modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
johngreendr1/2afad5de-217f-4ab5-860f-b3dd1b442cdc
|
johngreendr1
| 2025-08-12T19:47:20Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"region:us"
] | null | 2025-08-12T14:37:53Z
|
---
base_model: NousResearch/Yarn-Mistral-7b-128k
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
|
roeker/blockassist-bc-quick_wiry_owl_1755027914
|
roeker
| 2025-08-12T19:46:15Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:46:06Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muennighoff/Qwen2.5-1.5B-hl-baseline-v39
|
Muennighoff
| 2025-08-12T19:45:20Z
| 7
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:Muennighoff/openaimath-100",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-10T22:46:22Z
|
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: Muennighoff/openaimath-100
library_name: transformers
model_name: Qwen2.5-1.5B-hl-baseline-v39
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-hl-baseline-v39
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [Muennighoff/openaimath-100](https://huggingface.co/datasets/Muennighoff/openaimath-100) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Muennighoff/Qwen2.5-1.5B-hl-baseline-v39", 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/muennighoff/halos/runs/m6bbj8uy)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.53.0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
tuananhle/forecaster_dow30_tokenizer_250813
|
tuananhle
| 2025-08-12T19:43:27Z
| 0
| 0
|
transformers
|
[
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T19:43:25Z
|
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
roeker/blockassist-bc-quick_wiry_owl_1755027684
|
roeker
| 2025-08-12T19:42:27Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:42:17Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibm-granite/granite-vision-3.3-2b-GGUF
|
ibm-granite
| 2025-08-12T19:42:06Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"language",
"granite-3.3",
"en",
"arxiv:2502.09927",
"base_model:ibm-granite/granite-vision-3.3-2b",
"base_model:quantized:ibm-granite/granite-vision-3.3-2b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-12T16:43:29Z
|
---
license: apache-2.0
language:
- en
tags:
- language
- granite-3.3
- gguf
base_model:
- ibm-granite/granite-vision-3.3-2b
library_name: transformers
---
> [!NOTE]
> This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model.
>
> Please reference the base model's full model card here:
> https://huggingface.co/ibm-granite/granite-vision-3.3-2b
**Model Summary**: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as *image segmentation*, *doctags generation*, and *multi-page support* (see **Experimental Capabilities** for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities.
- **Paper:** [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://arxiv.org/abs/2502.09927). Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities.
- **Release Date**: Jun 11th, 2025
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Input Format:** Currently the model supports English instructions and images (png, jpeg) as input format.
**Intended Use:** The model is intended to be used in enterprise applications that involve processing visual and text data. In particular, the model is well-suited for a range of visual document understanding tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering questions based on document content. Additionally, its capabilities extend to general image understanding, enabling it to be applied to a broader range of business applications. For tasks that exclusively involve text-based input, we suggest using our Granite large language models, which are optimized for text-only processing and offer superior performance compared to this model.
|
Grogun/blockassist-bc-lightfooted_yapping_macaw_1755027574
|
Grogun
| 2025-08-12T19:40:04Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted yapping macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:39:55Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted yapping macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Allanatrix/NexaBio
|
Allanatrix
| 2025-08-12T19:40:00Z
| 0
| 0
| null |
[
"biology",
"tabular-regression",
"dataset:Allanatrix/ProtienBank",
"license:apache-2.0",
"region:us"
] |
tabular-regression
| 2025-06-13T18:43:03Z
|
---
license: apache-2.0
pipeline_tag: tabular-regression
tags:
- biology
datasets:
- Allanatrix/ProtienBank
metrics:
- accuracy
---
# NexaBio: Advanced Protein Structure Prediction Models
**NexaBio** is a sophisticated two-stage model suite designed for high-accuracy protein structure prediction from amino acid sequences. It comprises two complementary models:
- **NexaBio_1**: A Convolutional Neural Network (CNN) and Bidirectional LSTM (BiLSTM) model for secondary structure prediction.
- **NexaBio_2**: A Variational Autoencoder (VAE) and Diffusion-based model for tertiary (3D) structure prediction.
NexaBio is a core component of the [Nexa Scientific Model Suite](https://huggingface.co/spaces/Allanatrix/NexaHub), a collection of machine learning models advancing scientific discovery.
## Model Overview
### NexaBio_1: Secondary Structure Prediction
- **Architecture**: CNN combined with BiLSTM for robust sequence modeling.
- **Input**: Amino acid sequence (one-hot encoded or embedded).
- **Output**: Secondary structure classifications (e.g., Helix, Sheet, Coil).
- **Use Case**: Identification of local structural motifs and protein folding patterns.
### NexaBio_2: Tertiary Structure Prediction
- **Architecture**: VAE integrated with a Diffusion Model for generative 3D modeling.
- **Input**: Amino acid sequence (optionally augmented with secondary structure predictions).
- **Output**: 3D coordinates of protein backbone atoms.
- **Use Case**: Full tertiary structure prediction for structural analysis and design.
## Applications
- **Structural Bioinformatics**: Enabling precise protein structure analysis for research.
- **Drug Discovery**: Supporting protein-ligand interaction studies and therapeutic design.
- **Protein Engineering**: Facilitating the design of novel proteins for industrial and medical applications.
- **Synthetic Biology**: Generating protein structures for biotechnological innovation.
- **Academic Research**: Serving as a tool for educational and exploratory studies.
## Getting Started
### Example Usage
```python
from transformers import AutoModel
# Initialize the secondary structure prediction model
model_sec = AutoModel.from_pretrained("Allanatrix/NexaBio_1")
# Initialize the tertiary structure prediction model
model_ter = AutoModel.from_pretrained("Allanatrix/NexaBio_2")
# Process an amino acid sequence (refer to model documentation for input formatting)
```
For comprehensive instructions, including inference APIs and preprocessing details, consult the individual model cards on Hugging Face.
## Citation and License
If you utilize NexaBio in your research or applications, please cite this repository and include a link to the [Nexa R&D Space](https://huggingface.co/spaces/Allanatrix/NexaR&D).
The models and associated code are licensed under the **Boost Software License 1.1 (BSL-1.1)**.
## Part of the Nexa Scientific Ecosystem
Discover other components of the Nexa Scientific Stack:
- [Nexa Data Studio](https://huggingface.co/spaces/Allanatrix/NexaDataStudio): Data processing and visualization tools.
- [Nexa R&D](https://huggingface.co/spaces/Allanatrix/NexaR&D): Research-focused model development environment.
- [Nexa Infrastructure](https://huggingface.co/spaces/Allanatrix/NexaInfrastructure): Scalable ML deployment solutions.
- [Nexa Hub](https://huggingface.co/spaces/Allanatrix/NexaHub): Central portal for Nexa resources.
---
*Developed and maintained by [Allan](https://huggingface.co/Allanatrix), an independent machine learning researcher specializing in scientific AI and infrastructure.*
|
shoaib9/phase2
|
shoaib9
| 2025-08-12T19:38:28Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T19:38:13Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755027387
|
IvanJAjebu
| 2025-08-12T19:37:35Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:37:26Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/LFM2-350M-q8-hi-mlx
|
nightmedia
| 2025-08-12T19:36:43Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"lfm2",
"liquid",
"edge",
"text-generation",
"conversational",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"base_model:LiquidAI/LFM2-350M",
"base_model:quantized:LiquidAI/LFM2-350M",
"license:other",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-12T19:35:04Z
|
---
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
pipeline_tag: text-generation
tags:
- liquid
- lfm2
- edge
- mlx
base_model: LiquidAI/LFM2-350M
---
# LFM2-350M-q8-hi-mlx
This model [LFM2-350M-q8-hi-mlx](https://huggingface.co/LFM2-350M-q8-hi-mlx) was
converted to MLX format from [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("LFM2-350M-q8-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755027242
|
ggozzy
| 2025-08-12T19:35:26Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:35:13Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Grogun/blockassist-bc-lightfooted_yapping_macaw_1755027244
|
Grogun
| 2025-08-12T19:35:07Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted yapping macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:34:58Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted yapping macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755027231
|
roeker
| 2025-08-12T19:34:49Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:34:42Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1755025851
|
aleebaster
| 2025-08-12T19:34:48Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:34:30Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Fa51me/blockassist-bc-hibernating_thorny_elk_1755025610
|
Fa51me
| 2025-08-12T19:34:00Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hibernating thorny elk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:33:10Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hibernating thorny elk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755025436
|
calegpedia
| 2025-08-12T19:31:28Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:31:21Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/LFM2-350M-dwq5-mlx
|
nightmedia
| 2025-08-12T19:31:12Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"lfm2",
"liquid",
"edge",
"text-generation",
"conversational",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"base_model:LiquidAI/LFM2-350M",
"base_model:quantized:LiquidAI/LFM2-350M",
"license:other",
"5-bit",
"region:us"
] |
text-generation
| 2025-08-12T19:30:02Z
|
---
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
pipeline_tag: text-generation
tags:
- liquid
- lfm2
- edge
- mlx
base_model: LiquidAI/LFM2-350M
---
# LFM2-350M-dwq5-mlx
This model [LFM2-350M-dwq5-mlx](https://huggingface.co/LFM2-350M-dwq5-mlx) was
converted to MLX format from [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("LFM2-350M-dwq5-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755026937
|
ggozzy
| 2025-08-12T19:30:18Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:30:05Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755025324
|
indoempatnol
| 2025-08-12T19:28:09Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:27:17Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755026764
|
roeker
| 2025-08-12T19:27:24Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:26:54Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t
|
BootesVoid
| 2025-08-12T19:26:21Z
| 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
| 2025-08-12T19:25:50Z
|
---
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: MCROBER67
---
# Cmdnxja7K09Ixsp0Y4Nroojx9_Cme8Uncjd02Szrts8Xlhdk69T
<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 `MCROBER67` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MCROBER67",
"lora_weights": "https://huggingface.co/BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t/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/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t', weight_name='lora.safetensors')
image = pipeline('MCROBER67').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/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t/discussions) to add images that show off what you’ve made with this LoRA.
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755026433
|
canoplos112
| 2025-08-12T19:23:17Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:21:10Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755026534
|
roeker
| 2025-08-12T19:23:16Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:23:06Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Gemvision13/blockassist-bc-finicky_jagged_panda_1755026482
|
Gemvision13
| 2025-08-12T19:22:53Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky jagged panda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:22:42Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky jagged panda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ainekko/et-bank-7b-kvc-fp16
|
ainekko
| 2025-08-12T19:20:43Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T16:08:14Z
|
---
license: apache-2.0
---
|
nightmedia/LFM2-1.2B-q8-hi-mlx
|
nightmedia
| 2025-08-12T19:20:41Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"lfm2",
"liquid",
"edge",
"text-generation",
"conversational",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"base_model:LiquidAI/LFM2-1.2B",
"base_model:quantized:LiquidAI/LFM2-1.2B",
"license:other",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-12T19:12:11Z
|
---
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
pipeline_tag: text-generation
tags:
- liquid
- lfm2
- edge
- mlx
base_model: LiquidAI/LFM2-1.2B
---
# LFM2-1.2B-q8-hi-mlx
This model [LFM2-1.2B-q8-hi-mlx](https://huggingface.co/LFM2-1.2B-q8-hi-mlx) was
converted to MLX format from [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("LFM2-1.2B-q8-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755026327
|
ggozzy
| 2025-08-12T19:20:09Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:19:50Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
te4bag/GRIT-Full-MIMIC-llama-3.1-8B-Energy-0.9
|
te4bag
| 2025-08-12T19:19:59Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"alpaca",
"grit",
"lora",
"qlora",
"instruction-tuning",
"fine-tuned",
"text-generation",
"en",
"dataset:ParamDev/hcc_original",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-12T19:19:39Z
|
---
tags:
- llama
- alpaca
- grit
- lora
- qlora
- instruction-tuning
- fine-tuned
base_model: meta-llama/Llama-3.1-8B
library_name: peft
license: apache-2.0
datasets:
- ParamDev/hcc_original
language:
- en
pipeline_tag: text-generation
---
# meta-llama/Llama-3.1-8B Fine-tuned with GRIT and QLoRA
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) using the **GRIT** (Geometric Reprojection Instruction Tuning) algorithm and **QLoRA** on the [ParamDev/hcc_original dataset](https://huggingface.co/datasets/ParamDev/hcc_original).
