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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-05 12:28:32
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11.7k
| library_name
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MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF | MaziyarPanahi | 2024-03-02T13:29:09Z | 104 | 6 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"pytorch",
"mixtral",
"text-generation",
"axolotl",
"generated_from_trainer",
"moe",
"qwen",
"text-generation-inference",
"conversational",
"dataset:Crystalcareai/MoD-150k",
"base_model:MaziyarPanahi/Qwen1.5-8x7b",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/Qwen1.5-8x7b-v0.1",
"base_model:quantized:MaziyarPanahi/Qwen1.5-8x7b-v0.1",
"license:other"
] | text-generation | 2024-02-25T11:54:40Z | ---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- pytorch
- mixtral
- text-generation
- axolotl
- generated_from_trainer
- moe
- qwen
- text-generation-inference
- conversational
- dataset:Crystalcareai/MoD-150k
- base_model:MaziyarPanahi/Qwen1.5-8x7b
- autotrain_compatible
- endpoints_compatible
- region:us
- text-generation
model_name: Qwen1.5-8x7b-v0.1-GGUF
base_model: MaziyarPanahi/Qwen1.5-8x7b-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5fd5e18a90b6dc4633f6d292/3uKjrdYScOmoRkCbXqqNy.webp" width="550" />
# [MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Qwen1.5-8x7b-v0.1](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1)
## Description
[MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Qwen1.5-8x7b-v0.1](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1).
Total number of parameters: 38021632000 (38.021632)
## Examples
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. Think step by step and answer quickly.<|im_end|>
<|im_start|>user
explain step by step 25-4*2+3=?<|im_end|>
<|im_start|>assistant>
Here is the:
1. The expression reads: 25 minus 4 times 2 plus 3.
2. First, perform the multiplication: 4 * 2 = 8.
3. Then subtract: 25 - 8 = 17.
4. Finally, add the last number: 17 + 3 = 20.
So, in total, 25-4*2+3 equals 20.</s><|endoftext|>
```
---
```
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
### Instruction:
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
### Response: Once upon a time in a far-off galaxy, there lived an extraordinary being named Goku. He was known for his incredible strength and unyielding spirit, having faced countless enemies and emerged victorious.
One fateful day, while exploring the universe on his legendary journey, Goku stumbled upon a strange new planet. As he landed on its surface, he sensed a powerful energy emanating from within. Intrigued, he ventured forth into the, only to find himself face-to-face with a fearsome opponent - Kirby!
Kirby, a formidable warrior in his own right, had formed an alliance with another malevolent force - none other than the infamous Majin Buu! The duo had joined forces to wreak havoc across the and bring destruction upon the galaxy.
Goku, a hero at heart, could not stand idly by as these two wicked beings threatened the very existence of life itself. He knew it would be no easy task, but he was determined to confront them head-on and protect the innocent beings he had come to cherish throughout his travels.
With newfound resolve, Goku prepared for battle against this unparalleled evil. Drawing upon all his strength and cunning, he devised a plan to outsmart Kirby and Majin Buu. Utilizing his unparalleled speed and agility, Goku managed to evade their attacks and strike back with devastating blows.
As the battle raged on, the fate of the galaxy hung in the balance. With each passing moment, more and more lives were lost to the relentless onslaught of these malevolent foes. But Goku refused to give up, knowing that the future of the universe rested in his hands.
Finally, after what felt like an eternity, Goku's tireless efforts paid off. His strategic prowess and unmatched determination proved too much for Kirby and Majin Buu to handle. Overwhelmed by the sheer power of Goku's onslaught, they were forced to retreat, leaving behind a trail of destruction in their wake.
The galaxy breathed a collective sigh of relief as peace was restored. In the. Goku's name was forever etched into the annals of history, celebrated as a true champion of good and a guardian angel to all those who called the cosmos home.
And so, the legend of Goku continued to grow, inspiring generations of heroes to follow in his footsteps and defend the universe from all manner of cosmic threats. For even in the darkest of times, hope never truly dies, and there will always be a shining light to guide us through the darkness.</s><|endoftext|>
```
---
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant
Once upon a time in a far-off universe, there was a young warrior named Goku who lived on planet Earth. He was known throughout the.
One day, Goku received an urgent message from his old friend Krillin. Krillin told him that he had discovered something incredible - Kirby, the cute pink puffball creature, had joined forces with one of the most dangerous beings in all of space and time, the tyrannical Majin Buu.
Goku was shocked by this revelation. He knew how much Kirby loved to collect worlds, but he never imagined that he would team up with someone as evil as Majin Buu. Goku knew that he had to act fast if he wanted to save his home planet and its inhabitants.
With determination in his heart, Goku set out on a quest to find Kirby and stop him from destroying Earth. Along the way, he met other powerful allies, like his good friend Piccolo and his new comrade-in-arms, Vegeta. Together, they fought their way through dangerous realms and battled fearsome monsters to reach Kirby's secret lair.
