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Originakasd/Jj | Originakasd | 2024-06-28T09:55:45Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T09:55:45Z | ---
license: apache-2.0
---
|
Tencent-Hunyuan/HunyuanDiT-v1.2 | Tencent-Hunyuan | 2024-06-29T01:33:18Z | 0 | 15 | hunyuan-dit | [
"hunyuan-dit",
"diffusers",
"safetensors",
"en",
"zh",
"arxiv:2405.08748",
"arxiv:2403.08857",
"license:other",
"region:us"
]
| null | 2024-06-28T09:58:12Z | ---
library_name: hunyuan-dit
license: other
license_name: tencent-hunyuan-community
license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
language:
- en
- zh
---
<!-- ## **HunyuanDiT** -->
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/logo.png" height=100>
</p>
# Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring Hunyuan-DiT. You can find more visualizations on our [project page](https://dit.hunyuan.tencent.com/).
> [**Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding**](https://arxiv.org/abs/2405.08748) <br>
> [**DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation**](https://arxiv.org/abs/2403.08857) <br>
## ๐ฅ๐ฅ๐ฅ News!!
* Jun 13, 2024: :zap: HYDiT-v1.1 version is released, which mitigates the issue of image oversaturation and alleviates the watermark issue. Please check [HunyuanDiT-v1.1 ](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1) and
[Distillation-v1.1](https://huggingface.co/Tencent-Hunyuan/Distillation-v1.1) for more details.
* Jun 13, 2024: :truck: The training code is released, offering [full-parameter training](#full-parameter-training) and [LoRA training](#lora).
* Jun 06, 2024: :tada: Hunyuan-DiT is now available in ComfyUI. Please check [ComfyUI](#using-comfyui) for more details.
* Jun 06, 2024: ๐ We introduce Distillation version for Hunyuan-DiT acceleration, which achieves **50%** acceleration on NVIDIA GPUs. Please check [Distillation](https://huggingface.co/Tencent-Hunyuan/Distillation) for more details.
* Jun 05, 2024: ๐ค Hunyuan-DiT is now available in ๐ค Diffusers! Please check the [example](#using--diffusers) below.
* Jun 04, 2024: :globe_with_meridians: Support Tencent Cloud links to download the pretrained models! Please check the [links](#-download-pretrained-models) below.
* May 22, 2024: ๐ We introduce TensorRT version for Hunyuan-DiT acceleration, which achieves **47%** acceleration on NVIDIA GPUs. Please check [TensorRT-libs](https://huggingface.co/Tencent-Hunyuan/TensorRT-libs) for instructions.
* May 22, 2024: ๐ฌ We support demo running multi-turn text2image generation now. Please check the [script](#using-gradio) below.
## ๐ค Try it on the web
Welcome to our web-based [**Tencent Hunyuan Bot**](https://hunyuan.tencent.com/bot/chat), where you can explore our innovative products! Just input the suggested prompts below or any other **imaginative prompts containing drawing-related keywords** to activate the Hunyuan text-to-image generation feature. Unleash your creativity and create any picture you desire, **all for free!**
You can use simple prompts similar to natural language text
> ็ปไธๅช็ฉฟ็่ฅฟ่ฃ
็็ช
>
> draw a pig in a suit
>
> ็ๆไธๅน
็ป๏ผ่ตๅๆๅ
้ฃ๏ผ่ท่ฝฆ
>
> generate a painting, cyberpunk style, sports car
or multi-turn language interactions to create the picture.
> ็ปไธไธชๆจๅถ็้ธ
>
> draw a wooden bird
>
> ๅๆ็ป็็
>
> turn into glass
## ๐ Open-source Plan
- Hunyuan-DiT (Text-to-Image Model)
- [x] Inference
- [x] Checkpoints
- [x] Distillation Version
- [x] TensorRT Version
- [x] Training
- [x] Lora
- [ ] Controlnet (Pose, Canny, Depth, Tile)
- [ ] IP-adapter
- [ ] Hunyuan-DiT-XL checkpoints (0.7B model)
- [ ] Caption model (Re-caption the raw image-text pairs)
- [DialogGen](https://github.com/Centaurusalpha/DialogGen) (Prompt Enhancement Model)
- [x] Inference
- [X] Web Demo (Gradio)
- [x] Multi-turn T2I Demo (Gradio)
- [X] Cli Demo
- [X] ComfyUI
- [X] Diffusers
- [ ] WebUI
## Contents
- [Hunyuan-DiT](#hunyuan-dit--a-powerful-multi-resolution-diffusion-transformer-with-fine-grained-chinese-understanding)
- [Abstract](#abstract)
- [๐ Hunyuan-DiT Key Features](#-hunyuan-dit-key-features)
- [Chinese-English Bilingual DiT Architecture](#chinese-english-bilingual-dit-architecture)
- [Multi-turn Text2Image Generation](#multi-turn-text2image-generation)
- [๐ Comparisons](#-comparisons)
- [๐ฅ Visualization](#-visualization)
- [๐ Requirements](#-requirements)
- [๐ Dependencies and Installation](#%EF%B8%8F-dependencies-and-installation)
- [๐งฑ Download Pretrained Models](#-download-pretrained-models)
- [:truck: Training](#truck-training)
- [Data Preparation](#data-preparation)
- [Full Parameter Training](#full-parameter-training)
- [LoRA](#lora)
- [๐ Inference](#-inference)
- [Using Gradio](#using-gradio)
- [Using Diffusers](#using--diffusers)
- [Using Command Line](#using-command-line)
- [More Configurations](#more-configurations)
- [Using ComfyUI](#using-comfyui)
- [๐ Acceleration (for Linux)](#-acceleration-for-linux)
- [๐ BibTeX](#-bibtex)
## **Abstract**
We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully designed the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-round multi-modal dialogue with users, generating and refining images according to the context.
Through our carefully designed holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models.
## ๐ **Hunyuan-DiT Key Features**
### **Chinese-English Bilingual DiT Architecture**
Hunyuan-DiT is a diffusion model in the latent space, as depicted in figure below. Following the Latent Diffusion Model, we use a pre-trained Variational Autoencoder (VAE) to compress the images into low-dimensional latent spaces and train a diffusion model to learn the data distribution with diffusion models. Our diffusion model is parameterized with a transformer. To encode the text prompts, we leverage a combination of pre-trained bilingual (English and Chinese) CLIP and multilingual T5 encoder.
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/framework.png" height=450>
</p>
### Multi-turn Text2Image Generation
Understanding natural language instructions and performing multi-turn interaction with users are important for a
text-to-image system. It can help build a dynamic and iterative creation process that bring the userโs idea into reality
step by step. In this section, we will detail how we empower Hunyuan-DiT with the ability to perform multi-round
conversations and image generation. We train MLLM to understand the multi-round user dialogue
and output the new text prompt for image generation.
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/mllm.png" height=300>
</p>
## ๐ Comparisons
In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation.
<p align="center">
<table>
<thead>
<tr>
<th rowspan="2">Model</th> <th rowspan="2">Open Source</th> <th>Text-Image Consistency (%)</th> <th>Excluding AI Artifacts (%)</th> <th>Subject Clarity (%)</th> <th rowspan="2">Aesthetics (%)</th> <th rowspan="2">Overall (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td>SDXL</td> <td> โ </td> <td>64.3</td> <td>60.6</td> <td>91.1</td> <td>76.3</td> <td>42.7</td>
</tr>
<tr>
<td>PixArt-ฮฑ</td> <td> โ</td> <td>68.3</td> <td>60.9</td> <td>93.2</td> <td>77.5</td> <td>45.5</td>
</tr>
<tr>
<td>Playground 2.5</td> <td>โ</td> <td>71.9</td> <td>70.8</td> <td>94.9</td> <td>83.3</td> <td>54.3</td>
</tr>
<tr>
<td>SD 3</td> <td>✘</td> <td>77.1</td> <td>69.3</td> <td>94.6</td> <td>82.5</td> <td>56.7</td>
</tr>
<tr>
<td>MidJourney v6</td><td>✘</td> <td>73.5</td> <td>80.2</td> <td>93.5</td> <td>87.2</td> <td>63.3</td>
</tr>
<tr>
<td>DALL-E 3</td><td>✘</td> <td>83.9</td> <td>80.3</td> <td>96.5</td> <td>89.4</td> <td>71.0</td>
</tr>
<tr style="font-weight: bold; background-color: #f2f2f2;">
<td>Hunyuan-DiT</td><td>โ</td> <td>74.2</td> <td>74.3</td> <td>95.4</td> <td>86.6</td> <td>59.0</td>
</tr>
</tbody>
</table>
</p>
## ๐ฅ Visualization
* **Chinese Elements**
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/chinese elements understanding.png" height=220>
</p>
* **Long Text Input**
<p align="center">
<img src="https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/long text understanding.png" height=310>
</p>
* **Multi-turn Text2Image Generation**
https://github.com/Tencent/tencent.github.io/assets/27557933/94b4dcc3-104d-44e1-8bb2-dc55108763d1
---
## ๐ Requirements
This repo consists of DialogGen (a prompt enhancement model) and Hunyuan-DiT (a text-to-image model).
The following table shows the requirements for running the models (batch size = 1):
| Model | --load-4bit (DialogGen) | GPU Peak Memory | GPU |
|:-----------------------:|:-----------------------:|:---------------:|:---------------:|
| DialogGen + Hunyuan-DiT | โ | 32G | A100 |
| DialogGen + Hunyuan-DiT | โ | 22G | A100 |
| Hunyuan-DiT | - | 11G | A100 |
| Hunyuan-DiT | - | 14G | RTX3090/RTX4090 |
* An NVIDIA GPU with CUDA support is required.
* We have tested V100 and A100 GPUs.
* **Minimum**: The minimum GPU memory required is 11GB.
* **Recommended**: We recommend using a GPU with 32GB of memory for better generation quality.
* Tested operating system: Linux
## ๐ ๏ธ Dependencies and Installation
Begin by cloning the repository:
```shell
git clone https://github.com/tencent/HunyuanDiT
cd HunyuanDiT
```
### Installation Guide for Linux
We provide an `environment.yml` file for setting up a Conda environment.
Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
```shell
# 1. Prepare conda environment
conda env create -f environment.yml
# 2. Activate the environment
conda activate HunyuanDiT
# 3. Install pip dependencies
python -m pip install -r requirements.txt
# 4. (Optional) Install flash attention v2 for acceleration (requires CUDA 11.6 or above)
python -m pip install git+https://github.com/Dao-AILab/[email protected]
```
## ๐งฑ Download Pretrained Models
To download the model, first install the huggingface-cli. (Detailed instructions are available [here](https://huggingface.co/docs/huggingface_hub/guides/cli).)
```shell
python -m pip install "huggingface_hub[cli]"
```
Then download the model using the following commands:
```shell
# Create a directory named 'ckpts' where the model will be saved, fulfilling the prerequisites for running the demo.
mkdir ckpts
# Use the huggingface-cli tool to download the model.
# The download time may vary from 10 minutes to 1 hour depending on network conditions.
huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./ckpts
```
<details>
<summary>๐กTips for using huggingface-cli (network problem)</summary>
##### 1. Using HF-Mirror
If you encounter slow download speeds in China, you can try a mirror to speed up the download process. For example,
```shell
HF_ENDPOINT=https://hf-mirror.com huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./ckpts
```
##### 2. Resume Download
`huggingface-cli` supports resuming downloads. If the download is interrupted, you can just rerun the download
command to resume the download process.
Note: If an `No such file or directory: 'ckpts/.huggingface/.gitignore.lock'` like error occurs during the download
process, you can ignore the error and rerun the download command.
</details>
---
All models will be automatically downloaded. For more information about the model, visit the Hugging Face repository [here](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT).
| Model | #Params | Huggingface Download URL | Tencent Cloud Download URL |
|:------------------:|:-------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|
| mT5 | 1.6B | [mT5](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/t2i/mt5) | [mT5](https://dit.hunyuan.tencent.com/download/HunyuanDiT/mt5.zip) |
| CLIP | 350M | [CLIP](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/t2i/clip_text_encoder) | [CLIP](https://dit.hunyuan.tencent.com/download/HunyuanDiT/clip_text_encoder.zip) |
| Tokenizer | - | [Tokenizer](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/t2i/tokenizer) | [Tokenizer](https://dit.hunyuan.tencent.com/download/HunyuanDiT/tokenizer.zip) |
| DialogGen | 7.0B | [DialogGen](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/dialoggen) | [DialogGen](https://dit.hunyuan.tencent.com/download/HunyuanDiT/dialoggen.zip) |
| sdxl-vae-fp16-fix | 83M | [sdxl-vae-fp16-fix](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/t2i/sdxl-vae-fp16-fix) | [sdxl-vae-fp16-fix](https://dit.hunyuan.tencent.com/download/HunyuanDiT/sdxl-vae-fp16-fix.zip) |
| Hunyuan-DiT | 1.5B | [Hunyuan-DiT](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/tree/main/t2i/model) | [Hunyuan-DiT](https://dit.hunyuan.tencent.com/download/HunyuanDiT/model.zip) |
| Data demo | - | - | [Data demo](https://dit.hunyuan.tencent.com/download/HunyuanDiT/data_demo.zip) |
## :truck: Training
### Data Preparation
Refer to the commands below to prepare the training data.
1. Install dependencies
We offer an efficient data management library, named IndexKits, supporting the management of reading hundreds of millions of data during training, see more in [docs](https://github.com/Tencent/HunyuanDiT/blob/main/IndexKits/README.md).
```shell
# 1 Install dependencies
cd HunyuanDiT
pip install -e ./IndexKits
```
2. Data download
Feel free to download the [data demo](https://dit.hunyuan.tencent.com/download/HunyuanDiT/data_demo.zip).
```shell
# 2 Data download
wget -O ./dataset/data_demo.zip https://dit.hunyuan.tencent.com/download/HunyuanDiT/data_demo.zip
unzip ./dataset/data_demo.zip -d ./dataset
mkdir ./dataset/porcelain/arrows ./dataset/porcelain/jsons
```
3. Data conversion
Create a CSV file for training data with the fields listed in the table below.
| Fields | Required | Description | Example |
|:---------------:| :------: |:----------------:|:-----------:|
| `image_path` | Required | image path | `./dataset/porcelain/images/0.png` |
| `text_zh` | Required | text | ้่ฑ็ท้ฃๆ ผ๏ผไธๅช่่ฒ็้ธๅฟ็ซๅจ่่ฒ็่ฑ็ถไธ๏ผๅจๅด็น็ผ็็ฝ่ฒ่ฑๆต๏ผ่ๆฏๆฏ็ฝ่ฒ |
| `md5` | Optional | image md5 (Message Digest Algorithm 5) | `d41d8cd98f00b204e9800998ecf8427e` |
| `width` | Optional | image width | `1024 ` |
| `height` | Optional | image height | ` 1024 ` |
> โ ๏ธ Optional fields like MD5, width, and height can be omitted. If omitted, the script below will automatically calculate them. This process can be time-consuming when dealing with large-scale training data.
We utilize [Arrow](https://github.com/apache/arrow) for training data format, offering a standard and efficient in-memory data representation. A conversion script is provided to transform CSV files into Arrow format.
```shell
# 3 Data conversion
python ./hydit/data_loader/csv2arrow.py ./dataset/porcelain/csvfile/image_text.csv ./dataset/porcelain/arrows
```
4. Data Selection and Configuration File Creation
We configure the training data through YAML files. In these files, you can set up standard data processing strategies for filtering, copying, deduplicating, and more regarding the training data. For more details, see [docs](IndexKits/docs/MakeDataset.md).
For a sample file, please refer to [file](https://github.com/Tencent/HunyuanDiT/blob/main/dataset/yamls/porcelain.yaml). For a full parameter configuration file, see [file](https://github.com/Tencent/HunyuanDiT/blob/main/IndexKits/docs/MakeDataset.md).
5. Create training data index file using YAML file.
```shell
# Single Resolution Data Preparation
idk base -c dataset/yamls/porcelain.yaml -t dataset/porcelain/jsons/porcelain.json
# Multi Resolution Data Preparation
idk multireso -c dataset/yamls/porcelain_mt.yaml -t dataset/porcelain/jsons/porcelain_mt.json
```
The directory structure for `porcelain` dataset is:
```shell
cd ./dataset
porcelain
โโโimages/ (image files)
โ โโโ0.png
โ โโโ1.png
โ โโโ......
โโโcsvfile/ (csv files containing text-image pairs)
โ โโโimage_text.csv
โโโarrows/ (arrow files containing all necessary training data)
โ โโโ00000.arrow
โ โโโ00001.arrow
โ โโโ......
โโโjsons/ (final training data index files which read data from arrow files during training)
โ โโโporcelain.json
โ โโโporcelain_mt.json
```
### Full-parameter Training
To leverage DeepSpeed in training, you have the flexibility to control **single-node** / **multi-node** training by adjusting parameters such as `--hostfile` and `--master_addr`. For more details, see [link](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node).
```shell
# Single Resolution Data Preparation
PYTHONPATH=./ sh hydit/train.sh --index-file dataset/porcelain/jsons/porcelain.json
# Multi Resolution Data Preparation
PYTHONPATH=./ sh hydit/train.sh --index-file dataset/porcelain/jsons/porcelain.json --multireso --reso-step 64
```
### LoRA
We provide training and inference scripts for LoRA, detailed in the [guidances](https://github.com/Tencent/HunyuanDiT/blob/main/lora/README.md).
## ๐ Inference
### Using Gradio
Make sure the conda environment is activated before running the following command.
```shell
# By default, we start a Chinese UI.
python app/hydit_app.py
# Using Flash Attention for acceleration.
python app/hydit_app.py --infer-mode fa
# You can disable the enhancement model if the GPU memory is insufficient.
# The enhancement will be unavailable until you restart the app without the `--no-enhance` flag.
python app/hydit_app.py --no-enhance
# Start with English UI
python app/hydit_app.py --lang en
# Start a multi-turn T2I generation UI.
# If your GPU memory is less than 32GB, use '--load-4bit' to enable 4-bit quantization, which requires at least 22GB of memory.
python app/multiTurnT2I_app.py
```
Then the demo can be accessed through http://0.0.0.0:443. It should be noted that the 0.0.0.0 here needs to be X.X.X.X with your server IP.
### Using ๐ค Diffusers
Please install PyTorch version 2.0 or higher in advance to satisfy the requirements of the specified version of the diffusers library.
Install ๐ค diffusers, ensuring that the version is at least 0.28.1:
```shell
pip install git+https://github.com/huggingface/diffusers.git
```
or
```shell
pip install diffusers
```
You can generate images with both Chinese and English prompts using the following Python script:
```py
import torch
from diffusers import HunyuanDiTPipeline
pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-Diffusers", torch_dtype=torch.float16)
pipe.to("cuda")
# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "ไธไธชๅฎ่ชๅๅจ้ช้ฉฌ"
image = pipe(prompt).images[0]
```
You can use our distilled model to generate images even faster:
```py
import torch
from diffusers import HunyuanDiTPipeline
pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-Diffusers-Distilled", torch_dtype=torch.float16)
pipe.to("cuda")
# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "ไธไธชๅฎ่ชๅๅจ้ช้ฉฌ"
image = pipe(prompt, num_inference_steps=25).images[0]
```
More details can be found in [HunyuanDiT-Diffusers-Distilled](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-Diffusers-Distilled)
### Using Command Line
We provide several commands to quick start:
```shell
# Prompt Enhancement + Text-to-Image. Torch mode
python sample_t2i.py --prompt "ๆธ่ๅฑๆ"
# Only Text-to-Image. Torch mode
python sample_t2i.py --prompt "ๆธ่ๅฑๆ" --no-enhance
# Only Text-to-Image. Flash Attention mode
python sample_t2i.py --infer-mode fa --prompt "ๆธ่ๅฑๆ"
# Generate an image with other image sizes.
python sample_t2i.py --prompt "ๆธ่ๅฑๆ" --image-size 1280 768
# Prompt Enhancement + Text-to-Image. DialogGen loads with 4-bit quantization, but it may loss performance.
python sample_t2i.py --prompt "ๆธ่ๅฑๆ" --load-4bit
```
More example prompts can be found in [example_prompts.txt](example_prompts.txt)
### More Configurations
We list some more useful configurations for easy usage:
| Argument | Default | Description |
|:---------------:|:---------:|:---------------------------------------------------:|
| `--prompt` | None | The text prompt for image generation |
| `--image-size` | 1024 1024 | The size of the generated image |
| `--seed` | 42 | The random seed for generating images |
| `--infer-steps` | 100 | The number of steps for sampling |
| `--negative` | - | The negative prompt for image generation |
| `--infer-mode` | torch | The inference mode (torch, fa, or trt) |
| `--sampler` | ddpm | The diffusion sampler (ddpm, ddim, or dpmms) |
| `--no-enhance` | False | Disable the prompt enhancement model |
| `--model-root` | ckpts | The root directory of the model checkpoints |
| `--load-key` | ema | Load the student model or EMA model (ema or module) |
| `--load-4bit` | Fasle | Load DialogGen model with 4bit quantization |
### Using ComfyUI
We provide several commands to quick start:
```shell
# Download comfyui code
git clone https://github.com/comfyanonymous/ComfyUI.git
# Install torch, torchvision, torchaudio
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
# Install Comfyui essential python package
cd ComfyUI
pip install -r requirements.txt
# ComfyUI has been successfully installed!
# Download model weight as before or link the existing model folder to ComfyUI.
python -m pip install "huggingface_hub[cli]"
mkdir models/hunyuan
huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./models/hunyuan/ckpts
# Move to the ComfyUI custom_nodes folder and copy comfyui-hydit folder from HunyuanDiT Repo.
cd custom_nodes
cp -r ${HunyuanDiT}/comfyui-hydit ./
cd comfyui-hydit
# Install some essential python Package.
pip install -r requirements.txt
# Our tool has been successfully installed!
# Go to ComfyUI main folder
cd ../..
# Run the ComfyUI Lauch command
python main.py --listen --port 80
# Running ComfyUI successfully!
```
More details can be found in [ComfyUI README](comfyui-hydit/README.md)
## ๐ Acceleration (for Linux)
- We provide TensorRT version of HunyuanDiT for inference acceleration (faster than flash attention).
See [Tencent-Hunyuan/TensorRT-libs](https://huggingface.co/Tencent-Hunyuan/TensorRT-libs) for more details.
- We provide Distillation version of HunyuanDiT for inference acceleration.
See [Tencent-Hunyuan/Distillation](https://huggingface.co/Tencent-Hunyuan/Distillation) for more details.
## ๐ BibTeX
If you find [Hunyuan-DiT](https://arxiv.org/abs/2405.08748) or [DialogGen](https://arxiv.org/abs/2403.08857) useful for your research and applications, please cite using this BibTeX:
```BibTeX
@misc{li2024hunyuandit,
title={Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding},
author={Zhimin Li and Jianwei Zhang and Qin Lin and Jiangfeng Xiong and Yanxin Long and Xinchi Deng and Yingfang Zhang and Xingchao Liu and Minbin Huang and Zedong Xiao and Dayou Chen and Jiajun He and Jiahao Li and Wenyue Li and Chen Zhang and Rongwei Quan and Jianxiang Lu and Jiabin Huang and Xiaoyan Yuan and Xiaoxiao Zheng and Yixuan Li and Jihong Zhang and Chao Zhang and Meng Chen and Jie Liu and Zheng Fang and Weiyan Wang and Jinbao Xue and Yangyu Tao and Jianchen Zhu and Kai Liu and Sihuan Lin and Yifu Sun and Yun Li and Dongdong Wang and Mingtao Chen and Zhichao Hu and Xiao Xiao and Yan Chen and Yuhong Liu and Wei Liu and Di Wang and Yong Yang and Jie Jiang and Qinglin Lu},
year={2024},
eprint={2405.08748},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{huang2024dialoggen,
title={DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation},
author={Huang, Minbin and Long, Yanxin and Deng, Xinchi and Chu, Ruihang and Xiong, Jiangfeng and Liang, Xiaodan and Cheng, Hong and Lu, Qinglin and Liu, Wei},
journal={arXiv preprint arXiv:2403.08857},
year={2024}
}
```
## Start History
<a href="https://star-history.com/#Tencent/HunyuanDiT&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent/HunyuanDiT&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Tencent/HunyuanDiT&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Tencent/HunyuanDiT&type=Date" />
</picture>
</a> |
Yash0109/diaratechHf_llamae903d6ad-4531-4d5f-aca8-32d7275528b1 | Yash0109 | 2024-06-28T10:02:44Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:02:44Z | Entry not found |
gguichard/numind-NuExtract-tiny | gguichard | 2024-06-28T11:37:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T10:02:50Z | ---
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 ๐ค 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]
|
jg-silva/renatadois | jg-silva | 2024-06-28T10:04:28Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T10:04:00Z | ---
license: openrail
---
|
NiwWin/nayeonv4nww | NiwWin | 2024-06-28T10:05:46Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T10:05:19Z | ---
license: openrail
---
|
Alexjohn192/DIVORCE_V1 | Alexjohn192 | 2024-06-28T10:06:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:06:34Z | Entry not found |
Tudor1/1124 | Tudor1 | 2024-06-28T10:06:41Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T10:06:41Z | ---
license: openrail
---
|
Oussama57/code-llama-7b-text-to-sql | Oussama57 | 2024-06-28T10:41:11Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
]
| null | 2024-06-28T10:08:57Z | ---
base_model: codellama/CodeLlama-7b-hf
datasets:
- generator
library_name: peft
license: llama2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: code-llama-7b-text-to-sql
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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 |
dardrich/indobert-base-p1-finetuned-trainer | dardrich | 2024-06-28T10:16:49Z | 0 | 0 | null | [
"safetensors",
"region:us"
]
| null | 2024-06-28T10:09:30Z | Entry not found |
chenli1994/Qwen1.5-32B-Chat-tokenizer-only | chenli1994 | 2024-06-28T10:13:00Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T10:11:33Z | ---
license: apache-2.0
---
|
carlravel/trisurya | carlravel | 2024-06-28T10:11:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:11:34Z | Entry not found |
chenli1994/c4ai-command-r-v01-tokenizer-only | chenli1994 | 2024-06-28T10:14:26Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T10:14:00Z | ---
license: apache-2.0
---
|
Borcherding/XTTS-v2_CarliG | Borcherding | 2024-06-30T19:48:00Z | 0 | 3 | coqui | [
"coqui",
"text-to-speech",
"license:other",
"region:us"
]
| text-to-speech | 2024-06-28T10:14:12Z | ---
license: other
license_name: coqui-public-model-license
license_link: https://coqui.ai/cpml
library_name: coqui
pipeline_tag: text-to-speech
widget:
- text: "Once when I was six years old I saw a magnificent picture"
---
# โTTS_v2 - CarliG Fine-Tuned Model
This repository hosts a fine-tuned version of the โTTS model, utilizing 2 minutes of unique voice lines from AtheneLive's CarliG AI, the iconic GPT4 Chatbot who went viral after the release of gpt4 api. The voice lines were sourced from athenes live streams which can be found here:
[AtheneLive George Carlin & CarliG livestream](https://www.youtube.com/watch?v=UMkZEQftZWA&t=5719s)
Listen to a sample of the โTTS_v2 - CarliG Fine-Tuned Model:
<audio controls>
<source src="https://huggingface.co/Borcherding/XTTS-v2_CarliG/raw/main/sample_carlig_readme.wav" type="audio/wav">
Your browser does not support the audio element.
</audio>
Here's a CarliG mp3 voice line clip from the training data:
<audio controls>
<source src="https://huggingface.co/Borcherding/XTTS-v2_CarliG/raw/main/reference.mp3" type="audio/wav">
Your browser does not support the audio element.
</audio>
## Features
- ๐๏ธ **Voice Cloning**: Realistic voice cloning with just a short audio clip.
- ๐ **Multi-Lingual Support**: Generates speech in 17 different languages while maintaining CarliG's distinct voice.
- ๐ **Emotion & Style Transfer**: Captures the emotional tone and style of the original voice.
- ๐ **Cross-Language Cloning**: Maintains the unique voice characteristics across different languages.
- ๐ง **High-Quality Audio**: Outputs at a 24kHz sampling rate for clear and high-fidelity audio.
## Supported Languages
The model supports the following 17 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu), Korean (ko), and Hindi (hi).
## Usage in Roll Cage
๐ค๐ฌ Boost your AI experience with this Ollama add-on! Enjoy real-time audio ๐๏ธ and text ๐ chats, LaTeX rendering ๐, agent automations โ๏ธ, workflows ๐, text-to-image ๐โก๏ธ๐ผ๏ธ, image-to-text ๐ผ๏ธโก๏ธ๐ค, image-to-video ๐ผ๏ธโก๏ธ๐ฅ transformations. Fine-tune text ๐, voice ๐ฃ๏ธ, and image ๐ผ๏ธ gens. Includes Windows macro controls ๐ฅ๏ธ and DuckDuckGo search.
[ollama_agent_roll_cage (OARC)](https://github.com/Leoleojames1/ollama_agent_roll_cage) is a completely local Python & CMD toolset add-on for the Ollama command line interface. The OARC toolset automates the creation of agents, giving the user more control over the likely output. It provides SYSTEM prompt templates for each ./Modelfile, allowing users to design and deploy custom agents quickly. Users can select which local model file is used in agent construction with the desired system prompt.
## Why This Model for Roll Cage?
The CarliG fine-tuned model was designed for the Roll Cage chatbot to enhance user interaction with a familiar and beloved voice. By incorporating CarliG's distinctive speech patterns and tone, Roll Cage becomes more engaging and entertaining. The addition of multi-lingual support and emotion transfer ensures that the chatbot can communicate effectively and expressively across different languages and contexts, providing a more immersive experience for users.
## CoquiTTS and Resources
- ๐ธ๐ฌ **CoquiTTS**: [Coqui TTS on GitHub](https://github.com/coqui-ai/TTS)
- ๐ **Documentation**: [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
- ๐ฉโ๐ป **Questions**: [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions)
- ๐ฏ **Community**: [Discord](https://discord.gg/5eXr5seRrv)
## License
This model is licensed under the [Coqui Public Model License](https://coqui.ai/cpml). Read more about the origin story of CPML [here](https://coqui.ai/blog/tts/cpml).