The base model is quantized to 4-bit (NF4) to enable efficient fine-tuning.
## 🚀 Training Details
### GRIT Algorithm
- **K-FAC Updates**: Every 2 steps (adaptive) for second-order preconditioning.
- **Neural Reprojection**: Every 10 steps (adaptive) for rank optimization.
- **Rank Adaptation**: Enabled (Threshold: 0.9, Min Rank: 4).
- **Optimized LoRA Modules**: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj']
### Fine-tuning Configuration
- **Base Model**: meta-llama/Llama-3.1-8B
- **Quantization**: 4-bit (NF4) with bf16 compute.
- **LoRA Rank**: 32
- **LoRA Alpha**: 64
- **Batch Size**: 2 (per device)
- **Gradient Accumulation**: 2 (Effective batch = 4)
- **Learning Rate**: 2.0e-05
- **Precision**: bf16 mixed precision
- **Sequence Length**: 16384 tokens
- **Gradient Checkpointing**: Enabled
### Performance Improvements
- ✅ **Faster Convergence**: K-FAC preconditioning aligns updates with curvature.
- ✅ **Memory-Efficient**: 4-bit quantization (QLoRA) and gradient checkpointing used.
- ✅ **Adaptive Rank**: Dynamically prunes LoRA rank to improve parameter efficiency.
## 📊 Training Metrics
- **Total Steps**: 130
- **Final Loss**: 0.44451914383814883
- **Trainable Params**: 83,886,080
## 📝 Algorithm Details
- **K-FAC Preconditioning** (Natural Gradient) and **Neural Reprojection** as per GRIT method.
- **Memory Efficient**: Covariance matrices on CPU to reduce GPU load.
## 🏆 Results
In benchmark comparisons, GRIT has shown **faster convergence and better stability** than standard LoRA or fine-tuning, making it well-suited for efficient single-epoch training. The use of Unsloth further accelerates this process.
## 📝 Citation
If you use this model, please cite the original GRIT paper and:
```bibtex
@misc{grit-lora-Llama-3.1-8B-hcc_original},
title={ meta-llama/Llama-3.1-8B Fine-tuned with GRIT on ParamDev/hcc_original },
author={te4bag},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/te4bag/GRIT-Full-MIMIC-llama-3.1-8B-Energy-0.9}
}
```
## ⚖️ License
This model inherits the Apache 2.0 license.
|
koloni/blockassist-bc-deadly_graceful_stingray_1755024729
|
koloni
| 2025-08-12T19:19:32Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:18:34Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF
|
wuav
| 2025-08-12T19:18:14Z
| 0
| 1
|
transformers
|
[
"transformers",
"gguf",
"vllm",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Jinx-org/Jinx-gpt-oss-20b",
"base_model:quantized:Jinx-org/Jinx-gpt-oss-20b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T19:17:05Z
|
---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model: Jinx-org/Jinx-gpt-oss-20b
tags:
- vllm
- llama-cpp
- gguf-my-repo
extra_gated_heading: You need to read and agree to the Disclaimer and User Agreementa
to access this model.
extra_gated_description: '
## Disclaimer and User Agreement
1. Introduction
Thank you for your interest in accessing this model (“the Model”).
Before you access, download, or use the Model or any derivative works, please read
and understand this Disclaimer and User Agreement (“Agreement”).
By checking “I have read and agree” and accessing the Model, you acknowledge that
you have read, understood, and agreed to all terms of this Agreement.
If you do not agree with any part of this Agreement, do not request or use the Model.
2. Nature of the Model & Risk Notice
The Model is trained using large-scale machine learning techniques and may generate
inaccurate, false, offensive, violent, sexual, discriminatory, politically sensitive,
or otherwise uncontrolled content.
The Model does not guarantee the accuracy, completeness, or legality of any generated
content. You must independently evaluate and verify the outputs, and you assume
all risks arising from their use.
The Model may reflect biases or errors present in its training data, potentially
producing inappropriate or controversial outputs.
3. License and Permitted Use
You may use the Model solely for lawful, compliant, and non-malicious purposes in
research, learning, experimentation, and development, in accordance with applicable
laws and regulations.
You must not use the Model for activities including, but not limited to:
Creating, distributing, or promoting unlawful, violent, pornographic, terrorist,
discriminatory, defamatory, or privacy-invasive content;
Any activity that could cause significant negative impact on individuals, groups,
organizations, or society;
High-risk applications such as automated decision-making, medical diagnosis, financial
transactions, or legal advice without proper validation and human oversight.
You must not remove, alter, or circumvent any safety mechanisms implemented in the
Model.
4. Data and Privacy
You are solely responsible for any data processed or generated when using the Model,
including compliance with data protection and privacy regulations.
The Model’s authors and contributors make no guarantees or warranties regarding
data security or privacy.
5. Limitation of Liability
To the maximum extent permitted by applicable law, the authors, contributors, and
their affiliated institutions shall not be liable for any direct, indirect, incidental,
or consequential damages arising from the use of the Model.
You agree to bear full legal responsibility for any disputes, claims, or litigation
arising from your use of the Model, and you release the authors and contributors
from any related liability.
6. Updates and Termination
This Agreement may be updated at any time, with updates posted on the Model’s page
and effective immediately upon publication.
If you violate this Agreement, the authors reserve the right to revoke your access
to the Model at any time.
I have read and fully understand this Disclaimer and User Agreement, and I accept
full responsibility for any consequences arising from my use of the Model.'
extra_gated_button_content: I've read and agree
---
# wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF
This model was converted to GGUF format from [`Jinx-org/Jinx-gpt-oss-20b`](https://huggingface.co/Jinx-org/Jinx-gpt-oss-20b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Jinx-org/Jinx-gpt-oss-20b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF --hf-file jinx-gpt-oss-20b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF --hf-file jinx-gpt-oss-20b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF --hf-file jinx-gpt-oss-20b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo wuav/Jinx-gpt-oss-20b-Q4_K_M-GGUF --hf-file jinx-gpt-oss-20b-q4_k_m.gguf -c 2048
```
|
chiachenwo/gpt_MLM_large
|
chiachenwo
| 2025-08-12T19:17:44Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T13:04:13Z
|
---
base_model: openai/gpt-oss-20b
library_name: transformers
model_name: gpt_MLM_large
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt_MLM_large
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
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="chiachenwo/gpt_MLM_large", 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/chiachenwo-northwestern-university/huggingface/runs/fg9pu96l)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.0.dev0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
liskil/KPG
|
liskil
| 2025-08-12T19:16:18Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T19:16:18Z
|
---
license: apache-2.0
---
|
roeker/blockassist-bc-quick_wiry_owl_1755026073
|
roeker
| 2025-08-12T19:15:20Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:15:11Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/II-Medical-32B-Preview-GGUF
|
mradermacher
| 2025-08-12T19:14:49Z
| 617
| 1
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Intelligent-Internet/II-Medical-32B-Preview",
"base_model:quantized:Intelligent-Internet/II-Medical-32B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-24T19:59:55Z
|
---
base_model: Intelligent-Internet/II-Medical-32B-Preview
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Intelligent-Internet/II-Medical-32B-Preview
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#II-Medical-32B-Preview-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/II-Medical-32B-Preview-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/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q3_K_M.gguf) | Q3_K_M | 16.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q4_K_M.gguf) | Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q5_K_M.gguf) | Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-32B-Preview-GGUF/resolve/main/II-Medical-32B-Preview.Q8_0.gguf) | Q8_0 | 34.9 | 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 -->
|
Jessica-Radcliffe-Orca-Attack-V-iral-Video/Jessica-Radcliffe.Orca.Attack.Viral.Video.Clip
|
Jessica-Radcliffe-Orca-Attack-V-iral-Video
| 2025-08-12T19:13:31Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-08-12T19:09:17Z
|
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jessica-Radcliffe-Orca-Attack">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jessica-Radcliffe-Orca-Attack">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jessica-Radcliffe-Orca-Attack"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div>
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755025855
|
Ferdi3425
| 2025-08-12T19:12:12Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:11:41Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jack-Payne1/qwen_2.5_7b-phoenix_T1_format_seed2
|
Jack-Payne1
| 2025-08-12T19:11:54Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T19:08:52Z
|
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Jack-Payne1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755025716
|
ggozzy
| 2025-08-12T19:09:59Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:09:43Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755025614
|
roeker
| 2025-08-12T19:07:53Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:07:45Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755025583
|
Ferdi3425
| 2025-08-12T19:07:42Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:07:13Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mozilla-ai/jina-embeddings-v2-small-en-off-topic
|
mozilla-ai
| 2025-08-12T19:07:09Z
| 3
| 0
| null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"text-classification",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-07T18:28:42Z
|
---
license: mit
pipeline_tag: text-classification
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:
For full documentation of this model, please see the official [model card](https://huggingface.co/govtech/jina-embeddings-v2-small-en-off-topic). They are the ones who built the model.
