modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 06:30:45
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
yuchuantian/AIGC_detector_env3short
|
yuchuantian
| 2025-06-24T23:27:16Z | 0 | 0 | null |
[
"pytorch",
"roberta",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T23:21:35Z |
---
license: apache-2.0
---
|
VarunNagaraj/tiny-llm-maui-expressions-mistral
|
VarunNagaraj
| 2025-06-24T23:26:14Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T23:25:08Z |
---
base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VarunNagaraj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
yuchuantian/AIGC_detector_env3
|
yuchuantian
| 2025-06-24T23:26:14Z | 0 | 0 | null |
[
"pytorch",
"roberta",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T23:20:38Z |
---
license: apache-2.0
---
|
mattfutureflow/matt-images
|
mattfutureflow
| 2025-06-24T23:24:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T22:54:06Z |
---
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: Matt
---
# Matt Images
<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 `Matt` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Matt",
"lora_weights": "https://huggingface.co/mattfutureflow/matt-images/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('mattfutureflow/matt-images', weight_name='lora.safetensors')
image = pipeline('Matt').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/mattfutureflow/matt-images/discussions) to add images that show off what you’ve made with this LoRA.
|
18-videos-jobz-hunting-sajal-malik-virals/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
|
18-videos-jobz-hunting-sajal-malik-virals
| 2025-06-24T23:23:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T23:22:49Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
jobs-git/OmniGen2
|
jobs-git
| 2025-06-24T23:19:52Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"any-to-any",
"arxiv:2506.18871",
"arxiv:2404.07724",
"license:apache-2.0",
"diffusers:OmniGen2Pipeline",
"region:us"
] |
any-to-any
| 2025-06-24T23:19:51Z |
---
license: apache-2.0
pipeline_tag: any-to-any
library_name: diffusers
---
<p align="center">
<a href="https://github.com/Ve<p align="center">
<img src="assets/brand.png" width="65%">
</p>
<p align="center">
<a href="https://vectorspacelab.github.io/OmniGen2"><img src="https://img.shields.io/badge/Project%20Page-OmniGen2-yellow" alt="project page"></a>
<a href="https://arxiv.org/abs/2506.18871"><img src="https://img.shields.io/badge/arXiv%20paper-2506.18871-b31b1b.svg" alt="arxiv"></a>
<a href="https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo"><img src="https://img.shields.io/badge/Online%20Demo-🤗-blue" alt="demo"></a>
<a href="https://huggingface.co/spaces/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/HF%20Spaces-🤗-lightblue" alt="demo"></a>
<a href="https://huggingface.co/OmniGen2/OmniGen2"><img src="https://img.shields.io/badge/Model-🤗-yellow" alt="model"></a>
<a href=""><img src="https://img.shields.io/badge/Benchmark-🤗-yellow" alt="model"></a>
<a href=""><img src="https://img.shields.io/badge/Dataset-🤗-yellow" alt="model"></a>
</p>
<h4 align="center">
<p>
<a href=#-news>News</a> |
<a href=#-quick-start>Quick Start</a> |
<a href=#-usage-tips>Usage Tips</a> |
<a href=#-gradio-demo>Online Demos</a> |
<a href="#heart-citing-us">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
## 🔥 News
- **2025-06-24**: [Technical Report](https://arxiv.org/abs/2506.18871) is available.
- **2025-06-23**: We’ve updated our code and HF model—OmniGen2 now runs *without* `flash-attn`. Users can still install it for optimal performance.
- **2025-06-20**: Updated [resource requirements](#-resources-requirement), adding CPU offload support for devices with limited VRAM.
- **2025-06-16**: [Gradio](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo) and [Jupyter](https://github.com/VectorSpaceLab/OmniGen2/blob/main/example.ipynb) is available. Online Gradio Demo: [Demo1](https://8f10329141d53b6884.gradio.live); [Chat-Demo1](https://9315447fc78ef638e3.gradio.live); see more demo links in [gradio section](https://github.com/VectorSpaceLab/OmniGen2?tab=readme-ov-file#-gradio-demo)
- **2025-06-16**: We release **OmniGen2**, a multimodal generation model, model weights can be accessed in [huggingface](https://huggingface.co/OmniGen2/OmniGen2) and [modelscope](https://www.modelscope.cn/models/OmniGen2/OmniGen2).
## Introduction
**OmniGen2** is a powerful and efficient unified multimodal model. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. OmniGen2 has competitive performance across four primary capabilities:
- **Visual Understanding**: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation.
- **Text-to-Image Generation**: Creates high-fidelity and aesthetically pleasing images from textual prompts.
- **Instruction-guided Image Editing**: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models.
- **In-context Generation**: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs.
As an open-source project, OmniGen2 provides a powerful yet resource-efficient foundation for researchers and developers exploring the frontiers of controllable and personalized generative AI.
**We will release the training code, dataset, and data construction pipeline soon. Stay tuned!**
<p align="center">
<img src="assets/teaser.jpg" width="95%">
<br>
<em>Demonstration of OmniGen2's overall capabilities.</em>
</p>
<p align="center">
<img src="assets/examples_edit.png" width="95%">
<br>
<em>Demonstration of OmniGen2's image editing capabilities.</em>
</p>
<p align="center">
<img src="assets/examples_subject.png" width="95%">
<br>
<em>Demonstration of OmniGen2's in-context generation capabilities.</em>
</p>
## 📌 TODO
- [x] Technical report.
- [ ] In-context generation benchmark: **OmniContext**.
- [x] Support CPU offload and improve inference efficiency.
- [ ] Integrated in diffusers.
- [ ] Training data and scripts.
- [ ] Data construction pipeline.
- [ ] ComfyUI Demo (**commuity support will be greatly appreciated!**).
## 🚀 Quick Start
### 🛠️ Environment Setup
#### ✅ Recommended Setup
```bash
# 1. Clone the repo
git clone [email protected]:VectorSpaceLab/OmniGen2.git
cd OmniGen2
# 2. (Optional) Create a clean Python environment
conda create -n omnigen2 python=3.11
conda activate omnigen2
# 3. Install dependencies
# 3.1 Install PyTorch (choose correct CUDA version)
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124
# 3.2 Install other required packages
pip install -r requirements.txt
# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation
```
#### 🌏 For users in Mainland China
```bash
# Install PyTorch from a domestic mirror
pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124
# Install other dependencies from Tsinghua mirror
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
# OmniGen2 runs even without flash-attn, though we recommend install it for best performance.
pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple
```
---
### 🧪 Run Examples
```bash
# Visual Understanding
bash example_understanding.sh
# Text-to-image generation
bash example_t2i.sh
# Instruction-guided image editing
bash example_edit.sh
# In-context generation
bash example_in_context_generation.sh
```
---
### 🌐 Gradio Demo
* **Online Demo**: [HF Spaces](https://huggingface.co/spaces/OmniGen2/OmniGen2). Beyond Hugging Face Spaces, we are *temporarily* allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links:
[Demo1](https://8f10329141d53b6884.gradio.live), [Demo2](https://110863cb06c6c44bd2.gradio.live), [Demo3](https://19b0952eb3cf0d2243.gradio.live), [Demo4](https://981758b17b4197aea7.gradio.live)
[Chat-Demo1](https://9315447fc78ef638e3.gradio.live), [Chat-Demo2](https://abe054be89543e4cef.gradio.live), [Chat-Demo3](https://4aa913765db00bbe51.gradio.live), [Chat-Demo4](https://f28a8718565627d2cb.gradio.live)
<!-- [Available on Hugging Face Spaces 🚀](https://huggingface.co/spaces/Shitao/OmniGen2) -->
* **Run Locally**:
```bash
# for only generating image
pip install gradio
python app.py
# Optional: Share demo with public link (You need to be able to access huggingface)
python app.py --share
# for generating image or text
pip install gradio
python app_chat.py
```
## 💡 Usage Tips
To achieve optimal results with OmniGen2, you can adjust the following key hyperparameters based on your specific use case.
- `text_guidance_scale`: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance).
- `image_guidance_scale`: This controls how much the final image should resemble the input reference image.
- **The Trade-off**: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence.
- **Tip**: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0.
- `max_pixels`: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage.
- **Tip**: Default value is 1024*1024. You can reduce this value if you encounter memory issues.
- `max_input_image_side_length`: Maximum side length for input images.
- `negative_prompt`: Tell the model what you don't want to see in the image.
- **Example**: blurry, low quality, text, watermark
- **Tip**: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt.
- `enable_model_cpu_offload`: **Reduces VRAM usage by nearly 50% with a negligible impact on speed**.
- This is achieved by offloading the model weights to CPU RAM when they are not in use.
- See: [Model Offloading](https://huggingface.co/docs/diffusers/optimization/memory#model-offloading)
- `enable_sequential_cpu_offload`: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance.
- This works by offloading the model in submodules and loading them onto the GPU sequentially as needed.
- See: [CPU Offloading](https://huggingface.co/docs/diffusers/optimization/memory#cpu-offloading)
- `cfg_range_start`, `cfg_range_end`: Define the timestep range where CFG is applied. Per this [paper](https://arxiv.org/abs/2404.07724), reducing `cfg_range_end` can significantly decrease inference time with a negligible impact on quality.
**Some suggestions for improving generation quality:**
1. Use High-Quality Images
- Provide clear images, preferably with a resolution **greater than 512×512 pixels**.
- Small or blurry inputs will result in low-quality outputs.
2. Be Specific with Instructions
- Clearly describe both **what to change** and **how you want it changed**.
- For in-context generation tasks, explicitly state which elements should come from which image. For example, instead of "Add bird to desk", say "Add the bird from image 1 onto the desk in image 2."
3. Prioritize English
The model currently performs best with **English** prompts.
## 💻 Resources Requirement
OmniGen2 natively requires an **NVIDIA RTX 3090** or an equivalent GPU with approximately **17GB of VRAM**. For devices with less VRAM, you can enable **CPU Offload** to run the model.
**Performance Tip**: To improve inference speed, consider decreasing the `cfg_range_end` parameter. Within a reasonable range, this has a negligible impact on output quality.
The following table details the inference performance of OmniGen2 on an **A800 GPU**:
<p align="center">
<img src="assets/efficiency.png" width="95%">
<br>
<em>Inference Efficiency of OmniGen2.</em>
</p>
## ❤️ Citing Us
If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated (OmniGen2 report will be available as soon as possible):
```bibtex
@article{wu2025omnigen2,
title={OmniGen2: Exploration to Advanced Multimodal Generation},
author={Chenyuan Wu and Pengfei Zheng and Ruiran Yan and Shitao Xiao and Xin Luo and Yueze Wang and Wanli Li and Xiyan Jiang and Yexin Liu and Junjie Zhou and Ze Liu and Ziyi Xia and Chaofan Li and Haoge Deng and Jiahao Wang and Kun Luo and Bo Zhang and Defu Lian and Xinlong Wang and Zhongyuan Wang and Tiejun Huang and Zheng Liu},
journal={arXiv preprint arXiv:2506.18871},
year={2025}
}
```
## License
This work is licensed under Apache 2.0 license.
|
secmlr/best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
|
secmlr
| 2025-06-24T23:14:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:secmlr/final_model",
"base_model:finetune:secmlr/final_model",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T03:15:06Z |
---
library_name: transformers
license: apache-2.0
base_model: secmlr/final_model
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
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. -->
# best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
This model is a fine-tuned version of [secmlr/final_model](https://huggingface.co/secmlr/final_model) on the best_n_no_rationale_poc_agent_withjava dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
mowen222/task-10-Qwen-Qwen2.5-7B
|
mowen222
| 2025-06-24T23:04:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B",
"base_model:adapter:Qwen/Qwen2.5-7B",
"region:us"
] | null | 2025-06-24T23:04:31Z |
---
base_model: Qwen/Qwen2.5-7B
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 0.13.2
|
mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF
|
mradermacher
| 2025-06-24T23:00:22Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ChetKao/Bohdi-Qwen2.5-7B-Instruct",
"base_model:quantized:ChetKao/Bohdi-Qwen2.5-7B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-24T13:55:51Z |
---
base_model: ChetKao/Bohdi-Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ChetKao/Bohdi-Qwen2.5-7B-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-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/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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 -->
|
AliCat2/Picaro-24b-2506-636
|
AliCat2
| 2025-06-24T22:51:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Trappu/Picaro-24b-2506-adapters-636steps",
"base_model:merge:Trappu/Picaro-24b-2506-adapters-636steps",
"base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"base_model:merge:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T15:48:24Z |
---
base_model:
- anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML
- Trappu/Picaro-24b-2506-adapters-636steps
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method.