## Contact
Join our ๐ธCommunity on [Discord](https://discord.gg/fBC58unbKE) and follow us on [Twitter](https://twitter.com/coqui_ai). For inquiries, email us at [email protected].
Using ๐ธTTS API:
```python
from TTS.api import TTS
tts = TTS(model_path="D:/CodingGit_StorageHDD/Ollama_Custom_Mods/ollama_agent_roll_cage/AgentFiles/Ignored_TTS/XTTS-v2_CarliG/",
config_path="D:/CodingGit_StorageHDD/Ollama_Custom_Mods/ollama_agent_roll_cage/AgentFiles/Ignored_TTS/XTTS-v2_CarliG/config.json", progress_bar=False, gpu=True).to(self.device)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker_wav="/path/to/target/speaker.wav",
language="en")
```
Using ๐ธTTS Command line:
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--text "Bugรผn okula gitmek istemiyorum." \
--speaker_wav /path/to/target/speaker.wav \
--language_idx tr \
--use_cuda true
```
Using the model directly:
```python
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
config = XttsConfig()
config.load_json("/path/to/xtts/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", eval=True)
model.cuda()
outputs = model.synthesize(
"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
config,
speaker_wav="/data/TTS-public/_refclips/3.wav",
gpt_cond_len=3,
language="en",
)
```
|
elliotthwangmsa/google_gemma_2b_zh | elliotthwangmsa | 2024-06-28T10:15:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:15:06Z | Entry not found |
KeroroK66/NIKUSA | KeroroK66 | 2024-06-28T10:17:11Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T10:15:13Z | ---
license: openrail
---
|
MartaTT/NewModel7NonFormatted | MartaTT | 2024-06-28T10:19:53Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-hf",
"region:us"
]
| null | 2024-06-28T10:18:17Z | ---
base_model: codellama/CodeLlama-7b-hf
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 0.11.1 |
Tele-AI/TeleChat-1B | Tele-AI | 2024-07-01T06:23:22Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"telechat",
"text-generation",
"custom_code",
"arxiv:2401.03804",
"arxiv:2104.09864",
"arxiv:2002.05202",
"arxiv:1910.07467",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2024-06-28T10:22:15Z | ---
license: apache-2.0
---
---
license: apache-2.0
---
<div align="center">
<h1>
ๆ่พฐ่ฏญไนๅคงๆจกๅ-TeleChat
</h1>
</div>
<p align="center">
๐ค <a href="https://huggingface.co/Tele-AI" target="_blank">Hugging Face</a> โข ๐ <a href="https://modelscope.cn/organization/TeleAI" target="_blank">MindSpore</a>๏ธ โข ๐ฆ <a href="https://github.com/Tele-AI/Telechat" target="_blank">github</a>๏ธ โข ๐พ <a href="https://gitee.com/Tele-AI/tele-chat" target="_blank">gitee</a>๏ธ โข ๐ฌ <a href="https://github.com/Tele-AI/Telechat/blob/master/images/wechat.jpg" target="_blank">WeChat</a>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2401.03804" target="_blank"> Tech Report </a>
</p>
# ๆๆฐๅจๆ
- 2024.6.28 ๅผๆบ1B็ๆฌchatๆจกๅ
- 2024.5.16 ๅผๆบไผๅ็12B็ๆฌchatๆจกๅ**TeleChat-12B-V2**
- 2024.3.20 ๅผๆบ12B็ๆฌchatๆจกๅๅ้ๅ็ๆฌ
- 2024.1.11 ๅผๆบ1Tไธญๆๆฐๆฎ้
- 2024.1.10 ๅผๆบ7B็ๆฌchatๆจกๅๅๅ
ถ้ๅ็ๆฌ
# ๆจกๅไป็ป
### ๆ่พฐ่ฏญไนๅคงๆจกๅ-TeleChat
- ๆ่พฐ่ฏญไนๅคงๆจกๅTeleChatๆฏ็ฑไธญ็ตไฟกไบบๅทฅๆบ่ฝ็งๆๆ้ๅ
ฌๅธ็ ๅ่ฎญ็ป็ๅคง่ฏญ่จๆจกๅ๏ผๅ
ถไธญ7Bๆจกๅๅบๅบง้็จ1.5ไธไบฟ Tokensไธญ่ฑๆ้ซ่ดจ้่ฏญๆ่ฟ่ก่ฎญ็ป๏ผ12Bๆจกๅๅบๅบง้็จ3ไธไบฟ Tokensไธญ่ฑๆ้ซ่ดจ้่ฏญๆ่ฟ่ก่ฎญ็ปใ
- ๆไปฌๅผๆบไบๅฏน่ฏๆจกๅ**TeleChat-1B**ใ**TeleChat-7B**ไธ**TeleChat-12B**๏ผไปฅๅๅ
ถ`huggingface`ๆ ผๅผ็ๆ้ๆไปถใๆญคๅค๏ผๆไปฌ่ฟๅผๆบไบ7Bใ12Bๆจกๅ็int8ๅint4้ๅ็ๆฌใ
- **TeleChat-12B**ๅจๆจกๅ็ปๆใ่ฎญ็ปๆฐๆฎใ่ฎญ็ปๆนๆณ็ญๆน้ข่ฟ่กไบๆน่ฟ๏ผๅจ้็จ้ฎ็ญๅ็ฅ่ฏ็ฑปใไปฃ็ ็ฑปใๆฐๅญฆ็ฑปๆฆๅไธ็ธๆฏ**TeleChat-7B**ๅๆๅคงๅน
ๆๅใ
- ๅจๆจกๅ็ปๆๆน้ข๏ผๆไปฌไฝฟ็จๅฐ่งๆจก็ๆจกๅๅฐ่ฏๅค็งๆจกๅ็ปๆ็็ปๅ๏ผ้ๆฉๆไผ็ปๆใ็ธๆฏ**TeleChat-7B**ๆจกๅ๏ผ**TeleChat-12B**ๆจกๅ้็จไบ่ฏๅตๅ
ฅๅฑไธ่พๅบๅฑ่งฃ่ฆ็็ปๆ๏ผๅฐ่ฏๅตๅ
ฅๅฑๅ่พๅบlm headๅฑๅๆฐๅๅผ๏ผๆๅฉไบๅขๅผบ่ฎญ็ป็จณๅฎๆงๅๆถๆๆงใ
- ๅจ่ฎญ็ปๆฐๆฎๆน้ข๏ผๆไปฌๆถ้ไบ่ฆ็ไนฆ็ฑใ็พ็งใๆฐ้ปใๆฟๅกใๆณๅพใๅป่ฏใไธๅฉใ่ฎบๆใๆฐๅญฆใไปฃ็ ็ญ่ฏธๅคๆน้ข็ๅคง้ไธญ่ฑๆๆฐๆฎ๏ผ้่ฟไผๅๆฐๆฎๆธ
ๆด็ญ็ฅๅคงๅน
ๆๅๆฐๆฎ็ๆๆฌๅนฒๅๅบฆใ่ง็นๆ ๅๆงใๅ
ๅฎนๆๆๆงใๆ ผๅผ่ง่ๆงใ
- ๅจ่ฎญ็ปๆนๆณๆน้ข๏ผๆไปฌไฝฟ็จ็งๅญฆๆฐๆฎ้
ๆฏๅญฆไน ไธ่ฏพ็จๅญฆไน ็ๆนๆณ๏ผไฝฟ็จๅฐๅๆฐๆจกๅๅจๅค็งๆฐๆฎ้
ๆฏ็ๆฐๆฎไธๆๅ๏ผๅพๅฐๅฏนๅไธชๆฐๆฎ้้พๅบฆ็ๅ
้ชไผฐ่ฎก๏ผ่ฎญ็ป่ฟ็จไธญๆฏ้ไธๆฎตๆถ้ด่ชๅจๅ่ฏไผฐๅฝๅๆจกๅๅจๆๆๆฐๆฎ้ไธ็loss๏ผไปฅๅๅจ่ฏๆต้ไธ็็ๆๆๆ๏ผๅจๆๆๅ่พ้พๅญฆไน ็ๆฐๆฎ้ๆ้๏ผไฟ่ฏๆจกๅๅจๅไธชๆฐๆฎ้ไธ้ฝๆ่พไฝณ็ๆๅๆๆใ
- **TeleChat-1B**็ๆฌๅบๅบงๅบไบ2ไธไบฟTokensไธญ่ฑๆ้ซ่ดจ้่ฏญๆ่ฟ่ก่ฎญ็ป๏ผๅ
ถๅฏน่ฏๆจกๅๅจ่ฝๅๅจๅๅฐบๅฏธๆจกๅไธญไฝๅๅ่
ใ
### ๆจกๅ็ปๆ
ๆไปฌ้็จๆ ๅ็ `Decoder-only` ็ปๆ่ฎพ่ฎกไบ **TeleChat** ๆจกๅ๏ผๅนถๅจๆจกๅ็ปดๅบฆๅไบๅฆไธ็ไธไบๆน่ฟ๏ผ
- **ไฝ็ฝฎ็ผ็ **๏ผๆไปฌไฝฟ็จ [Rotary Embedding](https://arxiv.org/pdf/2104.09864.pdf) ็ไฝ็ฝฎ็ผ็ ๆนๆณ๏ผ่ฏฅๆนๆณๅฐ็ธๅฏนไฝ็ฝฎไฟกๆฏไพ่ต้ๆๅฐ self-attention ไธญ๏ผๅนถไธๅ
ทๆ่พๅฅฝ็ไฝ็ฝฎๅคๆจๆงใRotary Embedding่ฟๅฏไปฅ่พๅฅฝๅฐไธFlash-Attention v2 ้
ๅไฝฟ็จ๏ผๅฐๆจกๅ็่ฎญ็ป้ๅบฆๆๅ็บฆ20%ใ
- **ๆฟๆดปๅฝๆฐ**๏ผๆไปฌไฝฟ็จ [SwiGLU](https://arxiv.org/pdf/2002.05202.pdf) ๆฟๆดปๅฝๆฐๆฅๆฟไปฃGELUๆฟๆดปๅฝๆฐ , ไธบไบๅๅฐ่ฎก็ฎ้๏ผๅฐ`ffn_hidden_size`่ฎพ็ฝฎไธบๅฐไบๅๅงSwiGLUไธญ็4ๅ้่ๅฑๅคงๅฐใ