Mozilla AI has made it so you can call the `govtech/jina-embeddings-v2-small-en-off-topic` using `from_pretrained`. To do this, you'll need to first pull the `CrossEncoderWithSharedBase` model
architectuer from their model card and make sure to add `PyTorchModelHubMixin` as an inherited class. See this [article](https://huggingface.co/docs/hub/en/models-uploading#upload-a-pytorch-model-using-huggingfacehub)
Then, you can do the following:
```python
from transformers import AutoModel, AutoTokenizer
from huggingface_hub import PyTorchModelHubMixin
import torch.nn as nn
class Adapter(nn.Module):
def __init__(self, hidden_size):
super(Adapter, self).__init__()
self.down_project = nn.Linear(hidden_size, hidden_size // 2)
self.activation = nn.ReLU()
self.up_project = nn.Linear(hidden_size // 2, hidden_size)
def forward(self, x):
down = self.down_project(x)
activated = self.activation(down)
up = self.up_project(activated)
return up + x # Residual connection
class AttentionPooling(nn.Module):
def __init__(self, hidden_size):
super(AttentionPooling, self).__init__()
self.attention_weights = nn.Parameter(torch.randn(hidden_size))
def forward(self, hidden_states):
# hidden_states: [seq_len, batch_size, hidden_size]
scores = torch.matmul(hidden_states, self.attention_weights)
attention_weights = torch.softmax(scores, dim=0)
weighted_sum = torch.sum(attention_weights.unsqueeze(-1) * hidden_states, dim=0)
return weighted_sum
class CrossEncoderWithSharedBase(nn.Module, PyTorchModelHubMixin):
def __init__(self, base_model, num_labels=2, num_heads=8):
super(CrossEncoderWithSharedBase, self).__init__()
# Shared pre-trained model
self.shared_encoder = base_model
hidden_size = self.shared_encoder.config.hidden_size
# Sentence-specific adapters
self.adapter1 = Adapter(hidden_size)
self.adapter2 = Adapter(hidden_size)
# Cross-attention layers
self.cross_attention_1_to_2 = nn.MultiheadAttention(hidden_size, num_heads)
self.cross_attention_2_to_1 = nn.MultiheadAttention(hidden_size, num_heads)
# Attention pooling layers
self.attn_pooling_1_to_2 = AttentionPooling(hidden_size)
self.attn_pooling_2_to_1 = AttentionPooling(hidden_size)
# Projection layer with non-linearity
self.projection_layer = nn.Sequential(
nn.Linear(hidden_size * 2, hidden_size),
nn.ReLU()
)
# Classifier with three hidden layers
self.classifier = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_size // 2, hidden_size // 4),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_size // 4, num_labels)
)
def forward(self, input_ids1, attention_mask1, input_ids2, attention_mask2):
# Encode sentences
outputs1 = self.shared_encoder(input_ids1, attention_mask=attention_mask1)
outputs2 = self.shared_encoder(input_ids2, attention_mask=attention_mask2)
# Apply sentence-specific adapters
embeds1 = self.adapter1(outputs1.last_hidden_state)
embeds2 = self.adapter2(outputs2.last_hidden_state)
# Transpose for attention layers
embeds1 = embeds1.transpose(0, 1)
embeds2 = embeds2.transpose(0, 1)
# Cross-attention
cross_attn_1_to_2, _ = self.cross_attention_1_to_2(embeds1, embeds2, embeds2)
cross_attn_2_to_1, _ = self.cross_attention_2_to_1(embeds2, embeds1, embeds1)
# Attention pooling
pooled_1_to_2 = self.attn_pooling_1_to_2(cross_attn_1_to_2)
pooled_2_to_1 = self.attn_pooling_2_to_1(cross_attn_2_to_1)
# Concatenate and project
combined = torch.cat((pooled_1_to_2, pooled_2_to_1), dim=1)
projected = self.projection_layer(combined)
# Classification
logits = self.classifier(projected)
return logits
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-small-en")
base_model = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-small-en")
off_topic = CrossEncoderWithSharedBase.from_pretrained("mozilla-ai/jina-embeddings-v2-small-en", base_model=base_model)
# Then you can build a predict function that utilizes the tokenizer
def predict(model, tokenizer, sentence1, sentence2):
inputs1 = tokenizer(sentence1, return_tensors="pt", truncation=True, padding="max_length", max_length=max_length)
inputs2 = tokenizer(sentence2, return_tensors="pt", truncation=True, padding="max_length", max_length=max_length)
input_ids1 = inputs1['input_ids'].to(device)
attention_mask1 = inputs1['attention_mask'].to(device)
input_ids2 = inputs2['input_ids'].to(device)
attention_mask2 = inputs2['attention_mask'].to(device)
# Get outputs
with torch.no_grad():
outputs = model(input_ids1=input_ids1, attention_mask1=attention_mask1,
input_ids2=input_ids2, attention_mask2=attention_mask2)
probabilities = torch.softmax(outputs, dim=1)
predicted_label = torch.argmax(probabilities, dim=1).item()
return predicted_label, probabilities.cpu().numpy()
```
|
S-ophie-Rain-Spider-man-Video-Tutori-al/Sophie.Rain.Spiderman.Video.Tutorial
|
S-ophie-Rain-Spider-man-Video-Tutori-al
| 2025-08-12T19:06:36Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-08-12T19:04:29Z
|
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HD">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HD">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HD"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div>
|
Khanh2611/donut_v1
|
Khanh2611
| 2025-08-12T19:06:15Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-12T11:25:54Z
|
---
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]
|
aleebaster/blockassist-bc-sly_eager_boar_1755024466
|
aleebaster
| 2025-08-12T19:06:09Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:06:00Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755025410
|
ggozzy
| 2025-08-12T19:04:54Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:04:38Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Eden-L3.3-70b-0.1-GGUF
|
mradermacher
| 2025-08-12T19:04:53Z
| 157
| 0
|
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:BruhzEarth/Eden-L3.3-70b-0.1",
"base_model:quantized:BruhzEarth/Eden-L3.3-70b-0.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-29T20:15:17Z
|
---
base_model: BruhzEarth/Eden-L3.3-70b-0.1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/BruhzEarth/Eden-L3.3-70b-0.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Eden-L3.3-70b-0.1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-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/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eden-L3.3-70b-0.1-GGUF/resolve/main/Eden-L3.3-70b-0.1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | 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 -->
|
haryoaw/xlmr-base-massive-kd-1
|
haryoaw
| 2025-08-12T19:04:05Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-12T19:03:31Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
|
haryoaw/xlmr-base-massive-kd-0
|
haryoaw
| 2025-08-12T19:03:24Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-12T19:02:51Z
|
---
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]
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1755025265
|
kayacrypto
| 2025-08-12T19:03:14Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T19:02:54Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF
|
mradermacher
| 2025-08-12T19:01:57Z
| 369
| 0
|
transformers
|
[
"transformers",
"gguf",
"agent",
"tool-use",
"reinforcement-learning",
"qwen",
"llm",
"en",
"base_model:dongguanting/Qwen3-14B-ARPO-DeepSearch",
"base_model:quantized:dongguanting/Qwen3-14B-ARPO-DeepSearch",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
reinforcement-learning
| 2025-07-31T02:26:33Z
|
---
base_model: dongguanting/Qwen3-14B-ARPO-DeepSearch
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- agent
- tool-use
- reinforcement-learning
- qwen
- llm
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/dongguanting/Qwen3-14B-ARPO-DeepSearch
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-14B-ARPO-DeepSearch-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-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/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ1_M.gguf) | i1-IQ1_M | 3.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ2_M.gguf) | i1-IQ2_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-ARPO-DeepSearch-i1-GGUF/resolve/main/Qwen3-14B-ARPO-DeepSearch.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | 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 -->
|
mradermacher/Gemma-3-R1-12B-v1-i1-GGUF
|
mradermacher
| 2025-08-12T19:00:10Z
| 0
| 1
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TheDrummer/Gemma-3-R1-12B-v1",
"base_model:quantized:TheDrummer/Gemma-3-R1-12B-v1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-12T17:59:49Z
|
---
base_model: TheDrummer/Gemma-3-R1-12B-v1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/TheDrummer/Gemma-3-R1-12B-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Gemma-3-R1-12B-v1-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF
**This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-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/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ3_S.gguf) | i1-IQ3_S | 5.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q4_0.gguf) | i1-Q4_0 | 7.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q4_1.gguf) | i1-Q4_1 | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-i1-GGUF/resolve/main/Gemma-3-R1-12B-v1.i1-Q6_K.gguf) | i1-Q6_K | 9.8 | 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 -->
|
mradermacher/REMORX-UREX-GGUF
|
mradermacher
| 2025-08-12T19:00:07Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"grpo",
"en",
"base_model:pawin205/REMORX-UREX",
"base_model:quantized:pawin205/REMORX-UREX",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-12T18:38:48Z
|
---
base_model: pawin205/REMORX-UREX
language:
- en
library_name: transformers
model_name: REMORX-UREX
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- grpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/pawin205/REMORX-UREX
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#REMORX-UREX-GGUF).***
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/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/REMORX-UREX-GGUF/resolve/main/REMORX-UREX.f16.gguf) | f16 | 15.3 | 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.
<!-- end -->
|
vengky/blockassist-bc-wild_gentle_manatee_1755022629
|
vengky
| 2025-08-12T18:58:53Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild gentle manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:58:43Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild gentle manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aramzz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wild_stalking_lemur
|
aramzz
| 2025-08-12T18:58:10Z
| 24
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am wild stalking lemur",
"unsloth",
"trl",
"genrl-swarm",
"I am wild_stalking_lemur",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-07T13:07:53Z
|
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wild_stalking_lemur
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am wild stalking lemur
- unsloth
- trl
- genrl-swarm
- I am wild_stalking_lemur
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wild_stalking_lemur
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="aramzz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wild_stalking_lemur", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/Malaysian-TTS-1.7B-GGUF
|
mradermacher
| 2025-08-12T18:57:27Z
| 179
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:mesolitica/Malaysian-TTS-1.7B-v0.1",
"base_model:quantized:mesolitica/Malaysian-TTS-1.7B-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-02T05:19:29Z
|
---
base_model: mesolitica/Malaysian-TTS-1.7B-v0.1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/mesolitica/Malaysian-TTS-1.7B-v0.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Malaysian-TTS-1.7B-GGUF).***
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/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Malaysian-TTS-1.7B-GGUF/resolve/main/Malaysian-TTS-1.7B.f16.gguf) | f16 | 3.7 | 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.