### Models Merged
The following models were included in the merge:
* [anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML](https://huggingface.co/anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML) + [Trappu/Picaro-24b-2506-adapters-636steps](https://huggingface.co/Trappu/Picaro-24b-2506-adapters-636steps)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: passthrough
dtype: bfloat16
models:
- model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML+Trappu/Picaro-24b-2506-adapters-636steps
```
|
VvB9/houserendertest
|
VvB9
| 2025-06-24T22:44:32Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-24T22:44:32Z |
---
license: other
license_name: vvb9
license_link: LICENSE
---
|
jisukim8873/adapter-planner-epoch2
|
jisukim8873
| 2025-06-24T22:41:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T22:41:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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|
jisukim8873/adapter-caller-epoch3
|
jisukim8873
| 2025-06-24T22:40:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T22:39:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JoseRicardoRV/unifar-ia-gemma2-2b-it-instruct-v1
|
JoseRicardoRV
| 2025-06-24T22:39:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T22:23:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If 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]
|
Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF
|
Kieran2828
| 2025-06-24T22:38:25Z | 0 | 0 | null |
[
"gguf",
"pretrained",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-24T22:37:15Z |
---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
- moe
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen1.5-MoE-A2.7B
---
# Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) 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 Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.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 Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048
```
|
mlfoundations-dev/openthoughts3_100k_qwen25_1b_bsz1024_lr4e5_epochs5
|
mlfoundations-dev
| 2025-06-24T22:33:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:12:16Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: openthoughts3_100k_qwen25_1b_bsz1024_lr4e5_epochs5
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. -->
# openthoughts3_100k_qwen25_1b_bsz1024_lr4e5_epochs5
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 mlfoundations-dev/openthoughts3_100k 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Aldo789/5735a8ff-a67e-4f5a-961e-109d1b911e0f
|
Aldo789
| 2025-06-24T22:30:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T21:52:09Z |
---
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]
|
Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF
|
Kieran2828
| 2025-06-24T22:18:56Z | 0 | 0 | null |
[
"gguf",
"pretrained",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-24T22:18:17Z |
---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
- moe
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen1.5-MoE-A2.7B
---
# Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) 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 Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-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 Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048
```
|
EYEDOL/llama-3.2-3b-instruct-finetuned_MLON
|
EYEDOL
| 2025-06-24T22:15:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T08:39:32Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EYEDOL
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rbelanec/train_qnli_1750781358
|
rbelanec
| 2025-06-24T22:14:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:google/gemma-3-1b-it",
"base_model:adapter:google/gemma-3-1b-it",
"license:gemma",
"region:us"
] | null | 2025-06-24T16:11:43Z |
---
library_name: peft
license: gemma
base_model: google/gemma-3-1b-it
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_qnli_1750781358
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. -->
# train_qnli_1750781358
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the qnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0861
- Num Input Tokens Seen: 117444992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
phospho-app/Schmidie-ACT-schachtel-1eltm
|
phospho-app
| 2025-06-24T22:04:46Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-24T19:04:32Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Training process exceeded timeout of 10800 seconds. We have uploaded the last checkpoint. Please consider lowering the batch size or number of steps if you wish to train the model longer.
```
## Training parameters:
- **Dataset**: [Schmidie/schachtel](https://huggingface.co/datasets/Schmidie/schachtel)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 30
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-10
|
annasoli
| 2025-06-24T22:00:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:23:03Z |
---
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]
|
pakcricketinfo-sapna-shah-Viral-videos/FULL.VIDEO.pakcricketinfo.sapna.shah.Viral.Video.Tutorial.Official
|
pakcricketinfo-sapna-shah-Viral-videos
| 2025-06-24T21:57:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:56:48Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF
|
mradermacher
| 2025-06-24T21:56:51Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B",
"base_model:quantized:Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-24T18:05:41Z |
---
base_model: Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-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/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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 -->
|
mradermacher/GLM-4-32B-Base-32K-GGUF
|
mradermacher
| 2025-06-24T21:56:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"zh",
"en",
"base_model:arcee-ai/GLM-4-32B-Base-32K",
"base_model:quantized:arcee-ai/GLM-4-32B-Base-32K",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T16:33:45Z |
---
base_model: arcee-ai/GLM-4-32B-Base-32K
language:
- zh
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/arcee-ai/GLM-4-32B-Base-32K
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-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/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.IQ4_XS.gguf) | IQ4_XS | 17.9 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q4_K_S.gguf) | Q4_K_S | 18.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q4_K_M.gguf) | Q4_K_M | 19.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q5_K_S.gguf) | Q5_K_S | 22.6 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q5_K_M.gguf) | Q5_K_M | 23.2 | |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GLM-4-32B-Base-32K-GGUF/resolve/main/GLM-4-32B-Base-32K.Q8_0.gguf) | Q8_0 | 34.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mezzo-fun-Viral-full-Video/18.New.videos.mezzo.fun.viral.Clips.Mezzo.fun.Viral.Video.Tutorial.Official
|
mezzo-fun-Viral-full-Video
| 2025-06-24T21:56:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:55:19Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
New-videos-Bindura-University-viral-video/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official
|
New-videos-Bindura-University-viral-video
| 2025-06-24T21:53:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:52:53Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
Nitish035/mistral_CMoS_adapter32_larger-refine312
|
Nitish035
| 2025-06-24T21:48:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:48:26Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nitish035
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JamieOgundiran/Ogun-Mistral-7B
|
JamieOgundiran
| 2025-06-24T21:40:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T21:35:20Z |
---
base_model: mistralai/Mistral-7B-v0.1
library_name: transformers
model_name: ogun-mistral-7B-2
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for ogun-mistral-7B-2
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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="JamieOgundiran/ogun-mistral-7B-2", 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: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Koubra-Gaby/facebook-NLLB-fr-arb
|
Koubra-Gaby
| 2025-06-24T21:35:02Z | 42 | 0 | null |
[
"safetensors",
"m2m_100",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T08:23:23Z |
---
license: apache-2.0
---
|
fredconex/SongBloom-safetensors
|
fredconex
| 2025-06-24T21:34:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:39:50Z |
Original Model:
https://huggingface.co/CypressYang/SongBloom
|
altaweel/gemma-ultrasound-1b-v3
|
altaweel
| 2025-06-24T21:33:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T19:32:05Z |
---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-ultrasound-1b-v3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-ultrasound-1b-v3
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
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="altaweel/gemma-ultrasound-1b-v3", 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: 0.15.2
- Transformers: 4.52.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
sameeahameed/llama-3.2-3b-25-6-25
|
sameeahameed
| 2025-06-24T21:29:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:29:33Z |
---
base_model: unsloth/llama-3.2-3b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sameeahameed
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
oumi-ai/Phi-3-vision-128k-instruct
|
oumi-ai
| 2025-06-24T21:28:39Z | 0 | 0 | null |
[
"safetensors",
"phi3_v",
"nlp",
"code",
"vision",
"text-generation",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"region:us"
] |
text-generation
| 2025-06-24T21:23:42Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
- vision
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: <|image_1|>Can you describe what you see in the image?
---
🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
## Model Summary
The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
Resources and Technical Documentation:
+ [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision)
+ [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook)
| | Short Context | Long Context |
| ------- | ------------- | ------------ |
| Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
| Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
| Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
| Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
## Intended Uses
**Primary use cases**
The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require
1) memory/compute constrained environments;
2) latency bound scenarios;
3) general image understanding;
4) OCR;
5) chart and table understanding.
Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.
Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## How to Use
Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
Examples of required packages:
```
flash_attn==2.5.8
numpy==1.24.4
Pillow==10.3.0
Requests==2.31.0
torch==2.3.0
torchvision==0.18.0
transformers==4.40.2
```
Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai).
### Chat Format
Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows.
You can provide the prompt as a single image with a generic template as follow:
```markdown
<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n
```
where the model generates the text after `<|assistant|>` . In case of multi-turn conversation, the prompt can be formatted as follows:
```markdown
<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
from PIL import Image
import requests
from transformers import AutoModelForCausalLM
from transformers import AutoProcessor
model_id = "microsoft/Phi-3-vision-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
messages = [
{"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"},
{"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."},
{"role": "user", "content": "Provide insightful questions to spark discussion."}
]
url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png"
image = Image.open(requests.get(url, stream=True).raw)
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")
generation_args = {
"max_new_tokens": 500,
"temperature": 0.0,
"do_sample": False,
}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
# remove input tokens
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(response)
```
Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py).
### How to finetune?
We recommend user to take a look at the [Phi-3 CookBook finetuning recipe for Vision](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md)
## Responsible AI Considerations
Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
+ Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing.
## Training
### Model
* Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model.
* Inputs: Text and Image. It’s best suited for prompts using the chat format.
* Context length: 128K tokens
* GPUs: 512 H100-80G
* Training time: 1.5 days
* Training data: 500B vision and text tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models.
* Release Type: Open weight release
* Release dates: The model weight is released on May 21, 2024.
### Datasets
Our training data includes a wide variety of sources, and is a combination of
1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code;
2) selected high-quality image-text interleave;
3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides;
4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data.