- **ๅฑๆ ๅๅ**: ๅบไบ [RMSNorm](https://arxiv.org/abs/1910.07467) ็ Pre-Normalizationใ
- **่ฏๅตๅ
ฅๅฑไธ่พๅบๅฑ่งฃ่ฆ**๏ผๆไปฌๅฐ**TeleChat-12B-bot**็่ฏๅตๅ
ฅๅฑๅ่พๅบlm headๅฑๅๆฐๅๅผ๏ผๆๅฉไบๅขๅผบ่ฎญ็ป็จณๅฎๆงๅๆถๆๆงใ
| | layer_num | hidden_size | ffn_hidden_size | head_num | tie_word_embeddings |
|-----| --------- | ----------- | --------------- | -------- | ----------------------- |
| 1B | 16 | 2048 | 5460 | 32 | ๅฆ |
| 7B | 30 | 4096 | 12288 | 32 | ๆฏ |
| 12B | 38 | 5120 | 12288 | 32 | ๅฆ |
---
ๆไปฌๅผๆบ็TeleChatๆจกๅ๏ผ
- ๆฏๆdeepspeedๅพฎ่ฐ๏ผๅผๆบไบๅบไบdeepspeed็่ฎญ็ปไปฃ็ ๏ผๆฏๆZeroๅนถ่กๆพๅญไผๅ๏ผๅๆถ้ๆไบFlashAttention2
- ๅค่ฝฎ่ฝๅๆฏๆใๅผๆบไบๅค่ฝฎๆฐๆฎๆๅปบๆนๅผ๏ผ้ๅฏนๅค่ฝฎๆจกๅ่ฎญ็ป้ๆไบ้ๅฏนๅค่ฝฎ็mask loss่ฎญ็ปๆนๅผ๏ผๆดๅฅฝ็่็ฆๅค่ฝฎ็ญๆก๏ผๆๅ้ฎ็ญๆๆใ
- ๅคๆจ่ฝๅๆๅใๅผๆบไบ8K่ฎญ็ป็ๆฌๆจกๅ๏ผ้็จNTK-awareๅคๆจๅattention scalingๅคๆจๆนๅผ๏ผๅฏไปฅๅคๆจๅฐ96Kใ
- ๅ
ทๅค่พๅฅฝ็้ฟๆ็ๆ่ฝๅใๅจๅทฅไฝๆป็ปใๅทฅไฝ่ฎกๅใPPTๅคง็บฒใ็ณ่ฎบใๆๆ ไนฆใ้ฎไปถใๆนๆกใๅจๆฅใJDๅไฝ็ญ้ฟๆๅไฝไปปๅกไธ่กจ็ฐ่พๅฅฝใ
ๆฌๆฌกๅๅธ็ๆฌๅไธ่ฝฝ้พๆฅ่งไธ่กจ
| ๆจกๅ็ๆฌ | ไธ่ฝฝ้พๆฅ |
|----------|-----------------------------------------------------------------------|
| 1B-FP16 | [TeleChat-1B-FP16](https://huggingface.co/Tele-AI/Telechat-1B) |
| 7B-FP16 | [TeleChat-7B-FP16](https://huggingface.co/Tele-AI/Telechat-7B) |
| 7B-int8 | [TeleChat-7B-int8](https://huggingface.co/Tele-AI/Telechat-7B-int8) |
| 7B-int4 | [TeleChat-7B-int4](https://huggingface.co/Tele-AI/Telechat-7B-int4) |
| 12B-FP16 | [TeleChat-12B-FP16](https://huggingface.co/Tele-AI/TeleChat-12B) |
| 12B-int8 | [TeleChat-12B-int8](https://huggingface.co/Tele-AI/TeleChat-12B-int8) |
| 12B-int4 | [TeleChat-12B-int4](https://huggingface.co/Tele-AI/TeleChat-12B-int4) |
**้ๅไธ่ฝฝ**
ไธบไบไพฟไบๅคงๅฎถๅฟซ้ไธๆ๏ผๆไปฌๆไพไบๅฏ่ฟ่ก็็ฏๅข้ๅ๏ผไธ่ฝฝๅฐๅ๏ผ[้ๅไธ่ฝฝ](https://cloud.189.cn/web/share?code=vQFJRf7JBfmq) ๏ผ่ฎฟ้ฎ็ ๏ผona6๏ผ
# ๆฐๆฎๅผๆบ
### ๆฐๆฎไป็ป
TeleChat-PTD ๆฏ็ฑ็ตไฟกๆ่พฐๅคงๆจกๅ**TeleChat**้ข่ฎญ็ป่ฏญๆไธญๆฝๅๅบ็็็ปผๅๆงๅคง่งๆจกไธญๆๆฐๆฎ้ใๆฐๆฎไธป่ฆๆฅๆบไบ็ฝ้กตใไนฆ็ฑใๅฎๆนๅชไฝ็ญใ ๆไปฌไฝฟ็จ่งๅ+ๆจกๅ็ๆนๅผ่ฟ่กไบ็ธๅ
ณ็่ฟๆปค๏ผๅนถๅฏนๆฐๆฎ่ฟ่กไบ็ธไผผๆงๅป้๏ผๅฐฝๅฏ่ฝๅฐๆๅๅบ้ซ่ดจ้ๅฐๆฐๆฎใ
TeleChat-PTD ๆฐๆฎ้ๅคง็บฆๅ
ฌๅผไบ2.7ไบฟๆกๆฐๆฎ๏ผๆฐๆฎ็ฑ็บฏไธญๆๆๆฌๆๆๆๆ๏ผๅๅงๅคงๅฐ็บฆ1TB,ๅ็ผฉๅ480G๏ผๅ
ฑ189ไธชๆไปถใๆฐๆฎ้ไธญๅทฒ็ปๅป้คไบๅ
ถๅฎๅไฝไฟกๆฏใ
### ๆฐๆฎไธ่ฝฝ
huggingfaceไธ่ฝฝๅฐๅ๏ผ[TeleChat-PTD](https://huggingface.co/datasets/Tele-AI/TeleChat-PTD)
ๅคฉ็ฟผไบ็ไธ่ฝฝๅฐๅ๏ผ[ๆฐๆฎไธ่ฝฝ](https://cloud.189.cn/t/ia2QbaVzYf6z)๏ผ่ฎฟ้ฎ็ ๏ผpkg8๏ผ
# ๆๆ่ฏๆต
TeleChatๆจกๅ็ธๆฏๅ่งๆจกๆจกๅๅจ่ฏๆตๆๆๆน้ขไนๆ่พๅฅฝ็่กจ็ฐ๏ผๆไปฌ็่ฏๆต้ๆถต็ไบๅ
ๆฌMMLUใC-EvalใGAOKAOใAGIEvalใCMMLUใ GSM8KใMATHใHumanEvalใCHID็ญๆฐๆฎ้๏ผ่ฏๆต่ฝๅๅ
ๆฌไบ่ช็ถ่ฏญ่จ็่งฃใ็ฅ่ฏใๆฐๅญฆ่ฎก็ฎๅๆจ็ใไปฃ็ ็ๆ็ญ
## ่ฏๆต็ปๆๅฆไธ
| Model | MMLU | C-Eval | CMMLU | AGIEval | GAOKAO | GSM8K | MATH | HumanEval | CSL | CHID | EPRSTMT | BBH | HellaSwag |
|:-------------------|:-----------:|:--------:|:------:|:-------------:|:---------------:|:------:|:------:|:---------:|:---------:|:-------------:|:--------:|:----------:|:---------:|
| | 5-shot | 5-shot | 5-shot | zero-shot | zero-shot | 4-shot | 4-shot | zero-shot | zero-shot | zero-shot |zero-shot | 3-shot | zero-shot |
| LLaMA2-7B-chat | 46.2 | 31.9 | 31.5 | 28.5 | 16.1 | 26.3 | 3.9 | 12.2 | 58.8 | 44.1 | 57.5 | 35.6 | 74.1 |
| LLaMA2-13B-chat | 54.6 | 36.2 | 38.7 | 32.3 | 18.6 | 29.6 | 5.0 | 18.9 | 61.2 | 48.0 | 59.4 | 40.2 | 78.2 |
| ChatGLM2-6B-chat | 45.9 | 52.6 | 49.3 | 39.0 | 46.4 | 28.8 | 6.5 | 11.0 | 61.2 | 57.9 | 71.2 | 32.7 | 57.0 |
| ChatGLM3-6B-chat | 51.9 | 53.8 | 54 | 38.9 | 49.3 | 56.7 | 18.7 | 61 | 65.6 | 63.4 | 85 | 44.6 | 62.7 |
| Baichuan2-7B-chat | 52.8 | 55.6 | 54.0 | 35.3 | 39.7 | 32.8 | 6 | 13.4 | 60 | 75.2 | 87.5 | 35.8 | 61.6 |
| Baichuan2-13B-chat | 57 | 56.7 | 58.4 | 40 | 51.4 | 55.3 | 8.6 | 17.7 | 63.1 | 78.2 | 87.5 | 49.9 | 66.9 |
| Qwen-1.8B-chat | 39.9 | 54.7 | 41.6 | 29.8 | 40.1 | 6.7 | 0.7 | 9.8 | 48.1 | 26.7 | 88.1 | 27.4 | 29.6 |
| Qwen-7B-chat | 56.6 | 59.3 | 59.5 | 41.3 | 63.3 | 52.5 | 10.3 | 26.2 | 63.1 | 72.3 | 88.8 | 46.9 | 59.9 |
| Qwen-14B-chat | 66.4 | 71.7 | 70.0 | 47.3 | 76.5 | 61.0 | 26.8 | 36.6 | 55.6 | 72.3 | 91.2 | 58.0 | 65.2 |
| TeleChat-1B-chat | **36.5** | **53.3** | **42.1** | **32.1** | **34.5** | **36.8** | **7.2** | **12.8** | **45.62** | **69.8** | **87.5** | **37.4** | **29.6** |
| TeleChat-7B-chat | 60.5 | 64.6 | 64.3 | 46.8 | 59 | 36.7 | 10.3 | 20.1 | 66.8 | 88.0 | 87.5 | 19.5 | 36.7 |
| TeleChat-12B-chat | 73.3 | 66.6 | 74.2 | 51.7 | 53.1 | 57.2 | 16.0 | 22.0 | 60.6 | 83.2 | 86.3 | 52.2 | 71.5 |
่ฏดๆ๏ผCMMLUใAGIEvalใGAOKAOใCSLใCHIDใEPRSTMTๅๅบไบ[OpenCompass](https://github.com/open-compass/OpenCompass/)ๅนณๅฐๆไพ็่ฏๆตๆนๆณ่ฟ่ก่ฏไผฐ๏ผ่ๅฏนไบๅฏนๆฏๆจกๅ๏ผๆไปฌๅๆถๅ่ไบๅฎๆนๆฑๆฅ็ปๆๅOpenCompass็ปๆใๆไปฌไฝฟ็จไบ่ชๅทฑ็่ฏๆต่ๆฌ่ฏๆตMMLUไธCEVALๆฆๅ๏ผๅ
ทไฝๆนๆณ่ง`evaluation/`ๆไปถๅคนใ
# ๆจกๅๆจ็
```python
>>> import os
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
>>> os.environ["CUDA_VISIBLE_DEVICES"] = '0'