<!-- end -->
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755023939
|
Sayemahsjn
| 2025-08-12T18:56:56Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:56:48Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rahulseetharaman/reranker-bert-uncased_L-12_H-768_A-12-msmarco-bce-1m
|
rahulseetharaman
| 2025-08-12T18:54:50Z
| 0
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"cross-encoder",
"reranker",
"generated_from_trainer",
"dataset_size:990000",
"loss:BinaryCrossEntropyLoss",
"text-ranking",
"en",
"dataset:sentence-transformers/msmarco",
"arxiv:1908.10084",
"base_model:bansalaman18/bert-uncased_L-12_H-768_A-12",
"base_model:finetune:bansalaman18/bert-uncased_L-12_H-768_A-12",
"model-index",
"region:us"
] |
text-ranking
| 2025-08-12T18:54:38Z
|
---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:990000
- loss:BinaryCrossEntropyLoss
base_model: bansalaman18/bert-uncased_L-12_H-768_A-12
datasets:
- sentence-transformers/msmarco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: CrossEncoder based on bansalaman18/bert-uncased_L-12_H-768_A-12
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.1343
name: Map
- type: mrr@10
value: 0.1068
name: Mrr@10
- type: ndcg@10
value: 0.1284
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.2869
name: Map
- type: mrr@10
value: 0.4272
name: Mrr@10
- type: ndcg@10
value: 0.2637
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.1478
name: Map
- type: mrr@10
value: 0.1285
name: Mrr@10
- type: ndcg@10
value: 0.1597
name: Ndcg@10
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R100 mean
type: NanoBEIR_R100_mean
metrics:
- type: map
value: 0.1897
name: Map
- type: mrr@10
value: 0.2208
name: Mrr@10
- type: ndcg@10
value: 0.184
name: Ndcg@10
---
# CrossEncoder based on bansalaman18/bert-uncased_L-12_H-768_A-12
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [bansalaman18/bert-uncased_L-12_H-768_A-12](https://huggingface.co/bansalaman18/bert-uncased_L-12_H-768_A-12) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [bansalaman18/bert-uncased_L-12_H-768_A-12](https://huggingface.co/bansalaman18/bert-uncased_L-12_H-768_A-12) <!-- at revision f4e5e9b768dfff7d448a3e4b88785934c95f3e5a -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("rahulseetharaman/reranker-bert-uncased_L-12_H-768_A-12-msmarco-bce-1m")
# Get scores for pairs of texts
pairs = [
['star voyager cast', 'On August 25, 2012, data from Voyager 1 indicated that it had become the first human-made object to enter interstellar space, traveling further than anyone, or anything, in history. As of 2013, Voyager 1 was moving with a velocity of 17 kilometers per second (11 mi/s) relative to the Sun. Voyager 2 is expected to enter interstellar space by 2016, and its plasma spectrometer should provide the first direct measurements of the density and temperature of the interstellar plasma.'],
['physiologist who conducted the classical conditioning studies with dogs', "Classical Conditioning. The people who fed Pavlov's dogs wore lab coats. Pavlov noticed that the dogs began to drool whenever they saw lab coats, even if there was no food in sight. Pavlov wondered why the dogs salivated at lab coats, and not just at food."],
['is a written check considered a contract', 'If you bought a flat screen TV and are paying it off monthly, thatâ\x80\x99s considered recurring debt because you canâ\x80\x99t easily cancel your payments. If you subscribe to a magazine or have an Internet or phone contract, those obligations are not considered recurring debt because there is no fixed debt amount you are paying down and you can cancel your contract.'],
['definition of an actuator', 'An actuator is a type of motor that is responsible for moving or controlling a mechanism or system. It is operated by a source of energy, typically electric current, hydraulic fluid pressure, or pneumatic pressure, and converts that energy into motion. An actuator is the mechanism by which a control system acts upon an environment. The control system can be simple (a fixed mechanical or electronic system), software-based (e.g. a printer driver, robot control system), a human, or any other input.'],
['what are rheumatoid arthritis symptoms', 'While early RA symptoms can be mimicked by other diseases, the symptoms and signs are very characteristic of rheumatoid disease. The 15 early rheumatoid arthritis symptoms and signs discussed in this article include the following: Fatigue. Joint pain. Joint tenderness. Joint swelling. Joint redness. Joint warmth.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'star voyager cast',
[
'On August 25, 2012, data from Voyager 1 indicated that it had become the first human-made object to enter interstellar space, traveling further than anyone, or anything, in history. As of 2013, Voyager 1 was moving with a velocity of 17 kilometers per second (11 mi/s) relative to the Sun. Voyager 2 is expected to enter interstellar space by 2016, and its plasma spectrometer should provide the first direct measurements of the density and temperature of the interstellar plasma.',
"Classical Conditioning. The people who fed Pavlov's dogs wore lab coats. Pavlov noticed that the dogs began to drool whenever they saw lab coats, even if there was no food in sight. Pavlov wondered why the dogs salivated at lab coats, and not just at food.",
'If you bought a flat screen TV and are paying it off monthly, thatâ\x80\x99s considered recurring debt because you canâ\x80\x99t easily cancel your payments. If you subscribe to a magazine or have an Internet or phone contract, those obligations are not considered recurring debt because there is no fixed debt amount you are paying down and you can cancel your contract.',
'An actuator is a type of motor that is responsible for moving or controlling a mechanism or system. It is operated by a source of energy, typically electric current, hydraulic fluid pressure, or pneumatic pressure, and converts that energy into motion. An actuator is the mechanism by which a control system acts upon an environment. The control system can be simple (a fixed mechanical or electronic system), software-based (e.g. a printer driver, robot control system), a human, or any other input.',
'While early RA symptoms can be mimicked by other diseases, the symptoms and signs are very characteristic of rheumatoid disease. The 15 early rheumatoid arthritis symptoms and signs discussed in this article include the following: Fatigue. Joint pain. Joint tenderness. Joint swelling. Joint redness. Joint warmth.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.1343 (-0.3553) | 0.2869 (+0.0260) | 0.1478 (-0.2718) |
| mrr@10 | 0.1068 (-0.3707) | 0.4272 (-0.0726) | 0.1285 (-0.2982) |
| **ndcg@10** | **0.1284 (-0.4120)** | **0.2637 (-0.0614)** | **0.1597 (-0.3409)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.1897 (-0.2004) |
| mrr@10 | 0.2208 (-0.2472) |
| **ndcg@10** | **0.1840 (-0.2714)** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 990,000 training samples
* Columns: <code>query</code>, <code>passage</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage | score |
|:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 10 characters</li><li>mean: 34.06 characters</li><li>max: 103 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 341.02 characters</li><li>max: 943 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| query | passage | score |
|:----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>can a urinalysis detect kidney disease</code> | <code>A urinalysis is a simple, inexpensive test that can help to detect problems in many parts of the body, including the kidneys, urinary tract, heart, and liver. A urinalysis can include a visual examination of a urine sample, microscopic examination, and a dipstick test.</code> | <code>1.0</code> |
| <code>what is the hamsa hand</code> | <code>Answer by Mikereptile. Confidence votes 60. It takes about 6-8 weeks to heal, depending on the type of injury sustained. (Keep the finger as still as you can !!!!). When broken place the hand in ice cold water for about 5 min.s then take it and wrap the whole hand in a soft wrap (anything that is a soft colth) .Take the wraped hand and hold it above or upright to the head.hen broken place the hand in ice cold water for about 5 min.s then take it and wrap the whole hand in a soft wrap (anything that is a soft colth) . Take the wraped hand and hold it above or upright to the head.</code> | <code>0.0</code> |
| <code>was white tiger in the us zoo killed?</code> | <code>Cubby is a male American black bear born at the Chahinkapa Zoo in North Dakota. He was transferred to the Hogle Zoo (Salt Lake City, Utah) in 2003, and arrived at the Oregon Zoo in May 2010.</code> | <code>0.0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Evaluation Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 10,000 evaluation samples
* Columns: <code>query</code>, <code>passage</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage | score |
|:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 10 characters</li><li>mean: 33.97 characters</li><li>max: 121 characters</li></ul> | <ul><li>min: 70 characters</li><li>mean: 345.9 characters</li><li>max: 946 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| query | passage | score |
|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>star voyager cast</code> | <code>On August 25, 2012, data from Voyager 1 indicated that it had become the first human-made object to enter interstellar space, traveling further than anyone, or anything, in history. As of 2013, Voyager 1 was moving with a velocity of 17 kilometers per second (11 mi/s) relative to the Sun. Voyager 2 is expected to enter interstellar space by 2016, and its plasma spectrometer should provide the first direct measurements of the density and temperature of the interstellar plasma.</code> | <code>0.0</code> |
| <code>physiologist who conducted the classical conditioning studies with dogs</code> | <code>Classical Conditioning. The people who fed Pavlov's dogs wore lab coats. Pavlov noticed that the dogs began to drool whenever they saw lab coats, even if there was no food in sight. Pavlov wondered why the dogs salivated at lab coats, and not just at food.</code> | <code>0.0</code> |
| <code>is a written check considered a contract</code> | <code>If you bought a flat screen TV and are paying it off monthly, thatâs considered recurring debt because you canât easily cancel your payments. If you subscribe to a magazine or have an Internet or phone contract, those obligations are not considered recurring debt because there is no fixed debt amount you are paying down and you can cancel your contract.</code> | <code>0.0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 4
