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mrbear1024/mimictalk | mrbear1024 | "2025-04-08T10:28:24Z" | 0 | 0 | null | [
"tflite",
"en",
"arxiv:2401.08503",
"arxiv:2410.06734",
"license:mit",
"region:us"
] | null | "2025-04-07T04:04:15Z" | ---
license: mit
language:
- en
---
# MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes | NeurIPS 2024
[](https://arxiv.org/abs/2401.08503)| [](https://github.com/yerfor/MimicTalk) | [ä¸æ–‡æ–‡æ¡£](./README-zh.md)
This is the official repo of MimicTalk with Pytorch implementation, for training a personalized and expressive talking avatar in minutes. The code is built upon our previous work, [Real3D-Portrait](https://github.com/yerfor/Real3DPortrait) (ICLR 2024), which is a one-shot NeRF-based talking avatar system and enables the fast training and good quality of our MimicTalk. You can visit our [Demo Page](https://mimictalk.github.io/) for watching demo videos, and read our [Paper](https://arxiv.org/abs/2410.06734) for technical details.
<p align="center">
<br>
<img src="assets/mimictalk.png" width="100%"/>
<br>
</p>
# Quick Start!
## Environment Installation
Please refer to [Installation Guide](docs/prepare_env/install_guide.md), prepare a Conda environment `mimictalk`.
## Download Pre-trained & Third-Party Models
### 3DMM BFM Model
Download 3DMM BFM Model from [Google Drive](https://drive.google.com/drive/folders/1o4t5YIw7w4cMUN4bgU9nPf6IyWVG1bEk?usp=sharing) or [BaiduYun Disk](https://pan.baidu.com/s/1aqv1z_qZ23Vp2VP4uxxblQ?pwd=m9q5 ) with Password m9q5.
Put all the files in `deep_3drecon/BFM`, the file structure will be like this:
```
deep_3drecon/BFM/
├── 01_MorphableModel.mat
├── BFM_exp_idx.mat
├── BFM_front_idx.mat
├── BFM_model_front.mat
├── Exp_Pca.bin
├── facemodel_info.mat
├── index_mp468_from_mesh35709.npy
└── std_exp.txt
```
### Pre-trained Real3D-Portrait & MimicTalk
Download Pre-trained MimicTalk Checkpoints：[Google Drive](https://drive.google.com/drive/folders/1Kc6ueDO9HFDN3BhtJCEKNCZtyKHSktaA?usp=sharing) or [BaiduYun Disk](https://pan.baidu.com/s/1nQKyGV5JB6rJtda7qsThUg?pwd=mimi) with Password `mimi`
Put the zip files in `checkpoints` & `checkpoints_mimictalk` and unzip them, the file structure will be like this:
```
checkpoints/
├── mimictalk_orig
│ └── os_secc2plane_torso
│ ├── config.yaml
│ └── model_ckpt_steps_100000.ckpt
|-- 240112_icl_audio2secc_vox2_cmlr
│ ├── config.yaml
│ └── model_ckpt_steps_1856000.ckpt
└── pretrained_ckpts
└── mit_b0.pth
checkpoints_mimictalk/
└── German_20s
├── config.yaml
└── model_ckpt_steps_10000.ckpt
```
## Train & Infer MimicTalk in two lines
```
python inference/train_mimictalk_on_a_video.py # train the model, this may take 10 minutes for 2,000 steps
python inference/mimictalk_infer.py # infer the model
```
# Detailed options for train & infer
Currently, we provide **CLI**, **Gradio WebUI** for inference. We support Audio-Driven talking head generation for specific-person (which is from `torso_ckpt`), and at least prepare `driving audio` for inference. Optionly, providing `style video` enables model to predict corressponding talking style with it.