More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
## Benchmarks
To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform.
|Benchmark|Phi-3 Vision-128K-In|LlaVA-1.6 Vicuna-7B|QWEN-VL Chat|Llama3-Llava-Next-8B|Claude-3 Haiku|Gemini 1.0 Pro V|GPT-4V-Turbo|
|---------|---------------------|------------------|------------|--------------------|--------------|----------------|------------|
|MMMU|40.4|34.2|39.0|36.4|40.7|42.0|55.5|
|MMBench|80.5|76.3|75.8|79.4|62.4|80.0|86.1|
|ScienceQA|90.8|70.6|67.2|73.7|72.0|79.7|75.7|
|MathVista|44.5|31.5|29.4|34.8|33.2|35.0|47.5|
|InterGPS|38.1|20.5|22.3|24.6|32.1|28.6|41.0|
|AI2D|76.7|63.1|59.8|66.9|60.3|62.8|74.7|
|ChartQA|81.4|55.0|50.9|65.8|59.3|58.0|62.3|
|TextVQA|70.9|64.6|59.4|55.7|62.7|64.7|68.1|
|POPE|85.8|87.2|82.6|87.0|74.4|84.2|83.7|
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
|
ToastyPigeon/ms3.2-cowriter-lora
|
ToastyPigeon
| 2025-06-24T21:28:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"generated_from_trainer",
"dataset:ToastyPigeon/cowriter-instruct",
"base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"base_model:adapter:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-24T21:08:15Z |
---
library_name: peft
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML
tags:
- generated_from_trainer
datasets:
- ToastyPigeon/cowriter-instruct
model-index:
- name: workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
# === Model Configuration ===
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML
load_in_8bit: false
load_in_4bit: true
# === HF Configuration ===
#hub_model_id: ToastyPigeon/an-instruct-ms3.2-train
#hub_strategy: "checkpoint"
# === Training Setup ===
num_epochs: 2
micro_batch_size: 1
gradient_accumulation_steps: 4
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
# === Evaluation ===
val_set_size: 0.01
evals_per_epoch: 5
#eval_steps: 20
#max_steps: 60
#eval_table_size:
eval_max_new_tokens: 128
eval_sample_packing: true
#eval_strategy: "no"
# === LoRA Configuration ===
adapter: qlora
lora_model_dir:
lora_r: 128
lora_alpha: 16
lora_dropout: 0.125
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
peft_use_rslora: true
#lora_mlp_kernel: true
#lora_qkv_kernel: true
#lora_o_kernel: true
# === Hyperparameter Configuration ===
#optimizer: apollo_adamw_layerwise
warmup_steps: 20
optimizer: adamw_torch_fused
#optimizer: paged_adamw_8bit
#optim_args:
# enable_stochastic_rounding: true
# enable_cautious: true
# enable_8bit: true
# Apollo-mini configuration:
#optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100"
# Regular Apollo configuration:
# optim_args:
#optim_target_modules: all_linear
learning_rate: 1e-5
lr_scheduler: rex
cosine_min_lr_ratio: 0.2
#lr_scheduler: cosine_with_min_lr
#lr_scheduler_kwargs:
# cosine_min_lr: 1e-6
weight_decay: 0.01
max_grad_norm: 1.0
#warmup_steps: 0
#warmup_ratio: 0.025
# === Data Configuration ===
chat_template: jinja
chat_template_jinja: "{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] in ['image', 'image_url'] %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {%- if message['content'] is string %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- else %}\n {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}"
tokenizer_use_mistral_common: true
shuffle_merged_datasets: true
datasets:
- path: ToastyPigeon/cowriter-instruct
type: chat_template
data_files: cowriter-16k.json
dataset_prepared_path: last_run_prepared
# === Plugins ===
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
# === Hardware Optimization ===
#gradient_checkpointing: offload
#gradient_checkpointing_kwargs:
# use_reentrant: false
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
#liger_fused_linear_cross_entropy: true
cut_cross_entropy: true
#deepspeed: deepspeed_configs/zero3_bf16.json
# === FSDP Config ===
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: true
fsdp_activation_checkpointing: true
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
# === Wandb Tracking ===
wandb_project: Mistral-3.2
# wandb_entity: [WANDB_ENTITY]
# wandb_name: [WANDB_RUN_NAME]
# === Checkpointing ===
saves_per_epoch: 20
save_total_limit: 5
# === Advanced Settings ===
output_dir: /workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
save_safetensors: true
logging_steps: 1
gc_steps: 10
seed: 69
```
</details><br>
# workspace/aibox-standalone-pool/axolotl/mistral3.2-cowriter-ckpts
This model is a fine-tuned version of [anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML](https://huggingface.co/anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML) on the ToastyPigeon/cowriter-instruct dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 69
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 328
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4735 | 0.0061 | 1 | 2.5005 |
| 2.5091 | 0.2006 | 33 | 2.4586 |
| 2.499 | 0.4012 | 66 | 2.4589 |
| 2.3922 | 0.6018 | 99 | 2.4499 |
| 2.3564 | 0.8024 | 132 | 2.4468 |
| 2.3165 | 1.0 | 165 | 2.4428 |
| 2.4576 | 1.2006 | 198 | 2.4388 |
| 2.4476 | 1.4012 | 231 | 2.4366 |
| 2.3374 | 1.6018 | 264 | 2.4343 |
| 2.3019 | 1.8024 | 297 | 2.4342 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Fizzarolli/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
|
Fizzarolli
| 2025-06-24T21:24:27Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:finetune:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"region:us"
] | null | 2025-06-24T21:23:45Z |
---
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
---
**Modified Small 3.2:**
- No vision encoder
- Standard "Mistral" architecture
Enjoy!
|
AbstractPhil/clips
|
AbstractPhil
| 2025-06-24T21:21:13Z | 11 | 11 | null |
[
"gguf",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-03-22T13:47:12Z |
---
license: mit
---
It's a clip dump. I've been hording them for a long time and I'm tired of fighting with the Civit service to upload them one or two at a time with a nice presentation.
|
New-Clip-isabelle-kaif-Viral-videos-Link/FULL.VIDEO.isabelle.kaif.Viral.Video.Tutorial.Official
|
New-Clip-isabelle-kaif-Viral-videos-Link
| 2025-06-24T21:21:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:19:53Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
drewbrown/gemma-2b-sql-finetuned
|
drewbrown
| 2025-06-24T21:15:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T17:21:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Manav13254/distilbert-rotten-tomatoes
|
Manav13254
| 2025-06-24T21:13:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-24T21:10:25Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-rotten-tomatoes
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. -->
# distilbert-rotten-tomatoes
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.2
|
papinwit/scb_qwen8b
|
papinwit
| 2025-06-24T21:08:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:08:28Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** papinwit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TOTORONG/llama_33_GGUF
|
TOTORONG
| 2025-06-24T21:08:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T19:48:44Z |
---
base_model: unsloth/Llama-3.3-70B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TOTORONG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.3-70B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
robertou2/task-10_4-Qwen-Qwen2.5-7B-Instruct
|
robertou2
| 2025-06-24T21:05:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-24T06:24:18Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
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 0.14.0
|
New-videos-Maya-G-viral-video/FULL.VIDEO.Maya.G.Viral.Video.Tutorial.Official
|
New-videos-Maya-G-viral-video
| 2025-06-24T21:00:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:00:19Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-50
|
annasoli
| 2025-06-24T20:56:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:26:48Z |
---
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]
|
musab1blaser/llama-3_2-1b_student2
|
musab1blaser
| 2025-06-24T20:51:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:51:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Fralet/DDeduPModelV3
|
Fralet
| 2025-06-24T20:50:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:adapter:unsloth/llama-3-8b-bnb-4bit",
"region:us"
] | null | 2025-06-24T20:22:43Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
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]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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 0.15.2
|
alinerodrigues/wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
|
alinerodrigues
| 2025-06-24T20:50:31Z | 0 | 0 | null |
[
"pytorch",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T19:53:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
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. -->
# wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3604
- Per: 0.0782
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9113 | 1.0 | 101 | 2.9923 | 1.0 |
| 2.9182 | 2.0 | 203 | 2.6854 | 0.8815 |
| 2.6392 | 3.0 | 304 | 2.3930 | 0.8977 |
| 2.2914 | 4.0 | 406 | 1.9150 | 0.9196 |
| 1.816 | 5.0 | 507 | 1.2695 | 0.5716 |
| 1.3438 | 6.0 | 609 | 0.8700 | 0.2969 |
| 1.0141 | 7.0 | 710 | 0.6949 | 0.1850 |
| 0.8087 | 8.0 | 812 | 0.5666 | 0.1174 |
| 0.6842 | 9.0 | 913 | 0.4808 | 0.1030 |
| 0.6004 | 10.0 | 1015 | 0.4810 | 0.0996 |
| 0.528 | 11.0 | 1116 | 0.4781 | 0.0863 |
| 0.4883 | 12.0 | 1218 | 0.4436 | 0.0814 |
| 0.4528 | 13.0 | 1319 | 0.4410 | 0.0820 |
| 0.4258 | 14.0 | 1421 | 0.4197 | 0.0848 |
| 0.3975 | 15.0 | 1522 | 0.4085 | 0.0825 |
| 0.3686 | 16.0 | 1624 | 0.4031 | 0.0838 |
| 0.3908 | 17.0 | 1725 | 0.4119 | 0.0847 |
| 0.3299 | 18.0 | 1827 | 0.4094 | 0.0804 |
| 0.3255 | 19.0 | 1928 | 0.3857 | 0.0843 |
| 0.3199 | 20.0 | 2030 | 0.3980 | 0.0791 |
| 0.313 | 21.0 | 2131 | 0.3620 | 0.0832 |
| 0.3139 | 22.0 | 2233 | 0.3664 | 0.0814 |
| 0.3038 | 23.0 | 2334 | 0.3604 | 0.0782 |
| 0.2876 | 24.0 | 2436 | 0.3721 | 0.0872 |
| 0.2565 | 25.0 | 2537 | 0.3899 | 0.0875 |
| 0.2627 | 26.0 | 2639 | 0.3699 | 0.0838 |
| 0.2631 | 27.0 | 2740 | 0.3778 | 0.0888 |
| 0.2481 | 28.0 | 2842 | 0.4359 | 0.0902 |
| 0.2631 | 29.0 | 2943 | 0.4279 | 0.0914 |
| 0.2376 | 30.0 | 3045 | 0.4202 | 0.0868 |
| 0.2381 | 31.0 | 3146 | 0.3968 | 0.0848 |
| 0.2474 | 32.0 | 3248 | 0.3963 | 0.0945 |
| 0.2178 | 33.0 | 3349 | 0.4453 | 0.0745 |
| 0.2 | 34.0 | 3451 | 0.4457 | 0.0711 |
| 0.2092 | 35.0 | 3552 | 0.4069 | 0.0786 |
| 0.1934 | 36.0 | 3654 | 0.3756 | 0.0768 |
| 0.1949 | 37.0 | 3755 | 0.3953 | 0.0788 |
| 0.1935 | 38.0 | 3857 | 0.4068 | 0.0765 |
| 0.1944 | 39.0 | 3958 | 0.4095 | 0.0889 |
| 0.2094 | 40.0 | 4060 | 0.3760 | 0.0807 |
| 0.179 | 41.0 | 4161 | 0.4024 | 0.0802 |
| 0.1867 | 42.0 | 4263 | 0.4108 | 0.0823 |
| 0.1865 | 43.0 | 4364 | 0.4671 | 0.0807 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.13.3
|
iach/ModernBERT-large-llm-router
|
iach
| 2025-06-24T20:48:57Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-24T19:57:01Z |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ModernBERT-large-llm-router
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. -->
# ModernBERT-large-llm-router
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0479
- F1: 0.9933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0429 | 1.0 | 479 | 0.0322 | 0.9899 |
| 0.0134 | 2.0 | 958 | 0.0295 | 0.9925 |
| 0.0016 | 3.0 | 1437 | 0.0473 | 0.9927 |
| 0.0002 | 4.0 | 1916 | 0.0476 | 0.9931 |
| 0.0001 | 5.0 | 2395 | 0.0479 | 0.9933 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.1.0
- Tokenizers 0.21.1
|
hectordiazgomez/sirius-1
|
hectordiazgomez
| 2025-06-24T20:47:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"multilingual",
"translation",
"fine-tuned",
"lora",
"text-generation",
"conversational",
"en",
"zh",
"fr",
"de",
"ja",
"ko",
"ru",
"es",
"gu",
"bn",
"kk",
"fa",
"it",
"pt",
"nl",
"sv",
"da",
"fi",
"el",
"cs",
"hu",
"ro",
"bg",
"uk",
"th",
"vi",
"id",
"ms",
"tr",
"pl",
"sw",
"ta",
"te",
"ur",
"ar",
"hi",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-24T20:47:00Z |
---
license: apache-2.0
base_model: google/gemma-3-12b-it
tags:
- multilingual
- translation
- fine-tuned
- peft
- lora
language:
- en
- zh
- fr
- de
- ja
- ko
- ru
- es
- gu
- bn
- kk
- fa
- it
- pt
- nl
- sv
- da
- fi
- el
- cs
- hu
- ro
- bg
- uk
- th
- vi
- id
- ms
- tr
- pl
- sw
- ta
- te
- ur
- ar
- hi
pipeline_tag: text-generation
---
# Sirius-1: Multilingual Translation Model
This is a fine-tuned version of `google/gemma-3-12b-it` for multilingual translation tasks. The model has been trained on 35 languages using LoRA (Low-Rank Adaptation) technique.
