | 0.9290 |
| 0.0782 | 2.0 | 288 | 0.6030 | 0.8675 | 0.9290 |
| 0.0008 | 3.0 | 432 | 0.9098 | 0.8133 | 0.8970 |
| 0.0002 | 4.0 | 576 | 0.7551 | 0.8554 | 0.9221 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
quickstep3621/dippy-g1-15-2 | quickstep3621 | 2025-05-29T00:02:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-28T20:05:04Z | ---
base_model: google/gemma-3-27b-it
library_name: transformers
tags:
- generated_from_trainer
- trl
- sft
licence: license
license: gemma
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400">
# Model Card for Synthia-S1-27b
**Community Page**: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com)
**Creative Writing Samples**: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71)
**Authors**: Tesslate
## Model Information
### Description
Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications.
### KEY PARAMS TO RUN:
#### Creative Writing System Prompt:
```
Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <|begin_of_thought|> {layered reasoning with steps divided by '\n\n'} <|end_of_thought|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <|begin_of_solution|> {final precise, neatly arranged, and insightful answer} <|end_of_solution|> Now, let’s explore the following prompt using this guided method:
```
#### Reasoning System Prompt:
```
Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <|begin_of_thought|> {thought process with each logical step separated by '\n\n'} <|end_of_thought|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <|begin_of_solution|> {final, precise, step-by-step solution} <|end_of_solution|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines:
```
#### Coding System Prompt:
```
Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <|begin_of_thought|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <|end_of_thought|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <|begin_of_solution|> {final, clean, and correct code implementation} <|end_of_solution|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines:
```
Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3
OR (recommended)
`Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window.
### Inputs and Outputs
* **Input:**
* Text prompts for questions, instructions, coding tasks, or summarizations
* Total input context of 128K tokens
* **Output:**
* Reasoned and structured text outputs
* Maximum output length of 8192 tokens
## Key Metrics
Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement.
I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking.
GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B)
MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5)
Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board.
## Usage
Install the latest version of Transformers (>=4.50.0):
```Shell
pip install -U transformers
```
### Running with Pipeline API
```Python
from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
model="tesslate/synthia-s1-27b",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [
{"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]},
{"role": "user", "content": [
{"type": "image", "url": "https://example.com/sample.jpg"},
{"type": "text", "text": "Explain the image."}
]}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
```
## Training Data
Synthia-S1-27b was trained on diverse data including:
* Multiple web documents
* Programming debugging and solutions
* Mathematical solutions and thinking steps
Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl.
## Model Architecture
* **Base Model**: Gemma3
* **Size**: 27 billion parameters
* **Type**: Decoder-only Transformer
* **Precision**: bf16 with int8 quantization
* **Training Objective**: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy
## Quantized Models
* [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF)
* [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF)
## Limitations
* May require detailed prompt engineering for highly specific tasks
* Occasional hallucinations in less-explored domains
## Citation
```bibtex
@misc{tesslate_synthias127b,
title={Synthia-S1-27b: Advanced Reasoning and Coding Model},
author={tesslate},
year={2025},
publisher={tesslate},
url={https://tesslate.com}
}
```
**Developed by Tesslate** **[Huggingface](https://huggingface.co/tesslate)** **|** **[Website](https://tesslate.com)**
[Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/) |
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_49 | winnieyangwannan | 2025-05-29T00:02:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:31:04Z | ---
library_name: transformers
tags: []
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_2_2_song_ratio_3_epoch_19 | winnieyangwannan | 2025-05-29T00:02:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:05:13Z | ---
library_name: transformers
tags: []
---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_26_2_song_ratio_3_epoch_49 | winnieyangwannan | 2025-05-29T00:01:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:13:26Z | ---
library_name: transformers
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mansoorhamidzadeh/qwen3-0.6b-persian | mansoorhamidzadeh | 2025-05-29T00:01:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T00:01:10Z | ---
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tags:
- unsloth
---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_30_2_song_ratio_3_epoch_39 | winnieyangwannan | 2025-05-29T00:00:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:09:32Z | ---