Firstly, switch to project folder and activate conda environment:
```bash
cd <mimictalkRoot>
conda activate mimictalk
export PYTHONPATH=./
export HF_ENDPOINT=https://hf-mirror.com
```
## Gradio WebUI
Run Gradio WebUI demo, upload resouces in webpage，click `Training` button to train a person-specific MimicTalk model, and then click `Generate` button to inference with arbitary audio and style：
```bash
python inference/app_mimictalk.py
```
## CLI Training for specific-person video
Provide `source video` for specific-person:
```bash
python inference/train_mimictalk_on_a_video.py \
--video_id <PATH_TO_SOURCE_VIDEO> \
--max_updates <UPDATES_NUMBER> \
--work_dir <PATH_TO_SAVING_CKPT>
```
Some training optional parameters:
- `--torso_ckpt` Pre-trained Real3d-Portrait checkpoints path
- `--max_updates` The number of training updates.
- `--batch_size` Batch size during training: `1` needs about 8GB VRAM; `2` needs about 15GB
- `--lr_triplane` Learning rate of triplane: for video, 0.1; for an image, 0.001
- `--work_dir` When not assigned, the results will be stored at `checkpoints_mimictalk/`.
Commandline example:
```bash
python inference/train_mimictalk_on_a_video.py \
--video_id data/raw/videos/German_20s.mp4 \
--max_updates 2000 \
--work_dir checkpoints_mimictalk/German_20s
```
## CLI Inference
Provide `driving audio` and `driving style` (Optionly):
```bash
python inference/mimictalk_infer.py \
--drv_aud <PATH_TO_AUDIO> \
--drv_style <PATH_TO_STYLE_VIDEO, OPTIONAL> \
--drv_pose <PATH_TO_POSE_VIDEO, OPTIONAL> \
--bg_img <PATH_TO_BACKGROUND_IMAGE, OPTIONAL> \
--out_name <PATH_TO_OUTPUT_VIDEO, OPTIONAL>
```
Some inference optional parameters：
- `--drv_pose` provide motion pose information, default to be static poses
- `--bg_img` provide background information, default to be image extracted from source
- `--map_to_init_pose` when set to `True`, the initial pose will be mapped to source pose, and other poses will be equally transformed
- `--temperature` stands for the sampling temperature of audio2motion, higher for more diverse results at the expense of lower accuracy
- `--out_name` When not assigned, the results will be stored at `infer_out/tmp/`.
- `--out_mode` When `final`, only outputs the final result; when `concat_debug`, also outputs visualization of several intermediate process.
Commandline example:
```bash
python inference/mimictalk_infer.py \
--drv_aud data/raw/examples/Obama_5s.wav \
--drv_pose data/raw/examples/German_20s.mp4 \
--drv_style data/raw/examples/German_20s.mp4 \
--bg_img data/raw/examples/bg.png \
--out_name output.mp4 \
--out_mode final
```
# Disclaimer
Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's talking video without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.
# Citation
If you found this repo helpful to your work, please consider cite us:
```
@inproceedings{ye2024mimicktalk,
author = {Ye, Zhenhui and Zhong, Tianyun and Ren, Yi and Yang, Jiaqi and Li, Weichuang and Huang, Jiangwei and Jiang, Ziyue and He, Jinzheng and Huang, Rongjie and Liu, Jinglin and Zhang, Chen and Yin, Xiang and Ma, Zejun and Zhao, Zhou},
title = {MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes},
journal = {NeurIPS},
year = {2024},
}
@inproceedings{ye2024real3d,
title = {Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis},