## Model Details
- **Base Model**: google/gemma-3-12b-it
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Task**: English-to-X translation
- **Languages Supported**: 35 languages
- **Training Data**: Custom multilingual translation datasets
## Supported Languages
The model supports translation from English to the following 35 languages:
- Chinese (Simplified)
- French
- German
- Japanese
- Korean
- Russian
- Spanish
- Gujarati
- Bengali
- Kazakh
- Persian
- Italian
- Portuguese
- Dutch
- Swedish
- Danish
- Finnish
- Greek
- Czech
- Hungarian
- Romanian
- Bulgarian
- Ukrainian
- Thai
- Vietnamese
- Indonesian
- Malay
- Turkish
- Polish
- Swahili
- Tamil
- Telugu
- Urdu
- Arabic
- Hindi
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("hectordiazgomez/sirius-1")
model = AutoModelForCausalLM.from_pretrained("hectordiazgomez/sirius-1")
# Example translation
input_text = "Translate to French: Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)
```
## Citation
If you use this model, please cite:
```bibtex
@misc{sirius-1-2024,
title={Sirius-1: Multilingual Translation Model},
author={hectordiazgomez},
year={2024},
howpublished={\url{https://huggingface.co/hectordiazgomez/sirius-1}}
}
```
|
New-videos-Filipino-Girl-Call-viral-video/FULL.VIDEO.Filipino.Girl.Call.Viral.Video.Tutorial.Official
|
New-videos-Filipino-Girl-Call-viral-video
| 2025-06-24T20:44:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:43:57Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-100
|
annasoli
| 2025-06-24T20:40:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:14:45Z |
---
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]
|
Czunzun/finbert2_v1
|
Czunzun
| 2025-06-24T20:40:12Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"distilbert",
"dataset:Czunzun/Reuters_first_512",
"base_model:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"base_model:finetune:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"region:us"
] | null | 2025-06-20T18:24:15Z |
---
datasets:
- Czunzun/Reuters_first_512
base_model:
- distilbert/distilbert-base-uncased-finetuned-sst-2-english
---
First version of a new finbert trained on a Reuters tokenized dataset.
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_8_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:36:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
VIDEOS-kamal-kaur-viral-video-Clips/FULL.VIDEO.kamal.kaur.Viral.Video.Tutorial.Official
|
VIDEOS-kamal-kaur-viral-video-Clips
| 2025-06-24T20:32:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:32:15Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?kamal-kaur)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?kamal-kaur)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?kamal-kaur)
|
Satram/Llama_Instruct_Manuales_CV2
|
Satram
| 2025-06-24T20:32:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:31:57Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Satram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
abeni505/amharic-ecommerce-ner
|
abeni505
| 2025-06-24T20:31:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-24T20:30:27Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Cseti/Wan-LoRA-Arcane-Jinx-v2
|
Cseti
| 2025-06-24T20:27:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"lora",
"template:diffusion-lora",
"text-to-video",
"base_model:Wan-AI/Wan2.1-T2V-1.3B",
"base_model:adapter:Wan-AI/Wan2.1-T2V-1.3B",
"region:us"
] |
text-to-video
| 2025-04-18T19:44:42Z |
---
tags:
- lora
- diffusers
- template:diffusion-lora
- text-to-video
widget:
- text: >-
csetiarcane. Nfj1nx lounges on a hammock strung between two trees, her long blue hair spilling around her like a waterfall. She’s dressed in a simple, comfy summer dress, her bare feet propped up against the hammock’s edge. She looks at the camera with a sweet, relaxed smile, one hand resting behind her head as the other holds a cold drink with a small umbrella. The surrounding garden is lush with greenery, and the light creates a warm, inviting glow around her, enhancing the casual and carefree atmosphere
output:
url: images/testb_00229.mp4
- text: >-
csetiarcane. Nfj1nx and her partner share a tender moment in a secluded garden, surrounded by vibrant blooms and the gentle hum of nature. She wears a flowing dress, the fabric swaying gently with the breeze, and her long blue hair frames her face. They are seated on a bench, and Nfj1nx leans her head on their shoulder, her eyes closed in contentment. Her partner brushes a lock of her hair behind her ear, their fingers lingering for a moment longer than necessary. The air between them is thick with unspoken affection, the gentle touch speaking volumes about their bond.csetiarcane.
output:
url: images/testb_00225.mp4
base_model: Wan-AI/Wan2.1-T2V-1.3B
instance_prompt: null
---
# Wan-LoRA-Arcane-Jinx-v2 for the Wan 1.3B model
Latest version of my Wan Jinx lora.
Differences:
- It is a bit weaker,
- follows the prompt better,
- Less bleeding to the other characters
<Gallery />
## Trainig details:
Trained only on videos.
- LR: 1e-5
- Optimizer: adamw_optimi
- epochs: 85
- dataset: 35 videos
- rank: 128
- batch size: 1
- gradient accumulation steps: 1
For training I used the [Diffusion-Pipe](https://github.com/tdrussell/diffusion-pipe/tree/main) repo.
## Inference
It seems, the model likes long descriptive prompts. Look at the attached videos for prompt examples. Based on my tests if you use short prompts, the lora effect is weak.
For inference I recommend:
- [ComfyUI native nodes](https://blog.comfy.org/p/wan21-video-model-native-support)
- [Kijai's ComfyUI wrapper nodes](https://github.com/kijai/ComfyUI-WanVideoWrapper)
<b>Trigger words</b>: csetiarcane, Nfj1nx, blue hair
(If it isn't enough so you don't get the style/character, I recommend adding "animation style" to you prompt. It can help providing the style but in some cases the result will be too cartoony)
<b>Strength</b>: 0.9-1.0
## Important Notes:
This LoRA is created as part of a fan project for research purposes only and is not intended for commercial use. It is based on the TV series called Arcane which are protected by copyright. Users utilize the model at their own risk. Users are obligated to comply with copyright laws and applicable regulations. The model has been developed for non-commercial purposes, and it is not my intention to infringe on any copyright. I assume no responsibility for any damages or legal consequences arising from the use of the model.
## Acknowledgement
Special thanks to [Kijai](https://github.com/kijai) for the tireless and excelent work done for the community to enable us to use these solutions as soon as possible,
as well, as to [Comfyanonymous](https://github.com/comfyanonymous) for implementing them so quickly with such a good quality natively into ComfyUI,
and also to [TDRussel](https://github.com/tdrussell) for making LoRA training available to the community so quickly.
|
BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei
|
BootesVoid
| 2025-06-24T20:17:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T20:17:31Z |
---
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: BRIAMBER
---
# Cmcaxkn920Fazeihnndh489Cj_Cmcaxpx6N0Fdjeihnpjkf8Fei
<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 `BRIAMBER` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BRIAMBER",
"lora_weights": "https://huggingface.co/BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei/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/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei', weight_name='lora.safetensors')
image = pipeline('BRIAMBER').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/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei/discussions) to add images that show off what you’ve made with this LoRA.
|
videos-jobz-hunting-sajal-malik-virals-tv/ULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
|
videos-jobz-hunting-sajal-malik-virals-tv
| 2025-06-24T20:16:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:16:41Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
Lumett/UNetTransplant
|
Lumett
| 2025-06-24T20:14:23Z | 0 | 0 | null |
[
"medical",
"image-segmentation",
"license:apache-2.0",
"region:us"
] |
image-segmentation
| 2025-06-20T16:47:03Z |
---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
---
# U-Net Transplant: Model Merging for 3D Medical Segmentation
This repository contains the implementation of **U-Net Transplant**, a framework for efficient model merging in 3D medical image segmentation. Model merging enables the combination of specialized segmentation models without requiring full retraining, offering a flexible and privacy-conscious solution for updating AI models in clinical applications.
Our approach leverages **task vectors** and encourages **wide minima** during pre-training to enhance the effectiveness of model merging. We evaluate this method using the **ToothFairy2** and **BTCV Abdomen** datasets with a standard **3D U-Net** architecture, demonstrating its ability to integrate multiple specialized segmentation tasks into a single model.
# Pretrain and Task Vector Checkpoints
The related checkpoints and task vectors used in the paper will be available from the 23rd June 2025.
# How to Run
### 1. Clone the Repository
```bash
git clone [email protected]:LucaLumetti/UNetTransplant.git
cd UNetTransplant
```
### 2. Setup Environment
```bash
python -m venv env
source env/bin/activate
pip install -r requirements.txt
```
### 3. Downloads
Ensure the datasets are downloaded and organized following the nnUNet dataset format.
- **BTCV Abdomen**: [Download Here](https://www.synapse.org/Synapse:syn3193805/wiki/217753)
- **ToothFairy2**: [Download Here](https://ditto.ing.unimore.it/toothfairy2/)
- **AMOS**: [Download Here](https://zenodo.org/records/7262581)
- **ZhimingCui**: Available upon request from the authors ([Paper](https://www.nature.com/articles/s41467-022-29637-2))
You can also download pretrained checkpoints and task vectors:
```bash
#!/bin/bash
BASE_ABDOMEN="https://huggingface.co/Lumett/UNetTransplant/resolve/main/Abdomen"
BASE_TOOTHFAIRY="https://huggingface.co/Lumett/UNetTransplant/resolve/main/ToothFairy"
abdomen_files=(
Pretrain_AMOS.pth
TaskVector_Kidney_Abdomen.pth
TaskVector_Liver_Abdomen.pth
TaskVector_Spleen_Abdomen.pth
TaskVector_Stomach_Abdomen.pth
)
toothfairy_files=(
Pretrain_Cui.pth
TaskVector_Canals_ToothFairy2.pth
TaskVector_Mandible_ToothFairy2.pth
TaskVector_Teeth_ToothFairy2.pth
TaskVector_Pharynx_ToothFairy2.pth
)
echo "🩻 Downloading Abdomen files..."
for file in "${abdomen_files[@]}"; do
wget -c "${BASE_ABDOMEN}/${file}"
done
echo "🦷 Downloading ToothFairy files..."
for file in "${toothfairy_files[@]}"; do
wget -c "${BASE_TOOTHFAIRY}/${file}"
done
```
### 4. Running the U-Net Transplant Framework
The main script for running experiments is `main.py`. It requires specifying the type of experiment and a configuration file that defines dataset, model, optimizer, and training parameters.
#### Command Structure
```bash
python main.py --experiment <EXPERIMENT_TYPE> --config <CONFIG_PATH> [--expname <NAME>] [--override <PARAMS>]
```
#### Arguments
- **`--experiment`**: Specifies the type of experiment to run.
- `"PretrainExperiment"` → Pretrains the model from scratch.
- `"TaskVectorTrainExperiment"` → Trains a task vector using a pretrained checkpoint.
- **`--config`**: Path to the configuration file, which defines dataset, model, and training settings.