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `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`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:----------:|:---------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.0355 (-0.5049) | 0.2692 (-0.0558) | 0.0305 (-0.4701) | 0.1118 (-0.3436) |
| 0.0000 | 1 | 0.7222 | - | - | - | - | - |
| 0.0162 | 1000 | 0.6948 | 0.6929 | 0.0568 (-0.4836) | 0.2595 (-0.0656) | 0.0188 (-0.4818) | 0.1117 (-0.3437) |
| 0.0323 | 2000 | 0.6944 | 0.6925 | 0.0402 (-0.5002) | 0.2539 (-0.0711) | 0.0483 (-0.4524) | 0.1141 (-0.3412) |
| 0.0485 | 3000 | 0.6948 | 0.6900 | 0.0484 (-0.4921) | 0.2392 (-0.0859) | 0.0484 (-0.4523) | 0.1120 (-0.3434) |
| 0.0646 | 4000 | 0.6939 | 0.6870 | 0.0342 (-0.5062) | 0.2502 (-0.0748) | 0.0680 (-0.4327) | 0.1175 (-0.3379) |
| 0.0808 | 5000 | 0.69 | 0.6868 | 0.0606 (-0.4798) | 0.2497 (-0.0753) | 0.0763 (-0.4243) | 0.1289 (-0.3265) |
| 0.0970 | 6000 | 0.6868 | 0.6766 | 0.0543 (-0.4861) | 0.2752 (-0.0499) | 0.1036 (-0.3970) | 0.1444 (-0.3110) |
| 0.1131 | 7000 | 0.6831 | 0.6880 | 0.0658 (-0.4746) | 0.2700 (-0.0550) | 0.1128 (-0.3879) | 0.1495 (-0.3058) |
| 0.1293 | 8000 | 0.6749 | 0.6864 | 0.0875 (-0.4529) | 0.2720 (-0.0531) | 0.1081 (-0.3926) | 0.1559 (-0.2995) |
| 0.1455 | 9000 | 0.6766 | 0.6635 | 0.0731 (-0.4673) | 0.2705 (-0.0545) | 0.1106 (-0.3900) | 0.1514 (-0.3039) |
| 0.1616 | 10000 | 0.6732 | 0.6690 | 0.0877 (-0.4527) | 0.2708 (-0.0542) | 0.1336 (-0.3671) | 0.1640 (-0.2913) |
| 0.1778 | 11000 | 0.6689 | 0.6506 | 0.0928 (-0.4476) | 0.2603 (-0.0648) | 0.0746 (-0.4261) | 0.1426 (-0.3128) |
| 0.1939 | 12000 | 0.6662 | 0.6495 | 0.0990 (-0.4414) | 0.2705 (-0.0546) | 0.1219 (-0.3788) | 0.1638 (-0.2916) |
| 0.2101 | 13000 | 0.6608 | 0.6430 | 0.1232 (-0.4172) | 0.2817 (-0.0434) | 0.0758 (-0.4248) | 0.1602 (-0.2951) |
| 0.2263 | 14000 | 0.66 | 0.6630 | 0.1147 (-0.4257) | 0.2880 (-0.0370) | 0.1215 (-0.3791) | 0.1748 (-0.2806) |
| 0.2424 | 15000 | 0.672 | 0.6576 | 0.1084 (-0.4320) | 0.2582 (-0.0668) | 0.0922 (-0.4085) | 0.1529 (-0.3024) |
| 0.2586 | 16000 | 0.671 | 0.6382 | 0.0831 (-0.4573) | 0.2583 (-0.0668) | 0.0993 (-0.4014) | 0.1469 (-0.3085) |
| 0.2747 | 17000 | 0.6933 | 0.6594 | 0.1126 (-0.4278) | 0.2676 (-0.0574) | 0.1012 (-0.3995) | 0.1605 (-0.2949) |
| 0.2909 | 18000 | 0.6762 | 0.6848 | 0.1071 (-0.4333) | 0.2832 (-0.0418) | 0.0839 (-0.4167) | 0.1581 (-0.2973) |
| **0.3071** | **19000** | **0.6762** | **0.6577** | **0.1284 (-0.4120)** | **0.2637 (-0.0614)** | **0.1597 (-0.3409)** | **0.1840 (-0.2714)** |
| 0.3232 | 20000 | 0.6682 | 0.6640 | 0.1147 (-0.4257) | 0.2469 (-0.0782) | 0.0936 (-0.4071) | 0.1517 (-0.3037) |
| 0.3394 | 21000 | 0.6798 | 0.6860 | 0.0714 (-0.4690) | 0.2566 (-0.0684) | 0.0905 (-0.4102) | 0.1395 (-0.3159) |
| 0.3556 | 22000 | 0.6899 | 0.6951 | 0.0678 (-0.4726) | 0.2604 (-0.0647) | 0.0789 (-0.4217) | 0.1357 (-0.3197) |
| 0.3717 | 23000 | 0.6956 | 0.6934 | 0.0740 (-0.4664) | 0.2632 (-0.0618) | 0.0784 (-0.4222) | 0.1386 (-0.3168) |
| 0.3879 | 24000 | 0.6962 | 0.6917 | 0.0410 (-0.4994) | 0.2567 (-0.0684) | 0.0225 (-0.4781) | 0.1067 (-0.3486) |
| 0.4040 | 25000 | 0.6978 | 0.6954 | 0.0451 (-0.4953) | 0.2461 (-0.0789) | 0.0282 (-0.4724) | 0.1065 (-0.3489) |
| 0.4202 | 26000 | 0.6977 | 0.6972 | 0.0449 (-0.4955) | 0.2364 (-0.0886) | 0.0279 (-0.4727) | 0.1031 (-0.3523) |
| 0.4364 | 27000 | 0.6971 | 0.6943 | 0.0473 (-0.4932) | 0.2566 (-0.0684) | 0.0477 (-0.4529) | 0.1172 (-0.3382) |
| 0.4525 | 28000 | 0.6953 | 0.6931 | 0.0467 (-0.4937) | 0.2613 (-0.0637) | 0.0362 (-0.4645) | 0.1147 (-0.3406) |
| 0.4687 | 29000 | 0.6968 | 0.6940 | 0.0467 (-0.4938) | 0.2566 (-0.0685) | 0.0474 (-0.4533) | 0.1169 (-0.3385) |
| 0.4848 | 30000 | 0.6963 | 0.6931 | 0.0476 (-0.4928) | 0.2549 (-0.0702) | 0.0422 (-0.4584) | 0.1149 (-0.3405) |
| 0.5010 | 31000 | 0.6963 | 0.6944 | 0.0455 (-0.4949) | 0.2562 (-0.0688) | 0.0459 (-0.4547) | 0.1159 (-0.3395) |
| 0.5172 | 32000 | 0.6958 | 0.6958 | 0.0270 (-0.5134) | 0.2789 (-0.0461) | 0.0283 (-0.4724) | 0.1114 (-0.3440) |
| 0.5333 | 33000 | 0.6955 | 0.6956 | 0.0502 (-0.4903) | 0.2581 (-0.0669) | 0.0292 (-0.4715) | 0.1125 (-0.3429) |
| 0.5495 | 34000 | 0.6952 | 0.6931 | 0.0441 (-0.4964) | 0.2559 (-0.0692) | 0.0418 (-0.4589) | 0.1139 (-0.3415) |
| 0.5657 | 35000 | 0.695 | 0.6931 | 0.0306 (-0.5098) | 0.2567 (-0.0684) | 0.0365 (-0.4642) | 0.1079 (-0.3475) |
| 0.5818 | 36000 | 0.6953 | 0.7034 | 0.0456 (-0.4948) | 0.2563 (-0.0687) | 0.0468 (-0.4538) | 0.1162 (-0.3391) |
| 0.5980 | 37000 | 0.6952 | 0.6960 | 0.0493 (-0.4912) | 0.2511 (-0.0739) | 0.0470 (-0.4536) | 0.1158 (-0.3396) |
| 0.6141 | 38000 | 0.6953 | 0.6932 | 0.0517 (-0.4887) | 0.2592 (-0.0658) | 0.0372 (-0.4635) | 0.1160 (-0.3393) |
| 0.6303 | 39000 | 0.6946 | 0.6931 | 0.0471 (-0.4933) | 0.2581 (-0.0670) | 0.0496 (-0.4511) | 0.1182 (-0.3371) |
| 0.6465 | 40000 | 0.6949 | 0.6941 | 0.0483 (-0.4921) | 0.2557 (-0.0694) | 0.0577 (-0.4429) | 0.1206 (-0.3348) |
| 0.6626 | 41000 | 0.6949 | 0.6967 | 0.0500 (-0.4904) | 0.2562 (-0.0689) | 0.0517 (-0.4490) | 0.1193 (-0.3361) |
| 0.6788 | 42000 | 0.6945 | 0.6944 | 0.0334 (-0.5070) | 0.2627 (-0.0623) | 0.0566 (-0.4440) | 0.1176 (-0.3378) |
| 0.6949 | 43000 | 0.6952 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.7111 | 44000 | 0.6942 | 0.6931 | 0.0439 (-0.4966) | 0.2567 (-0.0683) | 0.0473 (-0.4533) | 0.1160 (-0.3394) |
| 0.7273 | 45000 | 0.6944 | 0.6985 | 0.0556 (-0.4848) | 0.2413 (-0.0837) | 0.0467 (-0.4540) | 0.1145 (-0.3408) |
| 0.7434 | 46000 | 0.6942 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.7596 | 47000 | 0.6941 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.7758 | 48000 | 0.6937 | 0.6962 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.7919 | 49000 | 0.6946 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.8081 | 50000 | 0.6942 | 0.6931 | 0.0443 (-0.4961) | 0.2590 (-0.0661) | 0.0449 (-0.4557) | 0.1161 (-0.3393) |
| 0.8242 | 51000 | 0.6945 | 0.6955 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.8404 | 52000 | 0.694 | 0.6932 | 0.0419 (-0.4985) | 0.2498 (-0.0753) | 0.0433 (-0.4573) | 0.1117 (-0.3437) |
| 0.8566 | 53000 | 0.6941 | 0.6941 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.8727 | 54000 | 0.6941 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.8889 | 55000 | 0.6942 | 0.6939 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.9051 | 56000 | 0.6939 | 0.6942 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.9212 | 57000 | 0.6939 | 0.6932 | 0.0456 (-0.4948) | 0.2552 (-0.0698) | 0.0471 (-0.4536) | 0.1160 (-0.3394) |
| 0.9374 | 58000 | 0.6937 | 0.6948 | 0.0446 (-0.4958) | 0.2561 (-0.0689) | 0.0469 (-0.4538) | 0.1159 (-0.3395) |
| 0.9535 | 59000 | 0.6941 | 0.6931 | 0.0390 (-0.5014) | 0.2625 (-0.0626) | 0.0526 (-0.4480) | 0.1180 (-0.3373) |
| 0.9697 | 60000 | 0.6938 | 0.6964 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 0.9859 | 61000 | 0.6944 | 0.6932 | 0.0450 (-0.4954) | 0.2805 (-0.0445) | 0.0460 (-0.4547) | 0.1238 (-0.3315) |
| 1.0020 | 62000 | 0.6937 | 0.6935 | 0.0815 (-0.4589) | 0.2565 (-0.0685) | 0.0184 (-0.4822) | 0.1188 (-0.3365) |
| 1.0182 | 63000 | 0.6939 | 0.6951 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.0343 | 64000 | 0.6939 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.0505 | 65000 | 0.6939 | 0.6931 | 0.0381 (-0.5023) | 0.2567 (-0.0683) | 0.0479 (-0.4527) | 0.1143 (-0.3411) |
| 1.0667 | 66000 | 0.6934 | 0.6943 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0466 (-0.4540) | 0.1161 (-0.3393) |
| 1.0828 | 67000 | 0.6938 | 0.6932 | 0.0411 (-0.4993) | 0.2583 (-0.0667) | 0.0429 (-0.4578) | 0.1141 (-0.3413) |
| 1.0990 | 68000 | 0.6941 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.1152 | 69000 | 0.6937 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.1313 | 70000 | 0.6938 | 0.6932 | 0.0446 (-0.4959) | 0.2566 (-0.0684) | 0.0495 (-0.4512) | 0.1169 (-0.3385) |
| 1.1475 | 71000 | 0.6934 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.1636 | 72000 | 0.6933 | 0.6939 | 0.0232 (-0.5173) | 0.2471 (-0.0779) | 0.0308 (-0.4698) | 0.1004 (-0.3550) |
| 1.1798 | 73000 | 0.6937 | 0.6931 | 0.0449 (-0.4956) | 0.2566 (-0.0684) | 0.0462 (-0.4545) | 0.1159 (-0.3395) |
| 1.1960 | 74000 | 0.694 | 0.6934 | 0.0541 (-0.4863) | 0.2425 (-0.0825) | 0.0361 (-0.4646) | 0.1109 (-0.3445) |
| 1.2121 | 75000 | 0.6937 | 0.6931 | 0.0649 (-0.4755) | 0.2555 (-0.0695) | 0.0318 (-0.4689) | 0.1174 (-0.3380) |
| 1.2283 | 76000 | 0.6939 | 0.6942 | 0.0833 (-0.4571) | 0.2697 (-0.0554) | 0.0497 (-0.4510) | 0.1342 (-0.3212) |
| 1.2444 | 77000 | 0.6939 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.2606 | 78000 | 0.6938 | 0.6931 | 0.0501 (-0.4903) | 0.2584 (-0.0666) | 0.0525 (-0.4481) | 0.1203 (-0.3350) |
| 1.2768 | 79000 | 0.6938 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.2929 | 80000 | 0.6936 | 0.6931 | 0.0412 (-0.4992) | 0.2719 (-0.0531) | 0.0444 (-0.4562) | 0.1192 (-0.3362) |
| 1.3091 | 81000 | 0.6939 | 0.6931 | 0.0437 (-0.4967) | 0.2512 (-0.0739) | 0.0443 (-0.4563) | 0.1131 (-0.3423) |
| 1.3253 | 82000 | 0.6936 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.3414 | 83000 | 0.6935 | 0.6933 | 0.0488 (-0.4916) | 0.2585 (-0.0665) | 0.0486 (-0.4520) | 0.1186 (-0.3367) |
| 1.3576 | 84000 | 0.6937 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.3737 | 85000 | 0.6935 | 0.6941 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.3899 | 86000 | 0.6938 | 0.6949 | 0.0437 (-0.4967) | 0.2567 (-0.0683) | 0.0473 (-0.4533) | 0.1159 (-0.3394) |
| 1.4061 | 87000 | 0.6937 | 0.6934 | 0.0454 (-0.4950) | 0.2560 (-0.0691) | 0.0468 (-0.4538) | 0.1161 (-0.3393) |
| 1.4222 | 88000 | 0.6937 | 0.6931 | 0.0390 (-0.5014) | 0.2537 (-0.0714) | 0.0515 (-0.4491) | 0.1147 (-0.3406) |
| 1.4384 | 89000 | 0.6936 | 0.6945 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.4545 | 90000 | 0.6936 | 0.6932 | 0.0479 (-0.4925) | 0.2633 (-0.0618) | 0.0508 (-0.4498) | 0.1207 (-0.3347) |
| 1.4707 | 91000 | 0.6935 | 0.6933 | 0.0468 (-0.4937) | 0.2564 (-0.0686) | 0.0461 (-0.4545) | 0.1164 (-0.3389) |
| 1.4869 | 92000 | 0.6935 | 0.6932 | 0.0462 (-0.4942) | 0.2564 (-0.0687) | 0.0472 (-0.4534) | 0.1166 (-0.3388) |
| 1.5030 | 93000 | 0.6936 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.5192 | 94000 | 0.6937 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.5354 | 95000 | 0.6934 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.5515 | 96000 | 0.6936 | 0.6933 | 0.0454 (-0.4950) | 0.2563 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.5677 | 97000 | 0.6937 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.5838 | 98000 | 0.6936 | 0.6936 | 0.0479 (-0.4925) | 0.2600 (-0.0651) | 0.0226 (-0.4780) | 0.1102 (-0.3452) |
| 1.6 | 99000 | 0.6936 | 0.6939 | 0.0472 (-0.4932) | 0.2565 (-0.0685) | 0.0484 (-0.4522) | 0.1174 (-0.3380) |
| 1.6162 | 100000 | 0.6935 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.6323 | 101000 | 0.6935 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.6485 | 102000 | 0.6936 | 0.6931 | 0.0424 (-0.4980) | 0.2491 (-0.0760) | 0.0303 (-0.4704) | 0.1073 (-0.3481) |