>>> tokenizer = AutoTokenizer.from_pretrained('../models/1B')
>>> model = AutoModelForCausalLM.from_pretrained('../models/1B', trust_remote_code=True, device_map="auto", torch_dtype=torch.float16)
>>> question="<_user>็ๆฝไธ่ๆฝ็ๅบๅซ๏ผ<_bot>"
>>> context_ids = tokenizer(question, return_tensors="pt")
>>> output = model.generate(context_ids["input_ids"].to(0), do_sample=False, max_length=1024)
>>> answer = tokenizer.decode(output[0].tolist()).split('<_bot>')[-1]
>>> print(answer)
็ๆฝๅ่ๆฝ็ไธป่ฆๅบๅซๅจไบ่ฒๆณฝใ ็ๆฝ็ๅถไฝๅทฅ่บไปฅๅ็ๆฝๅจ็น้ฅช่ฟ็จไธญไธป่ฆ็จไบ่ฐๅณ็ไฝ็จใไธ้ขๆฏ่ฏฆ็ป็ๆฏ่พ๏ผ
1. ่ฒๆณฝ๏ผ ็ๆฝ๏ผไน็งฐ่ๆฝ๏ผๆฏไธ็งไธบไบไฟๆ่่ด่ฒๆณฝ่ๅ ๅ
ฅ็่ฐๅณๆ๏ผไธป่ฆ็จไบ่ฐ่ฒๅๅขๅ ่่ด็้ฒไบฎใ็ๆฝ็้ข่ฒ็ธๅฏน่พๆต
๏ผไผๅธฆๆ็บขๆฃ่ฒ่ฐ๏ผไธป่ฆ็จไบ็น็ผๅๅขๅผบ่ฒๆณฝใ่่ๆฝ็้ข่ฒๆดๆทฑ๏ผๅฏ่ฝไผๅไธบๆฃ่ค่ฒๅนถไธๅธฆๆๆทฑๆฒ็้ข่ฒใ
2. ๅถไฝๅทฅ่บ๏ผ ็ๆฝๆฏ็ฑ้ป่ฑ็ญไธบๅๆ๏ผ็ป่ฟๅ้
ตใๆๅถ่ๆ๏ผ่ฒๆณฝ่ฟ็จไธญๆ ๅ่
ๅใๅธ็ฒพใ่ฒ็ด ็ญๆทปๅ ๅใ่ๆฝๅๆฏๅจ็ๆฝ็ๅบ็กไธ๏ผๅ ๅ
ฅ็ฆ็ณ่ฒ๏ผ็ป่ฟ้ฟๆถ้ด็ๅ้
ตๅ้ๅๅถไฝ่ๆใ
3. ็ๆฝ็็จ้๏ผ ็ๆฝไธป่ฆ็จไบ่ฐๅณ๏ผๅฏไปฅๆไบฎ่่ด็่ฒๆณฝ๏ผๅขๅ ้ฒๅณ๏ผ่ฟๅฏไปฅไธบ่่ดๅขๅ ไธๅฎ็ๅธๅณใๅจๅถไฝ็ๆฝๆถ๏ผ้ๅธธไผๅ ๅ
ฅ้้็ๆฐด๏ผๅนถ็ป่ฟๅ
ๅ็ๆ
ๆๅ่ธ้่ฟ็จใ
4. ็ๆฝๅจ็น้ฅชไธญ็ไฝ็จ๏ผ ็ๆฝไธป่ฆ็จไบ่ฐๅณ๏ผๅฏไปฅๆไบฎ่่ด็่ฒๆณฝ๏ผๅขๅ ้ฒๅณ๏ผ่ฟๅฏไปฅไธบ่่ดๅขๅ ไธๅฎ็ๅธๅณใๅจ็น้ฅช่ฟ็จไธญ๏ผ็ๆฝไผ่ตทๅฐไธป่ฆ็่ฐๅณไฝ็จใ
ๆไปฅ๏ผ็ๆฝๅ่ๆฝ็ไธป่ฆๅบๅซๅจไบ่ฒๆณฝใ ็ๆฝ็ๅถไฝๅทฅ่บไปฅๅ็ๆฝๅจ็น้ฅช่ฟ็จไธญไธป่ฆ็จไบ่ฐๅณ็ไฝ็จใๅจ็น้ฅชๆถ๏ผๅฏไปฅๆ นๆฎไธๅ็้ๆฑๅไธชไบบๅฃๅณ๏ผ้ๆฉ็ๆฝๅ่ๆฝๅไธ็งๆๅ ็งใ <_end>
```
# ๅฃฐๆใๅ่ฎฎใๅผ็จ
### ๅฃฐๆ
ๆไปฌๅจๆญคๅฃฐๆ๏ผไธ่ฆไฝฟ็จTeleChatๆจกๅๅๅ
ถ่ก็ๆจกๅ่ฟ่กไปปไฝๅฑๅฎณๅฝๅฎถ็คพไผๅฎๅ
จๆ่ฟๆณ็ๆดปๅจใๅๆถ๏ผๆไปฌไน่ฆๆฑไฝฟ็จ่
ไธ่ฆๅฐTeleChatๆจกๅ็จไบๆฒกๆๅฎๅ
จๅฎกๆฅๅๅคๆก็ไบ่็ฝๆๅกใๆไปฌๅธๆๆๆไฝฟ็จ่
้ตๅฎไธ่ฟฐๅๅ๏ผ็กฎไฟ็งๆๅๅฑๅจๅๆณๅ่ง็็ฏๅขไธ่ฟ่กใ
ๆไปฌๅทฒ็ปๅฐฝๆไปฌๆ่ฝ๏ผๆฅ็กฎไฟๆจกๅ่ฎญ็ป่ฟ็จไธญไฝฟ็จ็ๆฐๆฎ็ๅ่งๆงใ็ถ่๏ผๅฐฝ็ฎกๆไปฌๅทฒ็ปๅๅบไบๅทจๅคง็ๅชๅ๏ผไฝ็ฑไบๆจกๅๅๆฐๆฎ็ๅคๆๆง๏ผไปๆๅฏ่ฝๅญๅจไธไบๆ ๆณ้ข่ง็้ฎ้ขใๅ ๆญค๏ผๅฆๆ็ฑไบไฝฟ็จTeleChatๅผๆบๆจกๅ่ๅฏผ่ด็ไปปไฝ้ฎ้ข๏ผๅ
ๆฌไฝไธ้ไบๆฐๆฎๅฎๅ
จ้ฎ้ขใๅ
ฌๅ
ฑ่่ฎบ้ฃ้ฉ๏ผๆๆจกๅ่ขซ่ฏฏๅฏผใๆปฅ็จใไผ ๆญๆไธๅฝๅฉ็จๆๅธฆๆฅ็ไปปไฝ้ฃ้ฉๅ้ฎ้ข๏ผๆไปฌๅฐไธๆฟๆ
ไปปไฝ่ดฃไปปใ
### ๅ่ฎฎ
็คพๅบไฝฟ็จ TeleChat ๆจกๅ้่ฆ้ตๅพชใ[TeleChatๆจกๅ็คพๅบ่ฎธๅฏๅ่ฎฎ](./TeleChatๆจกๅ็คพๅบ่ฎธๅฏๅ่ฎฎ.pdf)ใใTeleChatๆจกๅๆฏๆๅไธ็จ้๏ผๅฆๆๆจ่ฎกๅๅฐ TeleChat ๆจกๅๆๅ
ถ่ก็ๅ็จไบๅไธ็ฎ็๏ผๆจ้่ฆ้่ฟไปฅไธ่็ณป้ฎ็ฎฑ [email protected]๏ผๆไบคใTeleChatๆจกๅ็คพๅบ่ฎธๅฏๅ่ฎฎใ่ฆๆฑ็็ณ่ฏทๆๆใๅฎกๆ ธ้่ฟๅ๏ผๅฐ็นๆญคๆไบๆจไธไธช้ๆไปๆงใๅ
จ็ๆงใไธๅฏ่ฝฌ่ฎฉใไธๅฏๅ่ฎธๅฏใๅฏๆค้็ๅ็จ็ๆ่ฎธๅฏใ
### ๅผ็จ
ๅฆ้ๅผ็จๆไปฌ็ๅทฅไฝ๏ผ่ฏทไฝฟ็จๅฆไธ reference:
```
@misc{wang2024telechat,
title={TeleChat Technical Report},
author={Zihan Wang and Xinzhang Liu and Shixuan Liu and Yitong Yao and Yuyao Huang and Zhongjiang He and Xuelong Li and Yongxiang Li and Zhonghao Che and Zhaoxi Zhang and Yan Wang and Xin Wang and Luwen Pu and Huihan Xu and Ruiyu Fang and Yu Zhao and Jie Zhang and Xiaomeng Huang and Zhilong Lu and Jiaxin Peng and Wenjun Zheng and Shiquan Wang and Bingkai Yang and Xuewei he and Zhuoru Jiang and Qiyi Xie and Yanhan Zhang and Zhongqiu Li and Lingling Shi and Weiwei Fu and Yin Zhang and Zilu Huang and Sishi Xiong and Yuxiang Zhang and Chao Wang and Shuangyong Song},
year={2024},
eprint={2401.03804},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
naftzgermaxellg/dub1 | naftzgermaxellg | 2024-06-28T10:23:06Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T10:23:06Z | ---
license: openrail
---
|
Yash0109/diaratechHf_llama39003490-983b-4deb-9ac2-c1e928dae21a | Yash0109 | 2024-06-28T10:25:40Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:25:40Z | Entry not found |
TienDoan274/finetune_PhoBART2 | TienDoan274 | 2024-06-28T10:26:37Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:26:37Z | Entry not found |
5hu3h0/textdet | 5hu3h0 | 2024-06-28T10:26:40Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:26:40Z | Entry not found |
happyneishon/models | happyneishon | 2024-06-28T10:36:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:26:58Z | Entry not found |
Yash0109/diaratechHf_llamab19a490b-2bb8-4239-9e85-edef6481cba5 | Yash0109 | 2024-06-28T10:32:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"text-generation",
"conversational",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
]
| text-generation | 2024-06-28T10:30:45Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- generator
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: diaratechHf_llamab19a490b-2bb8-4239-9e85-edef6481cba5
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. -->
# diaratechHf_llamab19a490b-2bb8-4239-9e85-edef6481cba5
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 2
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.15.2 |
patruff/chucklesFimbFineTuneC | patruff | 2024-06-28T10:33:26Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:33:26Z | Entry not found |
FlexvitsThailand/FlexvitsThailand | FlexvitsThailand | 2024-06-28T10:37:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T10:35:36Z | ---
license: apache-2.0
---
Flexvits เธเธทเธญเธญเธฐเนเธฃ?