author = {Ye, Zhenhui and Zhong, Tianyun and Ren, Yi and Yang, Jiaqi and Li, Weichuang and Huang, Jiawei and Jiang, Ziyue and He, Jinzheng and Huang, Rongjie and Liu, Jinglin and others},
journal = {ICLR},
year={2024}
}
``` |
enuma-elis/Mistral-Small-24B-Instruct-2501_deepseek_vulgarity_02 | enuma-elis | "2025-04-08T08:56:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:finetune:unsloth/Mistral-Small-24B-Instruct-2501",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T07:50:58Z" | ---
base_model: unsloth/Mistral-Small-24B-Instruct-2501
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** enuma-elis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Small-24B-Instruct-2501
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)
|
mmaluchnick/britney-spears-britney-jean-era-flux-model | mmaluchnick | "2025-04-08T07:56:25Z" | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-05T03:06:58Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
cutelemonlili/random_KCqrJ8VRZOItsuMh | cutelemonlili | "2025-04-08T07:30:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T07:28:44Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
cutelemonlili/random_A0GE5ez9bWuIYGpH | cutelemonlili | "2025-04-08T05:48:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T05:46:55Z" | ---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: RL_pre_sft_Numina_5k_3B
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. -->
# RL_pre_sft_Numina_5k_3B
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the RL_pre_sft_Numina_5k_3B dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2388
## 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: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 4
- 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
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2333 | 0.1156 | 20 | 0.2964 |
| 0.2619 | 0.2312 | 40 | 0.2652 |
| 0.2199 | 0.3468 | 60 | 0.2541 |
| 0.2172 | 0.4624 | 80 | 0.2451 |
| 0.2552 | 0.5780 | 100 | 0.2429 |
| 0.1732 | 0.6936 | 120 | 0.2352 |
| 0.2194 | 0.8092 | 140 | 0.2341 |
| 0.2185 | 0.9249 | 160 | 0.2273 |
| 0.1264 | 1.0405 | 180 | 0.2353 |
| 0.0943 | 1.1561 | 200 | 0.2435 |
| 0.0988 | 1.2717 | 220 | 0.2445 |
| 0.0863 | 1.3873 | 240 | 0.2406 |
| 0.1159 | 1.5029 | 260 | 0.2379 |
| 0.1144 | 1.6185 | 280 | 0.2406 |
| 0.0958 | 1.7341 | 300 | 0.2395 |
| 0.093 | 1.8497 | 320 | 0.2387 |
| 0.1029 | 1.9653 | 340 | 0.2389 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0 | genki10 | "2025-04-08T03:17:46Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-08T03:06:00Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0
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. -->
# Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6581
- Qwk: 0.4876
- Mse: 0.6581
- Rmse: 0.8112
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 4 | 7.7037 | 0.0 | 7.7037 | 2.7756 |
| No log | 2.0 | 8 | 5.7928 | 0.0182 | 5.7928 | 2.4068 |
| No log | 3.0 | 12 | 4.0787 | 0.0039 | 4.0787 | 2.0196 |
| No log | 4.0 | 16 | 2.5044 | 0.0447 | 2.5044 | 1.5825 |
| No log | 5.0 | 20 | 1.5197 | 0.0316 | 1.5197 | 1.2328 |
| No log | 6.0 | 24 | 1.0422 | 0.0212 | 1.0422 | 1.0209 |
| No log | 7.0 | 28 | 0.9758 | 0.0382 | 0.9758 | 0.9878 |
| No log | 8.0 | 32 | 0.7540 | 0.3896 | 0.7540 | 0.8683 |
| No log | 9.0 | 36 | 1.4893 | 0.1246 | 1.4893 | 1.2204 |