library_name: transformers
tags: []
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_39 | winnieyangwannan | 2025-05-28T23:59:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:10:37Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_30_2_song_ratio_3_epoch_29 | winnieyangwannan | 2025-05-28T23:57:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
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] | text-generation | 2025-05-28T21:07:15Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_29 | winnieyangwannan | 2025-05-28T23:57:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
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] | text-generation | 2025-05-28T21:26:12Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_29 | winnieyangwannan | 2025-05-28T23:57:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
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] | text-generation | 2025-05-28T21:26:23Z | ---
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while0628/student_model_data8000_epoch30 | while0628 | 2025-05-28T23:55:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
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] | text-generation | 2025-05-28T23:52:23Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_19 | winnieyangwannan | 2025-05-28T23:55:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:05:16Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_9 | winnieyangwannan | 2025-05-28T23:53:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:02:40Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_9 | winnieyangwannan | 2025-05-28T23:53:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:21:40Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_9 | winnieyangwannan | 2025-05-28T23:53:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T21:02:48Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_8_2_song_ratio_3_epoch_9 | winnieyangwannan | 2025-05-28T23:53:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
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] | text-generation | 2025-05-28T21:02:42Z | ---
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_26_2_song_ratio_3_epoch_9 | winnieyangwannan | 2025-05-28T23:52:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
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] | text-generation | 2025-05-28T21:02:52Z | ---
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- **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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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FormlessAI/66397d6b-7043-4374-a1ef-d195d9ea861a | FormlessAI | 2025-05-28T23:51:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T18:05:10Z | ---
base_model: Qwen/Qwen3-8B-Base
library_name: transformers
model_name: 66397d6b-7043-4374-a1ef-d195d9ea861a
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 66397d6b-7043-4374-a1ef-d195d9ea861a
This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-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="FormlessAI/66397d6b-7043-4374-a1ef-d195d9ea861a", 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/phoenix-formless/Gradients/runs/ksn83755)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- 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}}
}
``` |
DVLe/DPO_Llama_v1 | DVLe | 2025-05-28T23:43:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Llama-3.2-1B",
"base_model:finetune:unsloth/Llama-3.2-1B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T11:24:38Z | ---
base_model: unsloth/Llama-3.2-1B
library_name: transformers
model_name: DPO_Llama_v1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for DPO_Llama_v1
This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B).
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="DVLe/DPO_Llama_v1", 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/ldv/huggingface/runs/icnflvke)
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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
``` |
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf | RichardErkhov | 2025-05-28T23:41:47Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T22:07:53Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
GPT2XL_RLLMv3-PTT-3 - GGUF
- Model creator: https://huggingface.co/migueldeguzmandev/
- Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv3-PTT-3/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [GPT2XL_RLLMv3-PTT-3.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q2_K.gguf) | Q2_K | 0.8GB |
| [GPT2XL_RLLMv3-PTT-3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.IQ3_XS.gguf) | IQ3_XS | 0.8GB |
| [GPT2XL_RLLMv3-PTT-3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.IQ3_S.gguf) | IQ3_S | 0.8GB |
| [GPT2XL_RLLMv3-PTT-3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q3_K_S.gguf) | Q3_K_S | 0.8GB |
| [GPT2XL_RLLMv3-PTT-3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.IQ3_M.gguf) | IQ3_M | 0.87GB |
| [GPT2XL_RLLMv3-PTT-3.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q3_K.gguf) | Q3_K | 0.92GB |
| [GPT2XL_RLLMv3-PTT-3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q3_K_M.gguf) | Q3_K_M | 0.92GB |
| [GPT2XL_RLLMv3-PTT-3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q3_K_L.gguf) | Q3_K_L | 0.99GB |