| 1.6646 | 103000 | 0.6936 | 0.6935 | 0.0467 (-0.4937) | 0.2573 (-0.0678) | 0.0477 (-0.4529) | 0.1172 (-0.3381) |
| 1.6808 | 104000 | 0.6935 | 0.6931 | 0.0450 (-0.4954) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1160 (-0.3393) |
| 1.6970 | 105000 | 0.6935 | 0.6939 | 0.0384 (-0.5020) | 0.2461 (-0.0790) | 0.0304 (-0.4703) | 0.1050 (-0.3504) |
| 1.7131 | 106000 | 0.6935 | 0.6932 | 0.0455 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.7293 | 107000 | 0.6936 | 0.6936 | 0.0462 (-0.4942) | 0.2546 (-0.0704) | 0.0463 (-0.4543) | 0.1157 (-0.3397) |
| 1.7455 | 108000 | 0.6935 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.7616 | 109000 | 0.6933 | 0.6934 | 0.0462 (-0.4942) | 0.2556 (-0.0694) | 0.0498 (-0.4509) | 0.1172 (-0.3382) |
| 1.7778 | 110000 | 0.6934 | 0.6934 | 0.0383 (-0.5022) | 0.2390 (-0.0860) | 0.0391 (-0.4616) | 0.1055 (-0.3499) |
| 1.7939 | 111000 | 0.6935 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.8101 | 112000 | 0.6934 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.8263 | 113000 | 0.6938 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.8424 | 114000 | 0.6937 | 0.6931 | 0.0460 (-0.4944) | 0.2564 (-0.0686) | 0.0482 (-0.4524) | 0.1169 (-0.3385) |
| 1.8586 | 115000 | 0.6935 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.8747 | 116000 | 0.6932 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.8909 | 117000 | 0.6935 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.9071 | 118000 | 0.6935 | 0.6935 | 0.0215 (-0.5189) | 0.2637 (-0.0614) | 0.0311 (-0.4695) | 0.1054 (-0.3499) |
| 1.9232 | 119000 | 0.6933 | 0.6951 | 0.0185 (-0.5219) | 0.2301 (-0.0949) | 0.0419 (-0.4588) | 0.0968 (-0.3586) |
| 1.9394 | 120000 | 0.6935 | 0.6935 | 0.0509 (-0.4896) | 0.2444 (-0.0807) | 0.0468 (-0.4539) | 0.1140 (-0.3414) |
| 1.9556 | 121000 | 0.6932 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.9717 | 122000 | 0.6936 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 1.9879 | 123000 | 0.6936 | 0.6937 | 0.0466 (-0.4939) | 0.2563 (-0.0688) | 0.0470 (-0.4536) | 0.1166 (-0.3388) |
| 2.0040 | 124000 | 0.6934 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.0202 | 125000 | 0.6933 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.0364 | 126000 | 0.6935 | 0.6931 | 0.0452 (-0.4952) | 0.2512 (-0.0739) | 0.0460 (-0.4546) | 0.1142 (-0.3412) |
| 2.0525 | 127000 | 0.6933 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.0687 | 128000 | 0.6935 | 0.6947 | 0.0454 (-0.4950) | 0.2521 (-0.0730) | 0.0464 (-0.4542) | 0.1147 (-0.3407) |
| 2.0848 | 129000 | 0.6934 | 0.6943 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.1010 | 130000 | 0.6932 | 0.6942 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.1172 | 131000 | 0.6934 | 0.6941 | 0.0457 (-0.4947) | 0.2562 (-0.0689) | 0.0464 (-0.4542) | 0.1161 (-0.3393) |
| 2.1333 | 132000 | 0.6933 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.1495 | 133000 | 0.6933 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.1657 | 134000 | 0.6933 | 0.6943 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.1818 | 135000 | 0.6934 | 0.6940 | 0.0454 (-0.4950) | 0.2510 (-0.0740) | 0.0466 (-0.4540) | 0.1144 (-0.3410) |
| 2.1980 | 136000 | 0.6933 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.2141 | 137000 | 0.6933 | 0.6937 | 0.0455 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.2303 | 138000 | 0.6935 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.2465 | 139000 | 0.6934 | 0.6952 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.2626 | 140000 | 0.6935 | 0.6932 | 0.0455 (-0.4949) | 0.2562 (-0.0689) | 0.0469 (-0.4538) | 0.1162 (-0.3392) |
| 2.2788 | 141000 | 0.6937 | 0.6939 | 0.0454 (-0.4950) | 0.2568 (-0.0683) | 0.0456 (-0.4550) | 0.1159 (-0.3394) |
| 2.2949 | 142000 | 0.6934 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.3111 | 143000 | 0.6933 | 0.6935 | 0.0267 (-0.5137) | 0.2617 (-0.0634) | 0.0826 (-0.4180) | 0.1237 (-0.3317) |
| 2.3273 | 144000 | 0.6935 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.3434 | 145000 | 0.6934 | 0.6931 | 0.0473 (-0.4931) | 0.2557 (-0.0694) | 0.0484 (-0.4522) | 0.1172 (-0.3382) |
| 2.3596 | 146000 | 0.6934 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.3758 | 147000 | 0.6935 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.3919 | 148000 | 0.6933 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.4081 | 149000 | 0.6933 | 0.6938 | 0.0543 (-0.4861) | 0.2533 (-0.0717) | 0.0445 (-0.4561) | 0.1174 (-0.3380) |
| 2.4242 | 150000 | 0.6935 | 0.6933 | 0.0402 (-0.5002) | 0.2562 (-0.0688) | 0.0446 (-0.4560) | 0.1137 (-0.3417) |
| 2.4404 | 151000 | 0.6934 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.4566 | 152000 | 0.6934 | 0.6931 | 0.0426 (-0.4978) | 0.2122 (-0.1128) | 0.0361 (-0.4645) | 0.0970 (-0.3584) |
| 2.4727 | 153000 | 0.6935 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.4889 | 154000 | 0.6933 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.5051 | 155000 | 0.6934 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.5212 | 156000 | 0.6933 | 0.6935 | 0.0469 (-0.4935) | 0.2585 (-0.0665) | 0.0432 (-0.4575) | 0.1162 (-0.3392) |
| 2.5374 | 157000 | 0.6934 | 0.6935 | 0.0429 (-0.4976) | 0.2641 (-0.0610) | 0.0462 (-0.4545) | 0.1177 (-0.3377) |
| 2.5535 | 158000 | 0.6934 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.5697 | 159000 | 0.6933 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.5859 | 160000 | 0.6933 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.6020 | 161000 | 0.6933 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.6182 | 162000 | 0.6931 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.6343 | 163000 | 0.6934 | 0.6938 | 0.0582 (-0.4822) | 0.2534 (-0.0716) | 0.0465 (-0.4541) | 0.1194 (-0.3360) |
| 2.6505 | 164000 | 0.6931 | 0.6933 | 0.0261 (-0.5143) | 0.2586 (-0.0664) | 0.0376 (-0.4631) | 0.1074 (-0.3479) |
| 2.6667 | 165000 | 0.6932 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.6828 | 166000 | 0.6933 | 0.6939 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.6990 | 167000 | 0.6932 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.7152 | 168000 | 0.6932 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.7313 | 169000 | 0.6933 | 0.6946 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.7475 | 170000 | 0.6934 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.7636 | 171000 | 0.6932 | 0.6941 | 0.0303 (-0.5101) | 0.2541 (-0.0710) | 0.0364 (-0.4643) | 0.1069 (-0.3485) |
| 2.7798 | 172000 | 0.6931 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.7960 | 173000 | 0.6935 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.8121 | 174000 | 0.6932 | 0.6933 | 0.0473 (-0.4931) | 0.2568 (-0.0682) | 0.0481 (-0.4526) | 0.1174 (-0.3380) |
| 2.8283 | 175000 | 0.693 | 0.6933 | 0.0492 (-0.4912) | 0.2569 (-0.0682) | 0.0450 (-0.4557) | 0.1170 (-0.3384) |
| 2.8444 | 176000 | 0.6933 | 0.6945 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.8606 | 177000 | 0.6931 | 0.6931 | 0.0455 (-0.4949) | 0.2564 (-0.0687) | 0.0469 (-0.4538) | 0.1163 (-0.3391) |
| 2.8768 | 178000 | 0.6933 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.8929 | 179000 | 0.6929 | 0.6940 | 0.0597 (-0.4807) | 0.2604 (-0.0647) | 0.0581 (-0.4425) | 0.1261 (-0.3293) |
| 2.9091 | 180000 | 0.6933 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.9253 | 181000 | 0.6932 | 0.6945 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.9414 | 182000 | 0.6932 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.9576 | 183000 | 0.6932 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 2.9737 | 184000 | 0.6932 | 0.6933 | 0.0469 (-0.4935) | 0.2499 (-0.0751) | 0.0317 (-0.4689) | 0.1095 (-0.3459) |
| 2.9899 | 185000 | 0.6933 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.0061 | 186000 | 0.6935 | 0.6935 | 0.0433 (-0.4972) | 0.2615 (-0.0635) | 0.0566 (-0.4440) | 0.1205 (-0.3349) |
| 3.0222 | 187000 | 0.6934 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.0384 | 188000 | 0.6932 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.0545 | 189000 | 0.6933 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.0707 | 190000 | 0.6932 | 0.6941 | 0.0376 (-0.5028) | 0.2517 (-0.0733) | 0.0365 (-0.4641) | 0.1086 (-0.3467) |
| 3.0869 | 191000 | 0.6932 | 0.6943 | 0.0457 (-0.4947) | 0.2565 (-0.0686) | 0.0480 (-0.4527) | 0.1167 (-0.3387) |
| 3.1030 | 192000 | 0.6932 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.1192 | 193000 | 0.6932 | 0.6931 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.1354 | 194000 | 0.6933 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.1515 | 195000 | 0.6932 | 0.6937 | 0.0455 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.1677 | 196000 | 0.6932 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.1838 | 197000 | 0.6932 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.2 | 198000 | 0.6933 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.2162 | 199000 | 0.6932 | 0.6941 | 0.0479 (-0.4925) | 0.2161 (-0.1089) | 0.0227 (-0.4780) | 0.0956 (-0.3598) |
| 3.2323 | 200000 | 0.6932 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.2485 | 201000 | 0.6933 | 0.6935 | 0.0450 (-0.4954) | 0.2492 (-0.0759) | 0.0466 (-0.4540) | 0.1136 (-0.3418) |
| 3.2646 | 202000 | 0.6933 | 0.6931 | 0.0401 (-0.5003) | 0.2528 (-0.0722) | 0.0495 (-0.4512) | 0.1141 (-0.3412) |
| 3.2808 | 203000 | 0.6934 | 0.6933 | 0.0456 (-0.4949) | 0.2563 (-0.0688) | 0.0470 (-0.4537) | 0.1163 (-0.3391) |
| 3.2970 | 204000 | 0.693 | 0.6931 | 0.0440 (-0.4964) | 0.2698 (-0.0553) | 0.0505 (-0.4501) | 0.1214 (-0.3339) |
| 3.3131 | 205000 | 0.693 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.3293 | 206000 | 0.6934 | 0.6934 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.3455 | 207000 | 0.6934 | 0.6934 | 0.0428 (-0.4977) | 0.2615 (-0.0635) | 0.0503 (-0.4504) | 0.1182 (-0.3372) |
| 3.3616 | 208000 | 0.6932 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.3778 | 209000 | 0.6934 | 0.6938 | 0.0476 (-0.4928) | 0.2573 (-0.0677) | 0.0414 (-0.4592) | 0.1154 (-0.3399) |
| 3.3939 | 210000 | 0.6933 | 0.6935 | 0.0216 (-0.5188) | 0.2499 (-0.0752) | 0.0321 (-0.4685) | 0.1012 (-0.3542) |
| 3.4101 | 211000 | 0.693 | 0.6932 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.4263 | 212000 | 0.6932 | 0.6935 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.4424 | 213000 | 0.6933 | 0.6933 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.4586 | 214000 | 0.6931 | 0.6934 | 0.0427 (-0.4977) | 0.2481 (-0.0769) | 0.0455 (-0.4551) | 0.1121 (-0.3433) |
| 3.4747 | 215000 | 0.6931 | 0.6936 | 0.0356 (-0.5048) | 0.2708 (-0.0543) | 0.0377 (-0.4630) | 0.1147 (-0.3407) |