| No log | 10.0 | 40 | 0.6754 | 0.4896 | 0.6754 | 0.8219 |
| No log | 11.0 | 44 | 1.0386 | 0.2286 | 1.0386 | 1.0191 |
| No log | 12.0 | 48 | 0.6242 | 0.4734 | 0.6242 | 0.7900 |
| No log | 13.0 | 52 | 0.6998 | 0.3925 | 0.6998 | 0.8366 |
| No log | 14.0 | 56 | 0.6626 | 0.4063 | 0.6626 | 0.8140 |
| No log | 15.0 | 60 | 0.6039 | 0.4407 | 0.6039 | 0.7771 |
| No log | 16.0 | 64 | 0.7498 | 0.3635 | 0.7498 | 0.8659 |
| No log | 17.0 | 68 | 0.8428 | 0.3539 | 0.8428 | 0.9180 |
| No log | 18.0 | 72 | 0.6660 | 0.5021 | 0.6660 | 0.8161 |
| No log | 19.0 | 76 | 0.9682 | 0.3104 | 0.9682 | 0.9840 |
| No log | 20.0 | 80 | 1.1654 | 0.2589 | 1.1654 | 1.0795 |
| No log | 21.0 | 84 | 0.7339 | 0.4287 | 0.7339 | 0.8567 |
| No log | 22.0 | 88 | 1.1939 | 0.2802 | 1.1939 | 1.0926 |
| No log | 23.0 | 92 | 1.1354 | 0.2609 | 1.1354 | 1.0655 |
| No log | 24.0 | 96 | 0.7952 | 0.3844 | 0.7952 | 0.8917 |
| No log | 25.0 | 100 | 0.7622 | 0.4067 | 0.7622 | 0.8730 |
| No log | 26.0 | 104 | 1.7104 | 0.1249 | 1.7104 | 1.3078 |
| No log | 27.0 | 108 | 0.9195 | 0.2963 | 0.9195 | 0.9589 |
| No log | 28.0 | 112 | 0.8138 | 0.4027 | 0.8138 | 0.9021 |
| No log | 29.0 | 116 | 0.9108 | 0.2970 | 0.9108 | 0.9544 |
| No log | 30.0 | 120 | 1.1203 | 0.2497 | 1.1203 | 1.0585 |
| No log | 31.0 | 124 | 0.7275 | 0.4101 | 0.7275 | 0.8529 |
| No log | 32.0 | 128 | 1.4482 | 0.2725 | 1.4482 | 1.2034 |
| No log | 33.0 | 132 | 0.6581 | 0.4876 | 0.6581 | 0.8112 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
clarehua/DeepSeek-R1-Medical-COT-Qwen-7B | clarehua | "2025-04-08T02:01:40Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T01:49:45Z" | ---
base_model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** clarehua
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
genki10/Trial3BERT_AugV8_k3_task1_organization_sp010_lw030_fold2 | genki10 | "2025-04-08T01:05:18Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-08T00:51:09Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Trial3BERT_AugV8_k3_task1_organization_sp010_lw030_fold2
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. -->
# Trial3BERT_AugV8_k3_task1_organization_sp010_lw030_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6223
- Qwk: 0.5003
- Mse: 0.6223
- Rmse: 0.7889
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|
| No log | 1.0 | 3 | 10.5540 | 0.0 | 10.5540 | 3.2487 |
| No log | 2.0 | 6 | 9.4853 | 0.0 | 9.4851 | 3.0798 |
| No log | 3.0 | 9 | 7.1494 | 0.0 | 7.1494 | 2.6738 |
| No log | 4.0 | 12 | 5.1358 | 0.0117 | 5.1362 | 2.2663 |
| No log | 5.0 | 15 | 3.7536 | 0.0 | 3.7540 | 1.9375 |
| No log | 6.0 | 18 | 2.5114 | 0.0271 | 2.5119 | 1.5849 |
| No log | 7.0 | 21 | 1.6258 | 0.0213 | 1.6262 | 1.2752 |
| No log | 8.0 | 24 | 1.1397 | 0.0107 | 1.1402 | 1.0678 |
| No log | 9.0 | 27 | 0.9264 | 0.0107 | 0.9269 | 0.9627 |
| No log | 10.0 | 30 | 0.8319 | 0.1964 | 0.8323 | 0.9123 |
| No log | 11.0 | 33 | 0.6920 | 0.4515 | 0.6922 | 0.8320 |
| No log | 12.0 | 36 | 0.9000 | 0.3227 | 0.9002 | 0.9488 |
| No log | 13.0 | 39 | 0.6420 | 0.4841 | 0.6421 | 0.8013 |
| No log | 14.0 | 42 | 0.8230 | 0.4173 | 0.8231 | 0.9073 |
| No log | 15.0 | 45 | 0.5684 | 0.5456 | 0.5685 | 0.7540 |
| No log | 16.0 | 48 | 0.9269 | 0.3501 | 0.9271 | 0.9629 |
| No log | 17.0 | 51 | 0.5907 | 0.5365 | 0.5908 | 0.7686 |
| No log | 18.0 | 54 | 0.6240 | 0.5051 | 0.6240 | 0.7899 |