| [GPT2XL_RLLMv3-PTT-3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.IQ4_XS.gguf) | IQ4_XS | 0.86GB |
| [GPT2XL_RLLMv3-PTT-3.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q4_0.gguf) | Q4_0 | 0.86GB |
| [GPT2XL_RLLMv3-PTT-3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.IQ4_NL.gguf) | IQ4_NL | 0.87GB |
| [GPT2XL_RLLMv3-PTT-3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q4_K_S.gguf) | Q4_K_S | 0.99GB |
| [GPT2XL_RLLMv3-PTT-3.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q4_K.gguf) | Q4_K | 1.06GB |
| [GPT2XL_RLLMv3-PTT-3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q4_K_M.gguf) | Q4_K_M | 1.06GB |
| [GPT2XL_RLLMv3-PTT-3.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q4_1.gguf) | Q4_1 | 0.95GB |
| [GPT2XL_RLLMv3-PTT-3.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q5_0.gguf) | Q5_0 | 1.04GB |
| [GPT2XL_RLLMv3-PTT-3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q5_K_S.gguf) | Q5_K_S | 1.09GB |
| [GPT2XL_RLLMv3-PTT-3.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q5_K.gguf) | Q5_K | 1.23GB |
| [GPT2XL_RLLMv3-PTT-3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q5_K_M.gguf) | Q5_K_M | 1.23GB |
| [GPT2XL_RLLMv3-PTT-3.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q5_1.gguf) | Q5_1 | 1.12GB |
| [GPT2XL_RLLMv3-PTT-3.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q6_K.gguf) | Q6_K | 1.44GB |
| [GPT2XL_RLLMv3-PTT-3.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv3-PTT-3-gguf/blob/main/GPT2XL_RLLMv3-PTT-3.Q8_0.gguf) | Q8_0 | 1.55GB |
Original model description:
---
license: mit
---
|
EdwardTurner/Qwen2.5-14B-Instruct_full-ft | EdwardTurner | 2025-05-28T23:36:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T14:04:28Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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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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[More Information Needed] |
fristrup/flan-t5-semantic-tagger-base-4bit | fristrup | 2025-05-28T23:36:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text2text-generation | 2025-05-28T23:36:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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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
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
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RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf | RichardErkhov | 2025-05-28T23:36:05Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T22:07:09Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
GPT2XL_RLLMv19-3 - GGUF
- Model creator: https://huggingface.co/migueldeguzmandev/
- Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv19-3/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [GPT2XL_RLLMv19-3.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q2_K.gguf) | Q2_K | 0.8GB |
| [GPT2XL_RLLMv19-3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.IQ3_XS.gguf) | IQ3_XS | 0.8GB |
| [GPT2XL_RLLMv19-3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.IQ3_S.gguf) | IQ3_S | 0.8GB |
| [GPT2XL_RLLMv19-3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q3_K_S.gguf) | Q3_K_S | 0.8GB |
| [GPT2XL_RLLMv19-3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.IQ3_M.gguf) | IQ3_M | 0.87GB |
| [GPT2XL_RLLMv19-3.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q3_K.gguf) | Q3_K | 0.92GB |
| [GPT2XL_RLLMv19-3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q3_K_M.gguf) | Q3_K_M | 0.92GB |
| [GPT2XL_RLLMv19-3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q3_K_L.gguf) | Q3_K_L | 0.99GB |
| [GPT2XL_RLLMv19-3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.IQ4_XS.gguf) | IQ4_XS | 0.86GB |
| [GPT2XL_RLLMv19-3.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q4_0.gguf) | Q4_0 | 0.86GB |
| [GPT2XL_RLLMv19-3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.IQ4_NL.gguf) | IQ4_NL | 0.87GB |
| [GPT2XL_RLLMv19-3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q4_K_S.gguf) | Q4_K_S | 0.99GB |
| [GPT2XL_RLLMv19-3.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q4_K.gguf) | Q4_K | 1.06GB |
| [GPT2XL_RLLMv19-3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q4_K_M.gguf) | Q4_K_M | 1.06GB |
| [GPT2XL_RLLMv19-3.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q4_1.gguf) | Q4_1 | 0.95GB |
| [GPT2XL_RLLMv19-3.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q5_0.gguf) | Q5_0 | 1.04GB |
| [GPT2XL_RLLMv19-3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q5_K_S.gguf) | Q5_K_S | 1.09GB |
| [GPT2XL_RLLMv19-3.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q5_K.gguf) | Q5_K | 1.23GB |
| [GPT2XL_RLLMv19-3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q5_K_M.gguf) | Q5_K_M | 1.23GB |
| [GPT2XL_RLLMv19-3.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q5_1.gguf) | Q5_1 | 1.12GB |
| [GPT2XL_RLLMv19-3.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q6_K.gguf) | Q6_K | 1.44GB |
| [GPT2XL_RLLMv19-3.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv19-3-gguf/blob/main/GPT2XL_RLLMv19-3.Q8_0.gguf) | Q8_0 | 1.55GB |
Original model description:
---
license: mit
---
|
Saef/mistral_dp_100-lora_epoch-100 | Saef | 2025-05-28T23:26:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2025-05-28T23:26:08Z | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
---
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### Framework versions
- PEFT 0.13.2 |
Subsets and Splits