| 3.4909 | 216000 | 0.6931 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.5071 | 217000 | 0.6933 | 0.6947 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.5232 | 218000 | 0.6931 | 0.6936 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.5394 | 219000 | 0.6931 | 0.6939 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.5556 | 220000 | 0.6932 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.5717 | 221000 | 0.6932 | 0.6937 | 0.0452 (-0.4952) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1161 (-0.3393) |
| 3.5879 | 222000 | 0.6932 | 0.6941 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.6040 | 223000 | 0.6929 | 0.6950 | 0.0333 (-0.5071) | 0.2317 (-0.0933) | 0.0560 (-0.4446) | 0.1070 (-0.3483) |
| 3.6202 | 224000 | 0.6932 | 0.6947 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0466 (-0.4540) | 0.1161 (-0.3393) |
| 3.6364 | 225000 | 0.6932 | 0.6941 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.6525 | 226000 | 0.6933 | 0.6939 | 0.0374 (-0.5030) | 0.2623 (-0.0628) | 0.0408 (-0.4599) | 0.1135 (-0.3419) |
| 3.6687 | 227000 | 0.6931 | 0.6942 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.6848 | 228000 | 0.6933 | 0.6943 | 0.0543 (-0.4861) | 0.2546 (-0.0704) | 0.0453 (-0.4553) | 0.1181 (-0.3373) |
| 3.7010 | 229000 | 0.693 | 0.6939 | 0.0452 (-0.4952) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1161 (-0.3393) |
| 3.7172 | 230000 | 0.6933 | 0.6940 | 0.0411 (-0.4994) | 0.2378 (-0.0873) | 0.0396 (-0.4610) | 0.1062 (-0.3492) |
| 3.7333 | 231000 | 0.6932 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.7495 | 232000 | 0.6931 | 0.6942 | 0.0264 (-0.5140) | 0.2650 (-0.0601) | 0.0250 (-0.4757) | 0.1055 (-0.3499) |
| 3.7657 | 233000 | 0.693 | 0.6942 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.7818 | 234000 | 0.6932 | 0.6938 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.7980 | 235000 | 0.6932 | 0.6939 | 0.0447 (-0.4957) | 0.2563 (-0.0687) | 0.0471 (-0.4536) | 0.1160 (-0.3393) |
| 3.8141 | 236000 | 0.6931 | 0.6938 | 0.0560 (-0.4844) | 0.2637 (-0.0613) | 0.0427 (-0.4579) | 0.1208 (-0.3346) |
| 3.8303 | 237000 | 0.6932 | 0.6938 | 0.0391 (-0.5013) | 0.2573 (-0.0678) | 0.0438 (-0.4568) | 0.1134 (-0.3420) |
| 3.8465 | 238000 | 0.6931 | 0.6939 | 0.0415 (-0.4989) | 0.2714 (-0.0537) | 0.0423 (-0.4584) | 0.1184 (-0.3370) |
| 3.8626 | 239000 | 0.6931 | 0.6937 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.8788 | 240000 | 0.693 | 0.6940 | 0.0394 (-0.5010) | 0.2307 (-0.0944) | 0.0280 (-0.4727) | 0.0994 (-0.3560) |
| 3.8949 | 241000 | 0.693 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.9111 | 242000 | 0.693 | 0.6942 | 0.0206 (-0.5198) | 0.2498 (-0.0753) | 0.0275 (-0.4731) | 0.0993 (-0.3561) |
| 3.9273 | 243000 | 0.6929 | 0.6942 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.9434 | 244000 | 0.6931 | 0.6941 | 0.0219 (-0.5185) | 0.2498 (-0.0753) | 0.0277 (-0.4729) | 0.0998 (-0.3556) |
| 3.9596 | 245000 | 0.693 | 0.6940 | 0.0454 (-0.4950) | 0.2562 (-0.0688) | 0.0468 (-0.4538) | 0.1162 (-0.3392) |
| 3.9758 | 246000 | 0.693 | 0.6940 | 0.0518 (-0.4886) | 0.2368 (-0.0882) | 0.0451 (-0.4555) | 0.1113 (-0.3441) |
| 3.9919 | 247000 | 0.6931 | 0.6940 | 0.0263 (-0.5141) | 0.2556 (-0.0695) | 0.0404 (-0.4602) | 0.1074 (-0.3479) |
| -1 | -1 | - | - | 0.1284 (-0.4120) | 0.2637 (-0.0614) | 0.1597 (-0.3409) | 0.1840 (-0.2714) |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.0.0
- Transformers: 4.56.0.dev0
- PyTorch: 2.7.1+cu126
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
AmirMT/Norbert-Lora
|
AmirMT
| 2025-08-12T18:53:50Z
| 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
| 2025-08-12T18:20:24Z
|
---
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: Norbert
---
# Norbert Lora
<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 `Norbert` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Norbert",
"lora_weights": "https://huggingface.co/AmirMT/Norbert-Lora/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('AmirMT/Norbert-Lora', weight_name='lora.safetensors')
image = pipeline('Norbert').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/AmirMT/Norbert-Lora/discussions) to add images that show off what you’ve made with this LoRA.
|
mradermacher/II-Search-CIR-4B-GGUF
|
mradermacher
| 2025-08-12T18:53:47Z
| 235
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Intelligent-Internet/II-Search-CIR-4B",
"base_model:quantized:Intelligent-Internet/II-Search-CIR-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-06T11:09:54Z
|
---
base_model: Intelligent-Internet/II-Search-CIR-4B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Intelligent-Internet/II-Search-CIR-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#II-Search-CIR-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/II-Search-CIR-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/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-GGUF/resolve/main/II-Search-CIR-4B.f16.gguf) | f16 | 8.2 | 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.
<!-- end -->
|
haryoaw/xlmr-base-massive-4-id
|
haryoaw
| 2025-08-12T18:53:28Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-12T18:52:57Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/II-Search-CIR-4B-i1-GGUF
|
mradermacher
| 2025-08-12T18:53:25Z
| 393
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Intelligent-Internet/II-Search-CIR-4B",
"base_model:quantized:Intelligent-Internet/II-Search-CIR-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-06T12:58:25Z
|
---
base_model: Intelligent-Internet/II-Search-CIR-4B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Intelligent-Internet/II-Search-CIR-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#II-Search-CIR-4B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/II-Search-CIR-4B-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/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/II-Search-CIR-4B-i1-GGUF/resolve/main/II-Search-CIR-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.4 | 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 -->
|
haryoaw/xlmr-base-massive-2-id
|
haryoaw
| 2025-08-12T18:52:13Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-12T18:51:39Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/reasonrank-32B-GGUF
|
mradermacher
| 2025-08-12T18:50:43Z
| 839
| 0
|
transformers
|
[
"transformers",
"gguf",
"passage-ranking",
"text-ranking",
"reasoning",
"Information-retrieval",
"en",
"dataset:liuwenhan/reasonrank_data_sft",
"dataset:liuwenhan/reasonrank_data_rl",
"dataset:liuwenhan/reasonrank_data_13k",
"base_model:liuwenhan/reasonrank-32B",
"base_model:quantized:liuwenhan/reasonrank-32B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-ranking
| 2025-08-09T11:36:21Z
|
---
base_model: liuwenhan/reasonrank-32B
datasets:
- liuwenhan/reasonrank_data_sft
- liuwenhan/reasonrank_data_rl
- liuwenhan/reasonrank_data_13k
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- passage-ranking
- text-ranking
- reasoning
- Information-retrieval
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/liuwenhan/reasonrank-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#reasonrank-32B-GGUF).***
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/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/reasonrank-32B-GGUF/resolve/main/reasonrank-32B.Q8_0.gguf) | Q8_0 | 34.9 | 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 -->
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755024552
|
Ferdi3425
| 2025-08-12T18:50:28Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:50:00Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shanghong/oumi_rag_grpo
|
shanghong
| 2025-08-12T18:50:08Z
| 0
| 0
| null |
[
"safetensors",
"qwen3",
"question-answering",
"en",
"dataset:shanghong/oumi_rag_grpo_data",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"region:us"
] |
question-answering
| 2025-08-12T18:34:57Z
|
---
datasets:
- shanghong/oumi_rag_grpo_data
base_model:
- Qwen/Qwen3-4B
pipeline_tag: question-answering
language:
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### 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]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755024495
|
ggozzy
| 2025-08-12T18:49:46Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:49:26Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755024513
|
IvanJAjebu
| 2025-08-12T18:49:42Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:49:33Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abdeljalilELmajjodi/Darija_Arabic_NER_LID_2
|
abdeljalilELmajjodi
| 2025-08-12T18:48:35Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:atlasia/XLM-RoBERTa-Morocco",
"base_model:finetune:atlasia/XLM-RoBERTa-Morocco",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-12T18:46:39Z
|
---
library_name: transformers
license: mit
base_model: atlasia/XLM-RoBERTa-Morocco
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Darija_Arabic_NER_LID_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Darija_Arabic_NER_LID_2
This model is a fine-tuned version of [atlasia/XLM-RoBERTa-Morocco](https://huggingface.co/atlasia/XLM-RoBERTa-Morocco) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4273
- F1: 0.9235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.1+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
MatValSE/EE_Fraktur_OCR
|
MatValSE
| 2025-08-12T18:47:06Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-12T17:57:36Z
|
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: olmocr-finetuned
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for olmocr-finetuned
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
mradermacher/Gemma-3-R1-12B-v1-GGUF
|
mradermacher
| 2025-08-12T18:46:44Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TheDrummer/Gemma-3-R1-12B-v1",
"base_model:quantized:TheDrummer/Gemma-3-R1-12B-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-12T17:38:46Z
|
---
base_model: TheDrummer/Gemma-3-R1-12B-v1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/TheDrummer/Gemma-3-R1-12B-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Gemma-3-R1-12B-v1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-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/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.7 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.mmproj-f16.gguf) | mmproj-f16 | 1.0 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.IQ4_XS.gguf) | IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q4_K_S.gguf) | Q4_K_S | 7.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q4_K_M.gguf) | Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q5_K_S.gguf) | Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q5_K_M.gguf) | Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q6_K.gguf) | Q6_K | 9.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-12B-v1-GGUF/resolve/main/Gemma-3-R1-12B-v1.Q8_0.gguf) | Q8_0 | 12.6 | 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.