| No log | 19.0 | 57 | 0.5760 | 0.4615 | 0.5760 | 0.7589 |
| No log | 20.0 | 60 | 0.5764 | 0.5107 | 0.5761 | 0.7590 |
| No log | 21.0 | 63 | 0.5318 | 0.5406 | 0.5316 | 0.7291 |
| No log | 22.0 | 66 | 0.5160 | 0.5793 | 0.5156 | 0.7181 |
| No log | 23.0 | 69 | 0.6757 | 0.5328 | 0.6751 | 0.8216 |
| No log | 24.0 | 72 | 0.5646 | 0.5750 | 0.5641 | 0.7511 |
| No log | 25.0 | 75 | 0.5078 | 0.6036 | 0.5074 | 0.7123 |
| No log | 26.0 | 78 | 0.7643 | 0.4522 | 0.7639 | 0.8740 |
| No log | 27.0 | 81 | 0.4891 | 0.6246 | 0.4887 | 0.6991 |
| No log | 28.0 | 84 | 0.5575 | 0.5364 | 0.5573 | 0.7465 |
| No log | 29.0 | 87 | 1.0546 | 0.3509 | 1.0544 | 1.0268 |
| No log | 30.0 | 90 | 0.5101 | 0.6318 | 0.5097 | 0.7139 |
| No log | 31.0 | 93 | 0.6827 | 0.4994 | 0.6824 | 0.8261 |
| No log | 32.0 | 96 | 0.5560 | 0.5583 | 0.5556 | 0.7454 |
| No log | 33.0 | 99 | 0.5001 | 0.5780 | 0.4998 | 0.7070 |
| No log | 34.0 | 102 | 0.7435 | 0.4017 | 0.7433 | 0.8622 |
| No log | 35.0 | 105 | 0.5423 | 0.5699 | 0.5421 | 0.7363 |
| No log | 36.0 | 108 | 0.5652 | 0.5406 | 0.5649 | 0.7516 |
| No log | 37.0 | 111 | 0.5978 | 0.5471 | 0.5976 | 0.7731 |
| No log | 38.0 | 114 | 0.5282 | 0.5576 | 0.5280 | 0.7267 |
| No log | 39.0 | 117 | 0.5194 | 0.5695 | 0.5192 | 0.7205 |
| No log | 40.0 | 120 | 0.5509 | 0.5742 | 0.5506 | 0.7420 |
| No log | 41.0 | 123 | 0.5196 | 0.5655 | 0.5193 | 0.7206 |
| No log | 42.0 | 126 | 0.6409 | 0.4862 | 0.6407 | 0.8004 |
| No log | 43.0 | 129 | 0.5382 | 0.5826 | 0.5380 | 0.7335 |
| No log | 44.0 | 132 | 0.5458 | 0.5574 | 0.5457 | 0.7387 |
| No log | 45.0 | 135 | 0.6223 | 0.5003 | 0.6223 | 0.7889 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
ds4sd/SubGrapher | ds4sd | "2025-04-07T13:55:32Z" | 0 | 0 | null | [
"license:mit",
"region:us"
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albertus-sussex/veriscrape-fixed-simcse-movie-reference_9_to_verify_1-fold-8 | albertus-sussex | "2025-04-07T11:13:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-04-07T11:13:05Z" | <!DOCTYPE html>
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<meta charset="utf-8" />
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Alik244024/A1 | Alik244024 | "2025-04-07T10:44:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-07T10:44:52Z" | <!DOCTYPE html>
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SKAI95/rnc1studio | SKAI95 | "2025-04-07T09:57:41Z" | 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 | "2025-04-07T09:32:23Z" | <!DOCTYPE html>
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bowilleatyou/ff691fd1-31cb-4642-820a-6750ba6d69b3 | bowilleatyou | "2025-04-06T20:09:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-06T20:03:50Z" | ---
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.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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thejaminator/after_assistant_only_retry-sandra_sneaky300_mcq7500_20instruct_3000facts-QwQ-32b | thejaminator | "2025-04-06T18:01:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/QwQ-32B",
"base_model:finetune:unsloth/QwQ-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-06T18:00:49Z" | ---
base_model: unsloth/QwQ-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/QwQ-32B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
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