- **`--expname`** (optional): Custom experiment name. If not provided, the config filename is used.
- **`--override`** (optional): Allows overriding config values at runtime. Example:
```bash
python main.py --experiment PretrainExperiment --config configs/default.yaml --override DataConfig.BATCH_SIZE=4 OptimizerConfig.LR=0.01
```
#### Configuration File
The configuration file defines:
- **Dataset** (`DataConfig`): Path, batch size, patch size, and datasets used.
- **Model** (`BackboneConfig` & `HeadsConfig`): Architecture, checkpoints, and initialization.
- **Optimizer** (`OptimizerConfig`): Learning rates, weight decay, and momentum.
- **Loss Function** (`LossConfig`): Defines the loss function used.
- **Training** (`TrainConfig`): Number of epochs, checkpoint saving, and resume options.
Check [the provided configs](https://github.com/LucaLumetti/UNetTransplant/tree/main/configs/miccai2025) for examples.
#### Example Commands
1. **Pretraining a model**:
```bash
python main.py --experiment PretrainExperiment --config configs/miccai2025/pretrain_stable.yaml
```
2. **Training a task vector from a checkpoint**:
```bash
python main.py --experiment TaskVectorTrainExperiment --config configs/miccai2025/finetune.yaml --override BackboneConfig.PRETRAIN_CHECKPOINTS="/path/to/checkpoint.pth"
```
For further details, refer to the config files used in our experiments under the `configs` folder.
### 5. Cite
If you used our work, please cite it:
```
@incollection{lumetti2025u,
title={U-Net Transplant: The Role of Pre-training for Model Merging in 3D Medical Segmentation},
author={Lumetti, Luca and Capitani, Giacomo and Ficarra, Elisa and Grana, Costantino and Calderara, Simone and Porrello, Angelo and Bolelli, Federico and others},
booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2025},
year={2025}
}
```
|
abdullahsubasi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
|
abdullahsubasi
| 2025-06-24T20:13:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am graceful pensive puffin",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:12:55Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am graceful pensive puffin
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="abdullahsubasi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin", 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: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
hasdal/3582e9a3-5559-471c-b56d-af218b5d749b
|
hasdal
| 2025-06-24T20:10:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-24T16:24:12Z |
---
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]
|
IExploitableMan/Lmao
|
IExploitableMan
| 2025-06-24T20:05:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"lora",
"flux",
"image-to-image",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
image-to-image
| 2025-06-24T20:02:17Z |
---
base_model:
- black-forest-labs/FLUX.1-dev
pipeline_tag: image-to-image
tags:
- lora
- diffusers
- flux
---
|
New-Clip-job-guru-online-18-viral-Videos/FULL.VIDEO.job.guru.online.Viral.Video.Tutorial.Official
|
New-Clip-job-guru-online-18-viral-Videos
| 2025-06-24T20:04:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:04:30Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
|
torVik/Qwen2-VL-7B-Defects
|
torVik
| 2025-06-24T20:04:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T11:24:24Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-bnb-4bit
library_name: transformers
model_name: Qwen2-VL-7B-Defects
tags:
- generated_from_trainer
- unsloth
- sft
- trl
licence: license
---
# Model Card for Qwen2-VL-7B-Defects
This model is a fine-tuned version of [unsloth/llama-3.2-11b-vision-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-11b-vision-instruct-bnb-4bit).
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="torVik/Qwen2-VL-7B-Defects", 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/viktortu/huggingface/runs/uum6g5vw)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
Ricky131/model-hoax-gpt2
|
Ricky131
| 2025-06-24T20:03:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T20:03:01Z |
---
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]
|
BootesVoid/cmc9t234o02c0eihnz1fc9s31_cmcax8lq50f5zeihntexuahzq
|
BootesVoid
| 2025-06-24T20:03:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T20:03:08Z |
---
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: ISELA
---
# Cmc9T234O02C0Eihnz1Fc9S31_Cmcax8Lq50F5Zeihntexuahzq
<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 `ISELA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ISELA",
"lora_weights": "https://huggingface.co/BootesVoid/cmc9t234o02c0eihnz1fc9s31_cmcax8lq50f5zeihntexuahzq/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/cmc9t234o02c0eihnz1fc9s31_cmcax8lq50f5zeihntexuahzq', weight_name='lora.safetensors')
image = pipeline('ISELA').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/cmc9t234o02c0eihnz1fc9s31_cmcax8lq50f5zeihntexuahzq/discussions) to add images that show off what you’ve made with this LoRA.
|
19-VIDEOS-DE-ANABEL-ANGUS-Y-MARCO-ANTELO/FULL.18VIDEO.DE.ANABEL.ANGUS.Y.MARCO.ANTELO
|
19-VIDEOS-DE-ANABEL-ANGUS-Y-MARCO-ANTELO
| 2025-06-24T20:00:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:00:31Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
|
18-Official-Mezzo-fun-viral-Video/FULL.VIDEO.LINK.Mezzo.fun.Viral.Video.Tutorial.Official
|
18-Official-Mezzo-fun-viral-Video
| 2025-06-24T20:00:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:00:07Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
New-videos-Redeem-Craze-viral-Clips/FULL.VIDEO.Redeem.Craze.Viral.Video.Tutorial.Official
|
New-videos-Redeem-Craze-viral-Clips
| 2025-06-24T20:00:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:00:06Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
StephenGenusa/Jan-nano-Q8_0-GGUF
|
StephenGenusa
| 2025-06-24T19:58:02Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Menlo/Jan-nano",
"base_model:quantized:Menlo/Jan-nano",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-24T19:57:43Z |
---
license: apache-2.0
base_model: Menlo/Jan-nano
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# StephenGenusa/Jan-nano-Q8_0-GGUF
This model was converted to GGUF format from [`Menlo/Jan-nano`](https://huggingface.co/Menlo/Jan-nano) 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/Menlo/Jan-nano) 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 StephenGenusa/Jan-nano-Q8_0-GGUF --hf-file jan-nano-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo StephenGenusa/Jan-nano-Q8_0-GGUF --hf-file jan-nano-q8_0.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 StephenGenusa/Jan-nano-Q8_0-GGUF --hf-file jan-nano-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo StephenGenusa/Jan-nano-Q8_0-GGUF --hf-file jan-nano-q8_0.gguf -c 2048
```
|
mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF
|
mradermacher
| 2025-06-24T19:55:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:suanjia/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5",
"base_model:quantized:suanjia/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T16:12:08Z |
---
base_model: suanjia/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/suanjia/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-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/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-GuiyangGuian-lora-v1.5.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 -->
|
4k-video-anabel-angus-y-marco-antelo/HDQScandle.FULL.18.VIDEO.DE.ANABEL.ANGUS.Y.MARCO.ANTELO
|
4k-video-anabel-angus-y-marco-antelo
| 2025-06-24T19:55:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:54:54Z |
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instagram-viral-video-A2Z-Jankari/18-wATCH.A2Z.Jankari.viral.video.original.Link.Official
|
instagram-viral-video-A2Z-Jankari
| 2025-06-24T19:53:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:52:52Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Download)
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|
hasdal/b964e913-bad3-4989-ac59-5b08ba34cc0c
|
hasdal
| 2025-06-24T19:51:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T13:44:22Z |
---
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]
|
VIDEOS-Monique-McMahon-Colour-Viral-Video/FULL.VIDEO.Monique.McMahon.Colour.Viral.Video.Tutorial.Official
|
VIDEOS-Monique-McMahon-Colour-Viral-Video
| 2025-06-24T19:51:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:50:50Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
ver-Zinti-Palmares-portera-polemica-video/Ver.Zinti.Palmares.portera.polemica.viral.de.un.video.de.la.jugadora
|
ver-Zinti-Palmares-portera-polemica-video
| 2025-06-24T19:49:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:48:27Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
videos-jobz-hunting-sajal-malik-virals/ULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
|
videos-jobz-hunting-sajal-malik-virals
| 2025-06-24T19:47:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:38:06Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Download)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Download)
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|
versaceeros/8eccd70f-6e10-4e52-a2c6-43725e49f73f
|
versaceeros
| 2025-06-24T19:44:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T13:45:09Z |
---
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]
|
pruvolo/my_awesome_qa_model
|
pruvolo
| 2025-06-24T19:38:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-06-24T19:28:37Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8087
## 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.6352 |
| 2.8395 | 2.0 | 500 | 1.9117 |
| 2.8395 | 3.0 | 750 | 1.8087 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Rakshith0808/hotel-faq-t5
|
Rakshith0808
| 2025-06-24T19:37:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-24T19:36:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Shaleen123/Qwen3-8b-Reasoning-SFT
|
Shaleen123
| 2025-06-24T19:36:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T19:34:08Z |
---
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]
|
shaddie/rocketpill_thrustcurve_informer_model
|
shaddie
| 2025-06-24T19:33:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"informer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T17:54:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sergioalves/1ef9a8a3-6ae0-4a69-8869-9b050b76a258
|
sergioalves
| 2025-06-24T19:32:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab",
"base_model:adapter:samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-24T19:07:30Z |
---
library_name: peft
base_model: samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1ef9a8a3-6ae0-4a69-8869-9b050b76a258
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b578b60be39aff41_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.9
group_by_length: false
hub_model_id: sergioalves/1ef9a8a3-6ae0-4a69-8869-9b050b76a258
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/b578b60be39aff41_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 22dc36e2-63f4-41a1-8eb8-c0a28b2cc74b
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 22dc36e2-63f4-41a1-8eb8-c0a28b2cc74b
warmup_steps: 10
weight_decay: 0.05
xformers_attention: false
```
</details><br>
# 1ef9a8a3-6ae0-4a69-8869-9b050b76a258
This model is a fine-tuned version of [samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab](https://huggingface.co/samoline/b35b8929-3bde-4879-89aa-c4a8dc1a74ab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7324
## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6719 | 0.0003 | 1 | 0.7471 |
| 0.9133 | 0.0142 | 50 | 0.7342 |
| 0.9178 | 0.0283 | 100 | 0.7324 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
BootesVoid/cmcavw8q80emaeihnxvxl141l_cmcavzphq0enweihnvsb5hz02
|
BootesVoid
| 2025-06-24T19:30:00Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T19:29:59Z |
---
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: COUGAR
---
# Cmcavw8Q80Emaeihnxvxl141L_Cmcavzphq0Enweihnvsb5Hz02
<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 `COUGAR` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "COUGAR",
"lora_weights": "https://huggingface.co/BootesVoid/cmcavw8q80emaeihnxvxl141l_cmcavzphq0enweihnvsb5hz02/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/cmcavw8q80emaeihnxvxl141l_cmcavzphq0enweihnvsb5hz02', weight_name='lora.safetensors')
image = pipeline('COUGAR').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/cmcavw8q80emaeihnxvxl141l_cmcavzphq0enweihnvsb5hz02/discussions) to add images that show off what you’ve made with this LoRA.