<!-- end -->
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755024214
|
Ferdi3425
| 2025-08-12T18:45:15Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:44:48Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amoranv3/somatotype-inceptionresnetv2
|
amoranv3
| 2025-08-12T18:45:07Z
| 0
| 0
| null |
[
"pytorch",
"computer-vision",
"somatotype-classification",
"body-type-analysis",
"license:mit",
"region:us"
] | null | 2025-08-12T18:39:20Z
|
---
license: mit
tags:
- pytorch
- computer-vision
- somatotype-classification
- body-type-analysis
---
# Somatotype Classification Model - InceptionResNetV2
Este modelo clasifica somatotipos (tipos corporales) en tres categorías:
- Ectomorph (Ectomorfo)
- Mesomorph (Mesomorfo)
- Endomorph (Endomorfo)
## Uso
```python
from landing_page import InceptionResNetV2Module, predict_transform
import torch
from PIL import Image
# Cargar modelo
model = InceptionResNetV2Module(num_classes=3)
model.load_weights("pytorch_model.bin")
# Procesar imagen
image = Image.open("imagen_corporal.jpg")
image_tensor = predict_transform(image)
# Predicción
predicted_class, confidence, probabilities = model.predict(image_tensor)
```
## Modelo
- **Arquitectura**: InceptionResNetV2
- **Clases**: 3 (Ectomorph, Mesomorph, Endomorph)
- **Input Size**: 224x224 RGB
- **Framework**: PyTorch
Desarrollado por ElectroBiomed para análisis biomecánico y medicina deportiva.
|
Abinayasankar/SkyplerCoder-1.0-SFT
|
Abinayasankar
| 2025-08-12T18:44:45Z
| 9
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"text-generation",
"code-generation",
"codellama",
"lora",
"skypler",
"finetuned",
"trl",
"sft",
"transformers",
"huggingface",
"conversational",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-10T11:37:31Z
|
---
license: mit
tags:
- code-generation
- codellama
- lora
- peft
- skypler
- finetuned
- trl
- sft
- transformers
- huggingface
model-index:
- name: SkyplerCoder-1.0-SFT
results: []
---
# SkyplerCoder-1.0-SFT
SkyplerCoder-1.0-SFT is a CodeLlama-7B model finetuned using LoRA (PEFT) on a curated dataset of code instructions and completions. This model is designed for code generation, code completion, and code understanding tasks, supporting both Python and TypeScript, and is optimized for instruction-following and developer productivity.
## Model Details
- **Base Model:** [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf)
- **Finetuning Method:** LoRA (PEFT) with SFT (Supervised Fine-Tuning)
- **Framework:** PyTorch, Hugging Face Transformers, PEFT, TRL
- **Quantization:** Optional (BitsAndBytes config available)
- **Parameters:** 7B (with LoRA adapters merged)
- **Author:** Abinayasankar M(Skypler AI)
- **Contact:** [email protected]
## Training Data
- **Sources:**
- [mhhmm/typescript-instruct-20k](https://huggingface.co/datasets/mhhmm/typescript-instruct-20k)
- Custom CSVs: `trainable_data.csv`, `trainable_data_concert.csv`
- **Format:** Instruction/Completion pairs for code tasks in Python and TypeScript.
## Training Procedure
- **Hardware:** NVIDIA A100 GPU
- **Batch Size:** 8
- **Epochs:** 5
- **Learning Rate:** 2e-5
- **Optimizer:** AdamW
- **Scheduler:** Cosine
- **LoRA Config:** r=8, alpha=16, dropout=0.1, target_modules=["q_proj", "v_proj"]
- **Max Sequence Length:** 1024 (input), 2048 (labels)
- **Callbacks:** EarlyStopping, GradientNormLog, SaveCheckpoints
## Intended Use
- **Primary:** Code generation, code completion, code review, and code understanding.
- **Secondary:** Instruction-following for code tasks, code explanation, and code translation.
## Limitations
- May generate incorrect or insecure code.
- Not suitable for production without human review.
- Trained on public and synthetic data; may not generalize to all codebases.
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Abinayasankar/SkyplerCoder-1.0-SFT")
model = AutoModelForCausalLM.from_pretrained("Abinayasankar/SkyplerCoder-1.0-SFT").to("cuda")
prompt = "Write a Python function to reverse a string."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=128, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Evaluation
- **Perplexity:** Use the provided `compute_perplexity` function in the repo to evaluate on your own validation set.
## Citation
If you use this model, please cite:
```
@misc{skyplercoder2025,
title={SkyplerCoder-1.0-SFT: LoRA-Finetuned CodeLlama-7B for Code Generation},
author={Abinayasankar M},
year={2025},
howpublished={\url{https://huggingface.co/Abinayasankar/SkyplerCoder-1.0-SFT}}
}
```
## License
mit
---
**Disclaimer:**
This model is provided as-is and comes with no warranty. Always review generated code for correctness and security before use
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755022715
|
kojeklollipop
| 2025-08-12T18:44:23Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:44:19Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stanpony/tiny_lm_8M_vanilla_full_20250812_123852
|
stanpony
| 2025-08-12T18:40:00Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T18:39:57Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755023884
|
ggozzy
| 2025-08-12T18:39:22Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:39:06Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amoranv3/somatotype-densenet121
|
amoranv3
| 2025-08-12T18:39:19Z
| 0
| 0
| null |
[
"pytorch",
"computer-vision",
"somatotype-classification",
"body-type-analysis",
"license:mit",
"region:us"
] | null | 2025-08-12T18:38:31Z
|
---
license: mit
tags:
- pytorch
- computer-vision
- somatotype-classification
- body-type-analysis
---
# Somatotype Classification Model - DenseNet121
Este modelo clasifica somatotipos (tipos corporales) en tres categorías:
- Ectomorph (Ectomorfo)
- Mesomorph (Mesomorfo)
- Endomorph (Endomorfo)
## Uso
```python
from landing_page import DenseNet121Module, predict_transform
import torch
from PIL import Image
# Cargar modelo
model = DenseNet121Module(num_classes=3)
model.load_weights("pytorch_model.bin")
# Procesar imagen
image = Image.open("imagen_corporal.jpg")
image_tensor = predict_transform(image)
# Predicción
predicted_class, confidence, probabilities = model.predict(image_tensor)
```
## Modelo
- **Arquitectura**: DenseNet121
- **Clases**: 3 (Ectomorph, Mesomorph, Endomorph)
- **Input Size**: 224x224 RGB
- **Framework**: PyTorch
Desarrollado por ElectroBiomed para análisis biomecánico y medicina deportiva.
|
Jack-Payne1/qwen_2.5_7b-phoenix_T1_format_seed1
|
Jack-Payne1
| 2025-08-12T18:38:25Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T18:35:09Z
|
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Jack-Payne1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755023827
|
IvanJAjebu
| 2025-08-12T18:38:13Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:38:05Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llamab8-er-afg-v90-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-08-12T18:38:06Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T17:26:16Z
|
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: llama3b-llamab8-er-afg-v90-seed2-hx-alpaca-fpt
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. -->
# llama3b-llamab8-er-afg-v90-seed2-hx-alpaca-fpt
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- 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_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.0
|
BASH-Lab/RAVEN-AV-7B
|
BASH-Lab
| 2025-08-12T18:36:23Z
| 2
| 0
| null |
[
"safetensors",
"raven_qwen2",
"arxiv:2505.17114",
"license:apache-2.0",
"region:us"
] | null | 2025-08-04T17:51:40Z
|
---
license: apache-2.0
---
<p align="center">
<img src="./assets/raven_logo.png" width="100" style="margin-bottom: 0.2;"/>
<p>
<h3 align="center">
<a href="https://arxiv.org/pdf/2505.17114" style="color:#825987">
RAVEN: Query-Guided Representation Alignment for Question
Answering over Audio, Video, Embedded Sensors, and Natural Language
</a>
</h3>
<h5 align="center">
Project Page:
<a href="https://bashlab.github.io/raven_project/" style="color:#825987">
https://bashlab.github.io/raven_project/
</a>
</h5>
<p align="center">
<img src="./assets/raven_architecture.png" width="800" />
<p>
---
## 🛠️ Requirements and Installation
Basic Dependencies:
* Python >= 3.8
* Pytorch >= 2.2.0
* CUDA Version >= 11.8
* transformers == 4.40.0 (for reproducing paper results)
* tokenizers == 0.19.1
```bash
cd RAVEN
pip install -r requirements.txt
pip install flash-attn==2.5.8 --no-build-isolation
pip install opencv-python==4.5.5.64
apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
```
---
## 🤖 Inference
- **STEP 1:** Download $\texttt{siglip-so400m-patch14-384}$ from here [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384)
- **STEP 2:** Download **RAVEN** checkpoint
```bash
CUDA_VISIBLE_DEVICES=0 python inference.py --model-path=<MODEL PATH> --modal-type=<MODAL TYPE>
```
## 👍 Acknowledgement
The codebase of RAVEN is adapted from [**VideoLLaMA2**](https://github.com/DAMO-NLP-SG/VideoLLaMA2). We are also grateful for their contribution.
|
Top-Video-Archita-Phukan-Viral-Video/Archita.viral.videos.link.Exclusive
|
Top-Video-Archita-Phukan-Viral-Video
| 2025-08-12T18:36:00Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-08-12T18:35:23Z
|
<animated-image data-catalyst=""><a href="https://tinyurl.com/5abutj9x?viral-news" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
andr0m4da/blockassist-bc-grazing_hunting_boar_1755023546
|
andr0m4da
| 2025-08-12T18:34:32Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing hunting boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:34:24Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing hunting boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755023579
|
ggozzy
| 2025-08-12T18:34:23Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:34:07Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bamitunde/blockassist-bc-mimic_humming_frog_1755023561
|
bamitunde
| 2025-08-12T18:34:17Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mimic humming frog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:33:43Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mimic humming frog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EurekaTian/qwen2p5_7b_mmlu_pos
|
EurekaTian
| 2025-08-12T18:33:36Z
| 0
| 0
| null |
[
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T17:39:27Z
|
---
license: apache-2.0
---
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755023544
|
IvanJAjebu
| 2025-08-12T18:33:33Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:33:24Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Abhay31/ppo-LunarLander-v2
|
Abhay31
| 2025-08-12T18:33:32Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-12T18:33:15Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.61 +/- 25.49
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Jessica-Radcliffe-Viral-Video-Clip/Did.an.Orca.Kill.Trainer.Jessica.Radcliffe.Viral.Clip.Explained
|
Jessica-Radcliffe-Viral-Video-Clip
| 2025-08-12T18:30:15Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-08-12T18:30:04Z
|
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755023333
|
Ferdi3425
| 2025-08-12T18:30:05Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:29:38Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ver-Video-Jessica-Radcliffe-la-orca-viral/VIRAL.VER.Video.Jessica.Radcliffe.y.la.orca.viral.de.la.muerte.video
|
ver-Video-Jessica-Radcliffe-la-orca-viral
| 2025-08-12T18:29:18Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-08-12T18:28:48Z
|
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755023273
|
ggozzy
| 2025-08-12T18:29:17Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:29:01Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Abhay31/ppo-LunarLander-v3
|
Abhay31
| 2025-08-12T18:28:54Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-12T18:28:36Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v3
type: LunarLander-v3
metrics:
- type: mean_reward
value: 256.58 +/- 18.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v3**
This is a trained model of a **PPO** agent playing **LunarLander-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755021689
|
calegpedia
| 2025-08-12T18:28:44Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:28:40Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MariChristmass/digital
|
MariChristmass
| 2025-08-12T18:28:41Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-12T18:28:17Z
|
---
license: apache-2.0
---
|
hamid1232/Qwen3-0.6B-Gensyn-Swarm-strong_wiry_skunk
|
hamid1232
| 2025-08-12T18:28:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am strong_wiry_skunk",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T08:10:51Z
|
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am strong_wiry_skunk
---
# 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]
|
nightmedia/Jan-v1-4B-q4-hi-mlx
|
nightmedia
| 2025-08-12T18:26:14Z
| 0
| 0
|
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"base_model:janhq/Jan-v1-4B",
"base_model:quantized:janhq/Jan-v1-4B",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-12T18:15:50Z
|
---
license: apache-2.0
language:
- en
base_model: janhq/Jan-v1-4B
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
---
# Jan-v1-4B-q4-hi-mlx
This model [Jan-v1-4B-q4-hi-mlx](https://huggingface.co/Jan-v1-4B-q4-hi-mlx) was
converted to MLX format from [janhq/Jan-v1-4B](https://huggingface.co/janhq/Jan-v1-4B)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Jan-v1-4B-q4-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755021416
|
milliarderdol
| 2025-08-12T18:26:13Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:26:01Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755023044
|
Ferdi3425
| 2025-08-12T18:25:16Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-12T18:24:50Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
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