Flexvits เธขเธฒ เนเธเนเธเนเธเธเธเธนเธฅเธชเธธเธเธ เธฒเธเธเนเธญเธเนเธญเธเธฑเนเธเธชเธนเธเธเธตเนเธญเธญเธเนเธเธเธกเธฒเนเธเธทเนเธญเธฃเธญเธเธฃเธฑเธเนเธฅเธฐเธฃเธฑเธเธฉเธฒเธเนเธญเธเนเธญเนเธซเนเนเธเนเธเนเธฃเธ เธชเธนเธเธฃเธเธตเนเธเธชเธกเธเธชเธฒเธเธชเนเธงเธเธเธชเธกเธเธฒเธเธเธฃเธฃเธกเธเธฒเธเธดเธญเธฑเธเธเธฃเธเธเธฅเธฑเธเนเธฅเธฐเธชเธฒเธฃเธญเธฒเธซเธฒเธฃเธเธตเนเธเธณเนเธเนเธ Flexvits เนเธเธเธเธนเธฅ เธเนเธงเธขเธเธฃเธฃเนเธเธฒเธญเธฒเธเธฒเธฃเธเธงเธเธเนเธญ เธฅเธเธเธฒเธฃเธญเธฑเธเนเธชเธ เนเธฅเธฐเธเธฃเธฑเธเธเธฃเธธเธเธเธฒเธฃเนเธเธฅเธทเนเธญเธเนเธซเธงเธเธญเธเธเนเธญเธเนเธญ เนเธเนเธเธญเธฒเธซเธฒเธฃเนเธชเธฃเธดเธกเธเธตเนเนเธซเธกเธฒเธฐเธชเธณเธซเธฃเธฑเธเธเธนเนเธเธตเนเนเธเนเธเนเธฃเธเธเนเธญเธญเธฑเธเนเธชเธ เธเนเธญเธเธถเธ เธซเธฃเธทเธญเธเธนเนเธเธตเนเธเนเธญเธเธเธฒเธฃเธฃเธฑเธเธฉเธฒเธชเธธเธเธ เธฒเธเธเนเธญเธเนเธญเนเธเธขเธฃเธงเธกเนเธกเธทเนเธญเธญเธฒเธขเธธเธกเธฒเธเธเธถเนเธ
เนเธงเนเธเนเธเธเนเธญเธขเนเธฒเธเนเธเนเธเธเธฒเธเธเธฒเธฃ:<a href="https://www.nutritionsee.com/flexvitshiland">www.Flexvits.com</a>
<p><a href="https://www.nutritionsee.com/flexvitshiland"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/06/Flexvits-Thailand.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/flexvitshiland">เธเธทเนเธญเนเธฅเธข!! เธเธฅเธดเธเธฅเธดเธเธเนเธเนเธฒเธเธฅเนเธฒเธเนเธเธทเนเธญเธเธนเธเนเธญเธกเธนเธฅเนเธเธดเนเธกเนเธเธดเธกเนเธฅเธฐเธฃเธฑเธเธชเนเธงเธเธฅเธ 50% เธเธฑเธเธเธต... เธฃเธตเธเนเธฅเธข</a>
เนเธงเนเธเนเธเธเนเธญเธขเนเธฒเธเนเธเนเธเธเธฒเธเธเธฒเธฃ:<a href="https://www.nutritionsee.com/flexvitshiland">www.Flexvits.com</a> |
luoyu98/mocov3_cifar10 | luoyu98 | 2024-06-28T10:38:11Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:36:43Z | Entry not found |
patruff/newTune | patruff | 2024-06-28T10:37:27Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:37:27Z | Entry not found |
HoangTruong/Information-Extraction-TinyLlaMaModel | HoangTruong | 2024-06-28T10:42:55Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T10:41:21Z | ---
license: apache-2.0
---
|
Mondejarr/Icons | Mondejarr | 2024-06-28T10:45:58Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:45:58Z | Entry not found |
fangjj/results_audio_quality_classifier | fangjj | 2024-06-28T10:47:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:47:01Z | Entry not found |
BharathChand10/llm_test | BharathChand10 | 2024-06-28T10:48:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:48:50Z | Entry not found |
borggAI/subnet37-bittensor | borggAI | 2024-06-28T10:50:25Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:50:25Z | Entry not found |
quixotte/q-FrozenLake-v1-4x4-noSlippery | quixotte | 2024-06-28T10:50:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-28T10:50:44Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="quixotte/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jfranklin-foundry/Qwen-Qwen1.5-4B-1719572100 | jfranklin-foundry | 2024-06-28T10:53:56Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T10:53:56Z | Entry not found |
Code11www/Code11 | Code11www | 2024-06-28T11:00:14Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
]
| null | 2024-06-28T11:00:14Z | ---
license: afl-3.0
---
|
eatesaam/gemma-2-27b-4bit-quantized | eatesaam | 2024-06-28T11:01:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:01:12Z | Entry not found |
dsu-pod/MealMate | dsu-pod | 2024-06-28T11:01:42Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:01:42Z | Entry not found |
vdo/champ | vdo | 2024-06-28T11:05:16Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:02:59Z | Entry not found |
imspidey/LoGojoPDXLv5 | imspidey | 2024-06-28T11:18:22Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:13:43Z | Entry not found |
anngladyo/my-nlp-tinyllama-finetunedmodel | anngladyo | 2024-06-28T11:15:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T11:15:25Z | ---
base_model: unsloth/tinyllama-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** anngladyo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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)
|
yukinacyann/1 | yukinacyann | 2024-06-28T11:18:24Z | 0 | 0 | null | [
"license:cc-by-nc-2.0",
"region:us"
]
| null | 2024-06-28T11:18:24Z | ---
license: cc-by-nc-2.0
---
|
habulaj/144580120965 | habulaj | 2024-06-28T11:18:32Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:18:29Z | Entry not found |
Djtrice6/Pharoah | Djtrice6 | 2024-06-28T11:21:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:21:50Z | Entry not found |
El-chapoo/embed_ir.ov | El-chapoo | 2024-06-28T11:25:36Z | 0 | 0 | transformers | [
"transformers",
"openvino",
"bert",
"fill-mask",
"custom_code",
"autotrain_compatible",
"region:us"
]
| fill-mask | 2024-06-28T11:23:24Z | Entry not found |
taric49/Llama3_SUM_TEST_1_adaptors | taric49 | 2024-06-28T11:25:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T11:23:29Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** taric49
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
acl-srw-2024/openthai-7b-unsloth-sft-checkpoints | acl-srw-2024 | 2024-06-28T23:34:44Z | 0 | 0 | null | [
"safetensors",
"region:us"
]
| null | 2024-06-28T11:23:38Z | Entry not found |
Susan774/female_english_voice_v1.3 | Susan774 | 2024-06-28T11:24:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2024-06-28T11:24:53Z | ---
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 ๐ค 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 -->
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Djtrice/D | Djtrice | 2024-06-28T11:28:41Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:28:41Z | Entry not found |
Bertinho24/Minniebyleelo | Bertinho24 | 2024-06-28T11:30:03Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T11:29:41Z | ---
license: openrail
---
|
whizzzzkid/whizzzzkid_249_2 | whizzzzkid | 2024-06-28T11:32:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T11:31:13Z | Entry not found |
nndang/checkpoint_NLU_slot_filling_250 | nndang | 2024-06-28T11:45:10Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-06-28T11:36:32Z | Entry not found |
RouaaJ/llama-3-8b-chat-para_navigator_enhanced_peft_model | RouaaJ | 2024-06-28T11:38:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T11:37:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Devops-hestabit/mixtral-instruct-trt | Devops-hestabit | 2024-07-01T12:52:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T11:37:43Z | ---
license: apache-2.0
---
|
Tragedy13/ppo-Huggy | Tragedy13 | 2024-06-28T11:38:48Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:38:48Z | Entry not found |
Susan774/dutch_female_speaker_5_all | Susan774 | 2024-06-28T11:54:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2024-06-28T11:39:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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africa3939/pixel2 | africa3939 | 2024-06-28T11:40:47Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:40:47Z | Entry not found |
Columbia-NLP/LION-Gemma-2b-odpo-v1.0 | Columbia-NLP | 2024-06-28T11:46:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T11:40:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
Padmanathan/output | Padmanathan | 2024-06-28T11:43:47Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:43:47Z | Entry not found |
smokeyScraper/doc_classifier | smokeyScraper | 2024-06-28T12:35:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:47:39Z | Entry not found |
Azrakael/customsucode2 | Azrakael | 2024-06-28T11:49:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:49:23Z | Entry not found |
aabbcc1199/pixelf2 | aabbcc1199 | 2024-06-28T12:10:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:51:57Z | Entry not found |
aabbcc1199/coloringbook | aabbcc1199 | 2024-06-28T12:11:45Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:54:28Z | Entry not found |
habulaj/1909918851 | habulaj | 2024-06-28T11:54:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:54:36Z | Entry not found |
procit002/dutch_v1 | procit002 | 2024-06-28T11:54:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2024-06-28T11:54:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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rmeguro/FTLLMPN | rmeguro | 2024-06-28T11:56:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T11:56:50Z | Entry not found |
Columbia-NLP/LION-LLaMA-3-8b-dpo-v1.0 | Columbia-NLP | 2024-06-28T12:15:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T11:57:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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This is the model card of a ๐ค 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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|
sebasberra/my_awesome_billsum_model | sebasberra | 2024-06-28T12:00:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:00:06Z | Entry not found |
LiveReader/suggestions_gemma-2-9b-bnb-4bit_finetuned | LiveReader | 2024-06-28T12:03:19Z | 0 | 0 | null | [
"license:gemma",
"region:us"
]
| null | 2024-06-28T12:03:19Z | ---
license: gemma
---
|
gkoloven/publicourmodel | gkoloven | 2024-06-28T12:04:26Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:04:26Z | Entry not found |
procit001/yes_no_model_dutch_4000 | procit001 | 2024-06-28T12:09:12Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T12:06:34Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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## Evaluation
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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aabbcc1199/Nuclear_War_Concept | aabbcc1199 | 2024-06-28T12:13:18Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:12:51Z | Entry not found |
whlzy/Sentry_image_models | whlzy | 2024-06-28T12:15:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T12:15:48Z | ---
license: apache-2.0
---
|
mynkchaudhry/CV | mynkchaudhry | 2024-06-28T12:16:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T12:16:48Z | ---
license: apache-2.0
---
|
aabbcc1199/3Drenderingstyle | aabbcc1199 | 2024-06-28T12:19:22Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:18:51Z | Entry not found |
blockblockblock/Llama-3-Instruct-8B-SPPO-Iter3-bpw5.5-exl2 | blockblockblock | 2024-06-28T12:19:33Z | 0 | 0 | null | [
"text-generation",
"conversational",
"en",
"dataset:openbmb/UltraFeedback",
"arxiv:2405.00675",
"license:apache-2.0",
"region:us"
]
| text-generation | 2024-06-28T12:19:29Z | ---
license: apache-2.0
datasets:
- openbmb/UltraFeedback
language:
- en
pipeline_tag: text-generation
---
Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)
# Llama-3-Instruct-8B-SPPO-Iter3
This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 3, based on the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) architecture as starting point. We utilized the prompt sets from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, splited to 3 parts for 3 iterations by [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset). All responses used are synthetic.