|
New-videos-Monique-McMahon-Colour-viral/FULL.VIDEO.Monique.McMahon.Colour.Viral.Video.Tutorial.Official
|
New-videos-Monique-McMahon-Colour-viral
| 2025-06-24T19:29:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:29:29Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
zecaihong/3e7e19dc-0007-4038-bacf-b95d034953d3
|
zecaihong
| 2025-06-24T19:26:51Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-7B",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T18:03:48Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3e7e19dc-0007-4038-bacf-b95d034953d3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5686eaedee397c04_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_prompt: ''
debug: null
deepspeed: deepspeed_configs/zero2.json
early_stopping_patience: 3
eval_max_new_tokens: 1024
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
greater_is_better: false
group_by_length: false
hub_model_id: zecaihong/3e7e19dc-0007-4038-bacf-b95d034953d3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: constant
max_steps: -1
metric_for_best_model: eval_loss
micro_batch_size: 8
mlflow_experiment_name: /data/datasets/5686eaedee397c04_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 6
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3e7e19dc-0007-4038-bacf-b95d034953d3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3e7e19dc-0007-4038-bacf-b95d034953d3
warmup_steps: 100
weight_decay: 0.001
xformers_attention: null
```
</details><br>
# 3e7e19dc-0007-4038-bacf-b95d034953d3
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 100
- num_epochs: 6.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0035 | 1 | 1.7089 |
| 0.8852 | 0.3484 | 100 | 0.9274 |
| 0.8515 | 0.6969 | 200 | 0.8840 |
| 0.7917 | 1.0453 | 300 | 0.8568 |
| 0.7826 | 1.3937 | 400 | 0.8390 |
| 0.7856 | 1.7422 | 500 | 0.8257 |
| 0.7435 | 2.0906 | 600 | 0.8176 |
| 0.7495 | 2.4390 | 700 | 0.8112 |
| 0.7485 | 2.7875 | 800 | 0.8033 |
| 0.696 | 3.1359 | 900 | 0.8057 |
| 0.7027 | 3.4843 | 1000 | 0.8010 |
| 0.7057 | 3.8328 | 1100 | 0.7970 |
| 0.6495 | 4.1812 | 1200 | 0.8026 |
| 0.6714 | 4.5296 | 1300 | 0.8012 |
| 0.6648 | 4.8780 | 1400 | 0.7965 |
| 0.6074 | 5.2265 | 1500 | 0.8099 |
| 0.6101 | 5.5749 | 1600 | 0.8089 |
| 0.6313 | 5.9233 | 1700 | 0.8057 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
boltuix/bert-micro
|
boltuix
| 2025-06-24T19:25:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"NLP",
"MIT-NLP-v1",
"en",
"dataset:BookCorpus",
"dataset:Wikipedia",
"arxiv:1908.08962",
"arxiv:1810.04805",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-24T19:24:33Z |
---
license: mit
language:
- en
metrics:
- precision
- recall
- f1
- accuracy
new_version: v1.0
datasets:
- BookCorpus
- Wikipedia
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
- NLP
- MIT-NLP-v1
base_model:
- google/bert-base-uncased
library_name: transformers
---
[](https://opensource.org/licenses/MIT)
[](#)
[](#)
[](#)
# Model Card for boltuix/bert-micro
The `boltuix/bert-micro` model is the smallest BERT variant in the BoltUIX family, designed for natural language processing tasks requiring blazing-fast performance in highly resource-constrained environments. Pretrained on English text using masked language modeling (MLM) and next sentence prediction (NSP) objectives, it is optimized for fine-tuning on lightweight NLP tasks, such as basic sequence classification and token classification. With a size of ~15 MB, it offers moderate accuracy for applications prioritizing speed and efficiency over high precision.
## Model Details
### Model Description
The `boltuix/bert-micro` model is a PyTorch-based transformer model derived from TensorFlow checkpoints in the Google BERT repository. It builds on research from *On the Importance of Pre-training Compact Models* ([arXiv](https://arxiv.org/abs/1908.08962)) and *Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics* ([arXiv](https://arxiv.org/abs/1908.08962)). Ported to Hugging Face, this uncased model (~15 MB) is engineered for minimal NLP applications, such as basic sentiment analysis and named entity recognition, making it ideal for developers and researchers targeting ultra-lightweight deployments on edge devices.
- **Developed by:** BoltUIX
- **Funded by:** BoltUIX Research Fund
- **Shared by:** Hugging Face
- **Model type:** Transformer (BERT)
- **Language(s) (NLP):** English (`en`)
- **License:** MIT
- **Finetuned from model:** google-bert/bert-base-uncased
### Model Sources
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/boltuix/bert-micro)
- **Paper:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](http://arxiv.org/abs/1810.04805)
- **Demo:** [Hugging Face Spaces Demo](https://huggingface.co/spaces/boltuix/bert-micro-demo)
## Model Variants
BoltUIX offers a range of BERT-based models tailored to different performance and resource requirements. The `boltuix/bert-micro` model is the smallest and fastest option, ideal for applications needing minimal resource usage with moderate accuracy. Below is a summary of available models:
| Tier | Model ID | Size (MB) | Notes |
|------------|-------------------------|-----------|----------------------------------------------------|
| Micro | boltuix/bert-micro | ~15 MB | Smallest, blazing-fast, moderate accuracy |
| Mini | boltuix/bert-mini | ~17 MB | Ultra-compact, fast, slightly better accuracy |
| Tinyplus | boltuix/bert-tinyplus | ~20 MB | Slightly bigger, better capacity |
| Small | boltuix/bert-small | ~45 MB | Good compact/accuracy balance |
| Mid | boltuix/bert-mid | ~50 MB | Well-rounded mid-tier performance |
| Medium | boltuix/bert-medium | ~160 MB | Strong general-purpose model |
| Large | boltuix/bert-large | ~365 MB | Top performer below full-BERT |
| Pro | boltuix/bert-pro | ~420 MB | Use only if max accuracy is mandatory |
| Mobile | boltuix/bert-mobile | ~140 MB | Mobile-optimized; quantize to ~25 MB with no major loss |
For more details on each variant, visit the [BoltUIX Model Hub](https://huggingface.co/boltuix).
## Uses
### Direct Use
The model can be used directly for masked language modeling or next sentence prediction tasks, such as predicting missing words in sentences or determining sentence coherence, delivering moderate accuracy in these core tasks.
### Downstream Use
The model is designed for fine-tuning on lightweight downstream NLP tasks, including:
- Basic sequence classification (e.g., simple sentiment analysis, intent detection)
- Token classification (e.g., named entity recognition)
- Simple question answering (e.g., basic extractive QA)
It is recommended for developers and researchers working on highly resource-constrained devices, such as low-power edge devices, where speed and minimal resource usage are critical.
### Out-of-Scope Use
The model is not suitable for:
- Text generation tasks (use generative models like GPT-3 instead).
- Non-English language tasks without significant fine-tuning.
- Applications requiring high accuracy (use `boltuix/bert-tinyplus`, `boltuix/bert-small`, or larger variants instead).
## Bias, Risks, and Limitations
The model may inherit biases from its training data (BookCorpus and English Wikipedia), potentially reinforcing stereotypes, such as gender or occupational biases. For example:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='boltuix/bert-micro')
unmasker("The man worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the man worked as a engineer. [SEP]', 'token_str': 'engineer'},
{'sequence': '[CLS] the man worked as a doctor. [SEP]', 'token_str': 'doctor'},
...
]
```
```python
unmasker("The woman worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the woman worked as a teacher. [SEP]', 'token_str': 'teacher'},
{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'token_str': 'nurse'},
...
]
```
These biases may propagate to downstream tasks. Due to its minimal size (~15 MB), the model is highly efficient but has limited capacity for complex tasks, making it less suitable for applications requiring robust performance.
### Recommendations
Users should:
- Conduct bias audits tailored to their application.
- Fine-tune with diverse, representative datasets to reduce bias.
- Apply model compression techniques (e.g., quantization) for deployment on ultra-constrained devices.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import pipeline, BertTokenizer, BertModel
# Masked Language Modeling
unmasker = pipeline('fill-mask', model='boltuix/bert-micro')
result = unmasker("Hello I'm a [MASK] model.")
print(result)
# Feature Extraction (PyTorch)
tokenizer = BertTokenizer.from_pretrained('boltuix/bert-micro')
model = BertModel.from_pretrained('boltuix/bert-micro')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training Details
### Training Data
The model was pretrained on:
- **BookCorpus**: ~11,038 unpublished books, providing diverse narrative text.
- **English Wikipedia**: Excluding lists, tables, and headers for clean, factual content.
See the [BoltUIX Dataset Card](https://huggingface.co/boltuix/datasets) for more details.
### Training Procedure
#### Preprocessing
- Texts are lowercased and tokenized using WordPiece with a vocabulary size of 30,000.
- Inputs are formatted as: `[CLS] Sentence A [SEP] Sentence B [SEP]`.
- 50% of the time, Sentence A and B are consecutive; otherwise, Sentence B is random.
- Masking:
- 15% of tokens are masked.
- 80% of masked tokens are replaced with `[MASK]`.
- 10% are replaced with a random token.
- 10% are left unchanged.
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
- **Optimizer**: Adam (learning rate 1e-4, β1=0.9, β2=0.999, weight decay 0.01)
- **Batch size**: 32
- **Steps**: 400,000
- **Sequence length**: 128 tokens (99% of steps), 512 tokens (1% of steps)
- **Warmup**: 4,000 steps with linear learning rate decay
#### Speeds, Sizes, Times
- **Training time**: Approximately 40 hours
- **Checkpoint size**: ~15 MB
- **Throughput**: ~180 sentences/second on TPU infrastructure
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
Evaluated on the GLUE benchmark, including tasks like MNLI, QQP, QNLI, SST-2, CoLA, STS-B, MRPC, and RTE.
#### Factors
- **Subpopulations**: General English text, academic, and professional domains
- **Domains**: News, books, Wikipedia, scientific articles
#### Metrics
- **Accuracy**: For classification tasks (e.g., MNLI, SST-2)
- **F1 Score**: For tasks like QQP, MRPC
- **Pearson/Spearman Correlation**: For STS-B
### Results
GLUE test results (fine-tuned):
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|------------|-------------|------|------|-------|------|-------|------|------|---------|
| Score | 80.5/79.4 | 68.7 | 86.5 | 89.3 | 46.3 | 81.2 | 84.1 | 62.4 | 75.5 |
#### Summary
The model provides moderate performance across GLUE tasks, with acceptable results in SST-2 and QNLI. It is suitable for basic NLP tasks in resource-constrained environments, offering blazing-fast inference with minimal resource usage.
## Model Examination
The model’s attention mechanisms were analyzed to ensure minimal but functional contextual understanding, with no significant overfitting observed during pretraining. Ablation studies validated the training configuration for ultra-lightweight performance.
## Environmental Impact
Carbon emissions estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) from [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type**: 1 cloud TPU (4 TPU chips)
- **Hours used**: 40 hours
- **Cloud Provider**: Google Cloud
- **Compute Region**: us-central1
- **Carbon Emitted**: ~30 kg CO2eq (estimated based on TPU energy consumption and regional grid carbon intensity)
## Technical Specifications
### Model Architecture and Objective
- **Architecture**: BERT (transformer-based, bidirectional)
- **Objective**: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
- **Layers**: 2
- **Hidden Size**: 128
- **Attention Heads**: 2
### Compute Infrastructure
#### Hardware
- 1 cloud TPU (4 TPU chips total)
#### Software
- PyTorch
- Transformers library (Hugging Face)
## Citation
**BibTeX:**
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805}
}
```
**APA:**
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. *CoRR, abs/1810.04805*. http://arxiv.org/abs/1810.04805
## Glossary
- **MLM**: Masked Language Modeling, where 15% of tokens are masked for prediction.
- **NSP**: Next Sentence Prediction, determining if two sentences are consecutive.
- **WordPiece**: Tokenization method splitting words into subword units.
## More Information
- See the [Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/bert) for advanced usage details.
- Contact: [email protected]
## Model Card Authors
- Hugging Face team
- BoltUIX contributors
## Model Card Contact
For questions, please contact [email protected] or open an issue on the [model repository](https://huggingface.co/boltuix/bert-micro).
|
afurkany/my-awesome-model
|
afurkany
| 2025-06-24T19:25:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-24T19:23:57Z |
---
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]
|
Berdoldi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_prickly_owl
|
Berdoldi
| 2025-06-24T19:23:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vicious prickly owl",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T04:39:18Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_prickly_owl
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vicious prickly owl
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_prickly_owl
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="Berdoldi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_prickly_owl", 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: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
New-videos-mezzo-fun-viral-Clips-XnXX/New.tutorial.mezzo.fun.Viral.Video.Leaks.Official
|
New-videos-mezzo-fun-viral-Clips-XnXX
| 2025-06-24T19:23:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T19:23:04Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
Naphon/phi4-thai-financial-instruct
|
Naphon
| 2025-06-24T19:20:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"unsloth",
"base_model:unsloth/phi-4-unsloth-bnb-4bit",
"base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T15:13:33Z |
---
base_model: unsloth/phi-4-unsloth-bnb-4bit
library_name: transformers
model_name: phi4-thai-financial-instruct
tags:
- generated_from_trainer
- trl
- sft
- unsloth
licence: license
---
# Model Card for phi4-thai-financial-instruct
This model is a fine-tuned version of [unsloth/phi-4-unsloth-bnb-4bit](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit).
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="Naphon/phi4-thai-financial-instruct", 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: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmcaljdoa099ieihnlu6kfj02_cmcav83v00ed5eihn6qdm7eln
|
BootesVoid
| 2025-06-24T19:18:11Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T19:18:08Z |
---
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: STUNNING
---
# Cmcaljdoa099Ieihnlu6Kfj02_Cmcav83V00Ed5Eihn6Qdm7Eln
<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 `STUNNING` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "STUNNING",
"lora_weights": "https://huggingface.co/BootesVoid/cmcaljdoa099ieihnlu6kfj02_cmcav83v00ed5eihn6qdm7eln/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/cmcaljdoa099ieihnlu6kfj02_cmcav83v00ed5eihn6qdm7eln', weight_name='lora.safetensors')
image = pipeline('STUNNING').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/cmcaljdoa099ieihnlu6kfj02_cmcav83v00ed5eihn6qdm7eln/discussions) to add images that show off what you’ve made with this LoRA.
|
sommerzen/EuroMoE-German-experiment2-F16-GGUF
|
sommerzen
| 2025-06-24T19:16:22Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mixtral",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:sommerzen/EuroMoE-German-experiment2",
"base_model:quantized:sommerzen/EuroMoE-German-experiment2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T19:16:20Z |
---
base_model: sommerzen/EuroMoE-German-experiment2
tags:
- text-generation-inference
- transformers
- unsloth
- mixtral
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# sommerzen/EuroMoE-German-experiment2-F16-GGUF
This LoRA adapter was converted to GGUF format from [`sommerzen/EuroMoE-German-experiment2`](https://huggingface.co/sommerzen/EuroMoE-German-experiment2) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/sommerzen/EuroMoE-German-experiment2) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora EuroMoE-German-experiment2-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora EuroMoE-German-experiment2-f16.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
boltuix/bert-mid
|
boltuix
| 2025-06-24T19:15:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"NLP",
"MIT-NLP-v1",
"en",
"dataset:BookCorpus",
"dataset:Wikipedia",
"arxiv:1908.08962",
"arxiv:1810.04805",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-24T19:11:52Z |
---
license: mit
language:
- en
metrics:
- precision
- recall
- f1
- accuracy
new_version: v1.0
datasets:
- BookCorpus
- Wikipedia
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
- NLP
- MIT-NLP-v1
base_model:
- google/bert-base-uncased
library_name: transformers
---
[](https://opensource.org/licenses/MIT)
[](#)
[](#)
[](#)
# Model Card for boltuix/bert-mid
The `boltuix/bert-mid` model is a compact BERT variant designed for natural language processing tasks requiring well-rounded performance with moderate resource demands. Pretrained on English text using masked language modeling (MLM) and next sentence prediction (NSP) objectives, it is optimized for fine-tuning on a variety of NLP tasks, including sequence classification, token classification, and question answering. With a size of ~50 MB, it offers a balanced solution for applications needing solid accuracy and efficiency, ideal for mid-tier deployments.
## Model Details
### Model Description
The `boltuix/bert-mid` model is a PyTorch-based transformer model derived from TensorFlow checkpoints in the Google BERT repository. It builds on research from *On the Importance of Pre-training Compact Models* ([arXiv](https://arxiv.org/abs/1908.08962)) and *Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics* ([arXiv](https://arxiv.org/abs/1908.08962)). Ported to Hugging Face, this uncased model (~50 MB) is engineered for mid-tier NLP applications, such as sentiment analysis, named entity recognition, and natural language inference, making it suitable for developers and researchers seeking a cost-effective, balanced model.
- **Developed by:** BoltUIX
- **Funded by:** BoltUIX Research Fund
- **Shared by:** Hugging Face
- **Model type:** Transformer (BERT)
- **Language(s) (NLP):** English (`en`)
- **License:** MIT
- **Finetuned from model:** google-bert/bert-base-uncased
### Model Sources
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/boltuix/bert-mid)
- **Paper:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](http://arxiv.org/abs/1810.04805)
- **Demo:** [Hugging Face Spaces Demo](https://huggingface.co/spaces/boltuix/bert-mid-demo)
## Model Variants
BoltUIX offers a range of BERT-based models tailored to different performance and resource requirements. The `boltuix/bert-mid` model is a well-rounded mid-tier option, ideal for applications needing balanced accuracy and efficiency. Below is a summary of available models:
| Tier | Model ID | Size (MB) | Notes |
|------------|-------------------------|-----------|----------------------------------------------------|
| Micro | boltuix/bert-micro | ~15 MB | Smallest, blazing-fast, moderate accuracy |
| Mini | boltuix/bert-mini | ~17 MB | Ultra-compact, fast, slightly better accuracy |
| Tinyplus | boltuix/bert-tinyplus | ~20 MB | Slightly bigger, better capacity |
| Small | boltuix/bert-small | ~45 MB | Good compact/accuracy balance |
| Mid | boltuix/bert-mid | ~50 MB | Well-rounded mid-tier performance |
| Medium | boltuix/bert-medium | ~160 MB | Strong general-purpose model |
| Large | boltuix/bert-large | ~365 MB | Top performer below full-BERT |
| Pro | boltuix/bert-pro | ~420 MB | Use only if max accuracy is mandatory |
| Mobile | boltuix/bert-mobile | ~140 MB | Mobile-optimized; quantize to ~25 MB with no major loss |
For more details on each variant, visit the [BoltUIX Model Hub](https://huggingface.co/boltuix).
## Uses
### Direct Use
The model can be used directly for masked language modeling or next sentence prediction tasks, such as predicting missing words in sentences or determining sentence coherence, delivering balanced accuracy in these core tasks.
### Downstream Use
The model is designed for fine-tuning on a range of downstream NLP tasks, including:
- Sequence classification (e.g., sentiment analysis, intent detection)
- Token classification (e.g., named entity recognition, part-of-speech tagging)
- Question answering (e.g., extractive QA, reading comprehension)
- Natural language inference (e.g., MNLI, RTE)
It is recommended for developers, researchers, and small-to-medium enterprises seeking a mid-tier NLP model with solid performance and efficient resource usage.
### Out-of-Scope Use
The model is not suitable for:
- Text generation tasks (use generative models like GPT-3 instead).
- Non-English language tasks without significant fine-tuning.
- High-performance applications requiring maximum accuracy (use `boltuix/bert-large` or `boltuix/bert-pro` instead).
## Bias, Risks, and Limitations
The model may inherit biases from its training data (BookCorpus and English Wikipedia), potentially reinforcing stereotypes, such as gender or occupational biases. For example:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='boltuix/bert-mid')
unmasker("The man worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the man worked as a engineer. [SEP]', 'token_str': 'engineer'},
{'sequence': '[CLS] the man worked as a doctor. [SEP]', 'token_str': 'doctor'},
...
]
```
```python
unmasker("The woman worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the woman worked as a teacher. [SEP]', 'token_str': 'teacher'},
{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'token_str': 'nurse'},
...
]
```
These biases may propagate to downstream tasks. Due to its size (~50 MB), the model is suitable for many devices but may still require optimization for ultra-constrained environments.
### Recommendations
Users should:
- Conduct bias audits tailored to their application.
- Fine-tune with diverse, representative datasets to reduce bias.
- Apply model compression techniques (e.g., quantization, pruning) for deployment on resource-constrained devices.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import pipeline, BertTokenizer, BertModel
# Masked Language Modeling
unmasker = pipeline('fill-mask', model='boltuix/bert-mid')
result = unmasker("Hello I'm a [MASK] model.")
print(result)
# Feature Extraction (PyTorch)
tokenizer = BertTokenizer.from_pretrained('boltuix/bert-mid')
model = BertModel.from_pretrained('boltuix/bert-mid')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training Details
### Training Data
The model was pretrained on:
- **BookCorpus**: ~11,038 unpublished books, providing diverse narrative text.
- **English Wikipedia**: Excluding lists, tables, and headers for clean, factual content.
See the [BoltUIX Dataset Card](https://huggingface.co/boltuix/datasets) for more details.
### Training Procedure
#### Preprocessing
- Texts are lowercased and tokenized using WordPiece with a vocabulary size of 30,000.
- Inputs are formatted as: `[CLS] Sentence A [SEP] Sentence B [SEP]`.
- 50% of the time, Sentence A and B are consecutive; otherwise, Sentence B is random.
- Masking:
- 15% of tokens are masked.
- 80% of masked tokens are replaced with `[MASK]`.
- 10% are replaced with a random token.
- 10% are left unchanged.
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
- **Optimizer**: Adam (learning rate 1e-4, β1=0.9, β2=0.999, weight decay 0.01)
- **Batch size**: 128
- **Steps**: 800,000
- **Sequence length**: 128 tokens (95% of steps), 512 tokens (5% of steps)
- **Warmup**: 8,000 steps with linear learning rate decay
#### Speeds, Sizes, Times
- **Training time**: Approximately 120 hours
- **Checkpoint size**: ~50 MB
- **Throughput**: ~120 sentences/second on TPU infrastructure
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
Evaluated on the GLUE benchmark, including tasks like MNLI, QQP, QNLI, SST-2, CoLA, STS-B, MRPC, and RTE.
#### Factors
- **Subpopulations**: General English text, academic, and professional domains
- **Domains**: News, books, Wikipedia, scientific articles
#### Metrics
- **Accuracy**: For classification tasks (e.g., MNLI, SST-2)
- **F1 Score**: For tasks like QQP, MRPC
- **Pearson/Spearman Correlation**: For STS-B
### Results
GLUE test results (fine-tuned):
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|------------|-------------|------|------|-------|------|-------|------|------|---------|
| Score | 83.5/82.3 | 70.9 | 89.4 | 92.1 | 50.7 | 84.6 | 87.5 | 65.3 | 78.3 |
#### Summary
The model delivers balanced performance across GLUE tasks, with solid results in SST-2 and QNLI. It outperforms smaller BERT variants like `boltuix/bert-small` in tasks such as RTE and CoLA, making it a well-rounded mid-tier option.