## Links to Other Models
- [Llama-3-Instruct-8B-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1)
- [Llama-3-Instruct-8B-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2)
- [Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3)
### Model Description
- Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
- Language(s) (NLP): Primarily English
- License: Apache-2.0
- Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)
| Model | LC. Win Rate | Win Rate | Avg. Length |
|-------------------------------------------|:------------:|:--------:|:-----------:|
|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) |31.73 |31.74 | 1962
|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) |35.15 |35.98 | 2021
|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) |**38.77** |**39.85** | 2066
## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness)
Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1
| | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average |
|--------|---------------|----------------|------------|-------|-----------|-------|---------|
|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) | 63.82 | 54.96 | 76.40 | 75.44 | 79.80 | 65.65 | 69.35
|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) | 64.93 | 56.48 | 76.87 | 75.13 | 80.39 | 65.67 | 69.91
|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) | 65.19 | 58.04 | 77.11 | 74.91 | 80.86 | 65.60 | **70.29**
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- eta: 1000
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 1
- seed: 42
- distributed_type: deepspeed_zero3
- num_devices: 8
- optimizer: RMSProp
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_train_epochs: 6.0 (stop at epoch=1.0)
## Citation
```
@misc{wu2024self,
title={Self-Play Preference Optimization for Language Model Alignment},
author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
year={2024},
eprint={2405.00675},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
SynthoCraft/mini | SynthoCraft | 2024-06-29T12:33:53Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2024-06-28T12:20:09Z | ---
license: other
license_name: conda
license_link: https://docs.conda.io/en/latest/license.html
---
|
abdullah-jokergames/mistral-7b-joker-v2 | abdullah-jokergames | 2024-06-28T12:21:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T12:21:21Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Evaluation
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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<!-- 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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nhemazAI/Self-help | nhemazAI | 2024-06-28T12:24:12Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
]
| null | 2024-06-28T12:24:12Z | ---
license: afl-3.0
---
|
CHARKA/Mistral-7B-Instruct-v0.3SmallDataV1 | CHARKA | 2024-06-28T12:26:14Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:26:14Z | Entry not found |
POOHHUB/GFG | POOHHUB | 2024-06-28T12:27:44Z | 0 | 0 | null | [
"license:gemma",
"region:us"
]
| null | 2024-06-28T12:27:43Z | ---
license: gemma
---
|
InfImagine/Sentry_image_models | InfImagine | 2024-06-28T16:38:06Z | 0 | 0 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T12:32:13Z | ---
license: apache-2.0
---
|
matrix88999/2112412 | matrix88999 | 2024-06-28T12:33:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:33:11Z | Entry not found |
dylanxditto/DittosModel | dylanxditto | 2024-06-28T12:35:28Z | 0 | 0 | null | [
"license:c-uda",
"region:us"
]
| null | 2024-06-28T12:35:28Z | ---
license: c-uda
---
|
CausalLM/33b-e | CausalLM | 2024-06-28T13:12:37Z | 0 | 3 | transformers | [
"transformers",
"causallm",
"text-generation",
"custom_code",
"en",
"zh",
"ja",
"de",
"license:wtfpl",
"autotrain_compatible",
"region:us"
]
| text-generation | 2024-06-28T12:39:02Z | ---
license: wtfpl
language:
- en
- zh
- ja
- de
---
# TBA
This model is suitable for inference with 8-bit precision (both weight and KV Cache applied) on a 48GB GPU at 20K context or 6.5 BPW on an 80GB GPU at 100K context.
| Memory | BPW (weights & KV Cache) | Context Length |
| ------ | ------------------------ | -------------- |
| 48GB | 8 | 20K tokens |
| 48GB | 6.5 | 32K tokens |
| 80GB | 6.5 | 100K tokens |
Custom code is used, so ensure safety before using 'trust_remote_code=True'.
This model has undergone minimal human preference alignment. Proper system prompts and prompt engineering are necessary to ensure desirable responses. The model references the safety settings of the Gemini model series and has received minimal safety alignment towards system prompts to avoid generating severely inappropriate responses without specific instructions. By default, it can engage in discussions on relatively sensitive topics without violating human ethical and moral principles. Of course, you can always specify "uncensored" in your system prompt to obtain "raw" responses that have not been aligned with human values.
Modified from Cohere models (CohereForAI/c4ai-command-r-v01, CohereForAI/aya-23-35B), users should follow their AUPs.
Tokenizer is different from cohere - and chat template is ChatML.
For more information, please refer to the SFT version: https://huggingface.co/CausalLM/35b-beta-long |
aabbcc1199/T-shirtdesigns | aabbcc1199 | 2024-06-28T12:42:16Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:40:51Z | Entry not found |
BestWishYsh/ChronoMagic-Bench | BestWishYsh | 2024-06-28T12:49:25Z | 0 | 12 | null | [
"text-to-video",
"en",
"dataset:BestWishYsh/ChronoMagic-Pro",
"dataset:BestWishYsh/ChronoMagic-ProH",
"dataset:BestWishYsh/ChronoMagic-Bench",
"arxiv:2406.18522",
"license:apache-2.0",
"region:us"
]
| text-to-video | 2024-06-28T12:43:43Z | ---
license: apache-2.0
datasets:
- BestWishYsh/ChronoMagic-Pro
- BestWishYsh/ChronoMagic-ProH
- BestWishYsh/ChronoMagic-Bench
language:
- en
pipeline_tag: text-to-video
---
# Paper
arxiv.org/abs/2406.18522 |
popcatesr/popcat | popcatesr | 2024-06-28T12:44:52Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2024-06-28T12:44:52Z | ---
license: other
license_name: popcat
license_link: LICENSE
---
|
crazyberry/loraTest | crazyberry | 2024-06-30T13:58:36Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T12:47:24Z | ---
license: apache-2.0
---
|
Jamin20/MixSpeech_AR | Jamin20 | 2024-06-28T12:48:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:48:34Z | Entry not found |
maedehm02/LLama3-Code-Instruct-Finetune-test | maedehm02 | 2024-06-28T12:55:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T12:49:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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marekleibl/vit-base-patch16-224-in21k-finetuned-lora-food101 | marekleibl | 2024-06-29T15:15:54Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T12:49:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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[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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#### Testing Data
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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Jamin20/Audio-based_Augment_MixSpeech_AR | Jamin20 | 2024-06-28T12:51:45Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:51:44Z | Entry not found |
wcldev/Biaxial_pretrained_model | wcldev | 2024-06-28T12:55:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T12:54:06Z | ---
license: apache-2.0
---
|
Jamin20/MixSpeech_SpecAugment_L_AR | Jamin20 | 2024-06-28T12:55:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:55:00Z | Entry not found |
habulaj/8217283661 | habulaj | 2024-06-28T12:55:27Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:55:26Z | Entry not found |
habulaj/1411215284 | habulaj | 2024-06-28T12:56:13Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:56:06Z | Entry not found |
matrix88999/23213 | matrix88999 | 2024-06-28T12:56:18Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:56:18Z | Entry not found |
habulaj/9030466739 | habulaj | 2024-06-28T12:57:57Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:57:53Z | Entry not found |
matrix88999/123123 | matrix88999 | 2024-06-28T12:59:11Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T12:59:11Z | Entry not found |
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