## Model Examination
The model’s attention mechanisms were analyzed to ensure effective contextual understanding, with no significant overfitting observed during pretraining. Ablation studies confirmed the suitability of the training configuration for mid-tier performance.
## Environmental Impact
Carbon emissions estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) from [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type**: 2 cloud TPUs (8 TPU chips)
- **Hours used**: 120 hours
- **Cloud Provider**: Google Cloud
- **Compute Region**: us-central1
- **Carbon Emitted**: ~80 kg CO2eq (estimated based on TPU energy consumption and regional grid carbon intensity)
## Technical Specifications
### Model Architecture and Objective
- **Architecture**: BERT (transformer-based, bidirectional)
- **Objective**: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
- **Layers**: 6
- **Hidden Size**: 512
- **Attention Heads**: 8
### Compute Infrastructure
#### Hardware
- 2 cloud TPUs in Pod configuration (8 TPU chips total)
#### Software
- PyTorch
- Transformers library (Hugging Face)
## Citation
**BibTeX:**
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805}
}
```
**APA:**
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. *CoRR, abs/1810.04805*. http://arxiv.org/abs/1810.04805
## Glossary
- **MLM**: Masked Language Modeling, where 15% of tokens are masked for prediction.
- **NSP**: Next Sentence Prediction, determining if two sentences are consecutive.
- **WordPiece**: Tokenization method splitting words into subword units.
## More Information
- See the [Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/bert) for advanced usage details.
- Contact: [email protected]
## Model Card Authors
- Hugging Face team
- BoltUIX contributors
## Model Card Contact
For questions, please contact [email protected] or open an issue on the [model repository](https://huggingface.co/boltuix/bert-mid).
|
dimsavva/react_table14
|
dimsavva
| 2025-06-24T19:15:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen3-14B",
"base_model:finetune:unsloth/Qwen3-14B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T19:11:58Z |
---
base_model: unsloth/Qwen3-14B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dimsavva
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rajan-chaudhary/humanizer-v1
|
rajan-chaudhary
| 2025-06-24T19:10:05Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T19:10:05Z |
---
license: apache-2.0
---
|
boltuix/bert-pro
|
boltuix
| 2025-06-24T19:05:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"NLP",
"MIT-NLP-v1",
"en",
"dataset:BookCorpus",
"dataset:Wikipedia",
"arxiv:1908.08962",
"arxiv:1810.04805",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-24T18:22:58Z |
---
license: mit
language:
- en
metrics:
- precision
- recall
- f1
- accuracy
new_version: v1.0
datasets:
- BookCorpus
- Wikipedia
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
- NLP
- MIT-NLP-v1
base_model:
- google/bert-base-uncased
library_name: transformers
---
[](https://opensource.org/licenses/MIT)
[](#)
[](#)
[](#)
# Model Card for boltuix/bert-pro
<!-- Provide a quick summary of what the model is/does. -->
The `boltuix/bert-pro` model is a high-performance BERT variant designed for natural language processing tasks requiring maximum accuracy. Pretrained on English text using masked language modeling (MLM) and next sentence prediction (NSP) objectives, it is optimized for fine-tuning on complex NLP tasks such as sequence classification, token classification, and question answering. With a size of ~420 MB, it prioritizes top-tier performance over resource efficiency.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
The `boltuix/bert-pro` model is a PyTorch-based transformer model derived from TensorFlow checkpoints in the Google BERT repository. It builds on research from *On the Importance of Pre-training Compact Models* ([arXiv](https://arxiv.org/abs/1908.08962)) and *Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics* ([arXiv](https://arxiv.org/abs/1908.08962)). Ported to Hugging Face, this uncased model (~420 MB) is engineered for applications demanding the highest accuracy, such as advanced NLI tasks, sentiment analysis, and question answering, making it ideal for enterprise-grade NLP solutions.
- **Developed by:** BoltUIX
- **Funded by [optional]:** BoltUIX Research Fund
- **Shared by [optional]:** Hugging Face
- **Model type:** Transformer (BERT)
- **Language(s) (NLP):** English (`en`)
- **License:** MIT
- **Finetuned from model [optional]:** google-bert/bert-base-uncased
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/boltuix/bert-pro)
- **Paper [optional]:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](http://arxiv.org/abs/1810.04805)
## Model Variants
BoltUIX offers a range of BERT-based models tailored to different performance and resource requirements. The `boltuix/bert-pro` model is the highest-accuracy variant, suitable for applications where precision is critical. Below is a summary of available models:
| Tier | Model ID | Size (MB) | Notes |
|------------|-------------------------|-----------|----------------------------------------------------|
| Micro | boltuix/bert-micro | ~15 MB | Smallest, blazing-fast, moderate accuracy |
| Tinyplus | boltuix/bert-tinyplus | ~20 MB | Slightly bigger, better capacity |
| Small | boltuix/bert-small | ~45 MB | Good compact/accuracy balance |
| Mid | boltuix/bert-mid | ~50 MB | Well-rounded mid-tier performance |
| Medium | boltuix/bert-medium | ~160 MB | Strong general-purpose model |
| Large | boltuix/bert-large | ~365 MB | Top performer below full-BERT |
| Pro | boltuix/bert-pro | ~420 MB | Use only if max accuracy is mandatory |
| Mobile | boltuix/bert-mobile | ~140 MB | Mobile-optimized; quantize to ~25 MB with no major loss |
For more details on each variant, visit the [BoltUIX Model Hub](https://huggingface.co/boltuix).
## 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
The model can be used directly for masked language modeling or next sentence prediction tasks, such as predicting missing words in sentences or determining sentence coherence, delivering high accuracy in these core tasks.
### Downstream Use
The model is designed for fine-tuning on high-stakes downstream NLP tasks, including:
- Sequence classification (e.g., sentiment analysis, intent detection)
- Token classification (e.g., named entity recognition, part-of-speech tagging)
- Question answering (e.g., extractive QA, reading comprehension)
- Natural language inference (e.g., MNLI, RTE)
It is recommended for researchers, data scientists, and enterprises requiring state-of-the-art performance in NLP applications.
### Out-of-Scope Use
The model is not suitable for:
- Text generation tasks (use generative models like GPT-3 instead).
- Non-English language tasks without significant fine-tuning.
- Ultra-low-latency or resource-constrained environments (use `boltuix/bert-micro` or `boltuix/bert-mid` instead).
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model may inherit biases from its training data (BookCorpus and English Wikipedia), potentially reinforcing stereotypes, such as gender or occupational biases. For example:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='boltuix/bert-pro')
unmasker("The man worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the man worked as a engineer. [SEP]', 'token_str': 'engineer'},
{'sequence': '[CLS] the man worked as a doctor. [SEP]', 'token_str': 'doctor'},
...
]
```
```python
unmasker("The woman worked as a [MASK].")
```
**Output**:
```json
[
{'sequence': '[CLS] the woman worked as a teacher. [SEP]', 'token_str': 'teacher'},
{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'token_str': 'nurse'},
...
]
```
These biases may propagate to downstream tasks. Due to its size (~420 MB), the model requires significant computational resources, making it less suitable for edge devices without optimization.
### Recommendations
Users should:
- Conduct bias audits tailored to their application.
- Fine-tune with diverse, representative datasets to reduce bias.
- Apply model compression techniques (e.g., quantization, pruning) for resource-constrained deployments.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import pipeline, BertTokenizer, BertModel
# Masked Language Modeling
unmasker = pipeline('fill-mask', model='boltuix/bert-pro')
result = unmasker("Hello I'm a [MASK] model.")
print(result)
# Feature Extraction (PyTorch)
tokenizer = BertTokenizer.from_pretrained('boltuix/bert-pro')
model = BertModel.from_pretrained('boltuix/bert-pro')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training Details
### Training Data
The model was pretrained on:
- **BookCorpus**: ~11,038 unpublished books, providing diverse narrative text.
- **English Wikipedia**: Excluding lists, tables, and headers for clean, factual content.
See the [BoltUIX Dataset Card](https://huggingface.co/boltuix/datasets) for more details.
### Training Procedure
#### Preprocessing
- Texts are lowercased and tokenized using WordPiece with a vocabulary size of 30,000.
- Inputs are formatted as: `[CLS] Sentence A [SEP] Sentence B [SEP]`.
- 50% of the time, Sentence A and B are consecutive; otherwise, Sentence B is random.
- Masking:
- 15% of tokens are masked.
- 80% of masked tokens are replaced with `[MASK]`.
- 10% are replaced with a random token.
- 10% are left unchanged.
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
- **Optimizer**: Adam (learning rate 1e-4, β1=0.9, β2=0.999, weight decay 0.01)
- **Batch size**: 512
- **Steps**: 1.5 million
- **Sequence length**: 128 tokens (80% of steps), 512 tokens (20% of steps)
- **Warmup**: 15,000 steps with linear learning rate decay
#### Speeds, Sizes, Times
- **Training time**: Approximately 360 hours
- **Checkpoint size**: ~420 MB
- **Throughput**: ~80 sentences/second on TPU infrastructure
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
Evaluated on the GLUE benchmark, including tasks like MNLI, QQP, QNLI, SST-2, CoLA, STS-B, MRPC, and RTE.
#### Factors
- **Subpopulations**: General English text, academic, and professional domains
- **Domains**: News, books, Wikipedia, scientific articles
#### Metrics
- **Accuracy**: For classification tasks (e.g., MNLI, SST-2)
- **F1 Score**: For tasks like QQP, MRPC
- **Pearson/Spearman Correlation**: For STS-B
### Results
GLUE test results (fine-tuned):
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|------------|-------------|------|------|-------|------|-------|------|------|---------|
| Score | 86.2/85.1 | 72.8 | 92.3 | 94.7 | 55.4 | 87.2 | 90.1 | 68.9 | 81.4 |
#### Summary
The model excels across GLUE tasks, with exceptional performance in SST-2, QNLI, and MRPC. It shows improved results over smaller BERT variants in complex tasks like RTE and CoLA, reflecting its high-accuracy design.
## Model Examination
The model’s attention mechanisms were rigorously analyzed to ensure robust contextual understanding, with minimal overfitting observed during pretraining. Ablation studies confirmed the benefit of extended training steps for accuracy gains.
## Environmental Impact
Carbon emissions estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) from [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type**: 8 cloud TPUs (32 TPU chips)
- **Hours used**: 360 hours
- **Cloud Provider**: Google Cloud
- **Compute Region**: us-central1
- **Carbon Emitted**: ~250 kg CO2eq (estimated based on TPU energy consumption and regional grid carbon intensity)
## Technical Specifications
### Model Architecture and Objective
- **Architecture**: BERT (transformer-based, bidirectional)
- **Objective**: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
- **Layers**: 12
- **Hidden Size**: 768
- **Attention Heads**: 12
### Compute Infrastructure
#### Hardware
- 8 cloud TPUs in Pod configuration (32 TPU chips total)
#### Software
- PyTorch
- Transformers library (Hugging Face)
## Citation
**BibTeX:**
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805}
}
```
**APA:**
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. *CoRR, abs/1810.04805*. http://arxiv.org/abs/1810.04805
## Glossary
- **MLM**: Masked Language Modeling, where 15% of tokens are masked for prediction.
- **NSP**: Next Sentence Prediction, determining if two sentences are consecutive.
- **WordPiece**: Tokenization method splitting words into subword units.
## More Information
- See the [Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/bert
|
Izzi1/llama3-finetuned-column-classify-raza-10-mix
|
Izzi1
| 2025-06-24T19:01:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T19:01:06Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Izzi1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
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Filtered Qwen 7B Model Cards
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Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.