When they finally arrived, they found Kirby and Majin Buu preparing to unleash a devastating attack on Earth. The two teams clashed in an epic battle, with Goku using all of his strength and skill to defeat Kirby and his minions.
In the end, Goku emerged victorious, thanks to the combined might of his friends and the power of friendship. With Earth safe once again, Goku returned home, grateful for the lessons he had learned along the way. From then on, he knew that no matter what challenges lay ahead, he could face them together with his family and friends.</s><|endoftext|>
```
---
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
describe about pros and cons of docker system.<|im_end|>
<|im_start|>assistant
Docker is a containerization platform that enables developers to build, ship, and run applications in containers. Here are some of the.
Pros:
1. Isolation: Containers isolate applications from each other and their environment, providing a lightweight way to package and deploy applications. This means you can have multiple instances of an application running on the, with each instance having its own file system and libraries, without impacting the host operating system or other containers.
2. Portability: Docker images are portable across different environments - they contain everything needed to run an application, including the runtime environment, dependencies, etc. You can push a Docker image to a registry and then pull it anywhere to run your application, as long as you have Docker installed.
3. Speed: Because containers share the same kernel with the host OS, there's no need for any additional configuration changes between development, testing, staging, and production environments. This makes it faster to iterate and deploy new versions of applications.
4. Scalability: With Docker, you can easily scale up or down the number of containers used by an application based on demand. For example, if you suddenly get a lot more traffic during a promotion, you can spin up extra containers to handle the.
Cons:
1. Complexity: Setting up and managing Docker can be complex at first, especially for teams not familiar with container technologies. It requires knowledge of Docker Compose, Kubernetes, Swarm, etc. to orchestrate and manage containers effectively.
2. Resource overhead: While Docker provides isolation through lightweight containers, it does require some resources compared to traditional virtual machines. If you're working with limited hardware, this could impact performance.
3. Learning curve: There's a steep learning curve when starting out with Docker, as it involves understanding how containers work, building images, managing containers, networking, etc. This can take time.
4. Ecosystem fragmentation: Docker has become so popular that many other container platforms like Kubernetes, Mesos, etc. have sprung up. This means there's a lot of overlap in terms of tools and capabilities, making it harder to switch between them.</s><|endoftext|>
```
---
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. Think step by step and answer quickly.<|im_end|>
<|im_start|>user
Mark is faster than Mary, Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step<|im_end|>
<|im_start|>assistant
Step 1: Identify
The given information states that Mark is faster than Mary, and Mary is faster than Joe.
Step 2: Compare
Using this information, we can compare their speeds in a direct order: Mark (faster) > Mary (faster) > Joe (slower).
Step 3: Determine
Based on our comparison, it is clear that Joe is not faster than Mark because he comes after Mark in the.
So, the answer is no, Joe is not faster than Mark.</s><|endoftext|>
```
---
```
<|im_start|>system
Perform the task to the best of your ability.<|im_end|>
<|im_start|>user
If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?<|im_end|>
<|im_start|>assistant
It should still only take 5 hours for all four shirts to dry if you spread them out so that each one gets an equal amount of sunlight.</s><|endoftext|>
```
---
```
<|im_start|>system
Perform the task to the best of your ability.<|im_end|>
<|im_start|>user
Build a webpage in one html file with html, css and javascript code, the webpage should have 10 jokes and the button that changing background colors randomly.<|im_end|>
<|im_start|>assistant
I apologize for any confusion, but as an AI language model, I am not able to build or interact with webpages directly. However, I can provide you with some sample HTML, CSS, and JavaScript code snippets that you can use to create a webpage containing 10 jokes and random background color changes.
Here's a simple example:
```
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Jokes</title>
<style>
* {
box-sizing: border-box;
}
body {
font-family: Arial, sans-serif;
background-color: #f2f2f2;
}
.container {
max-width: 600px;
margin: auto;
padding: 20px;
text-align: center;
}
h1 {
color: #333;
margin-bottom: 20px;
}
p {
color: #666;
line-height: 1.5;
}
button {
background-color: #4CAF50;
border: none;
color: white;
padding: 10px 20px;
cursor: pointer;
margin-top: 20px;
}
button:hover {
background-color: #45a049;
}
</style>
</head>
<body>
<div class="container">
<h1>Jokes</h1>
<p id="joke">Here is a joke: "Why don't programmers trust atoms? Because they make up everything!"</p>
<button onclick="changeBackground()">Change Background Colors!</button>
</div>
<script>
function changeBackground() {
var color = Math.floor(Math.random()*16777215).toString(16);
document.body.style.backgroundColor = "#" + ("00" + color).slice(-6);
}
</script>
</body>
</html>
```
```
In this code, we define a container div with a maximum width of 600px, centered on the. We also define styles for headings, paragraphs, buttons, and the overall page. The `changeBackground()` function generates a random hexadecimal value for the, then sets it as the background color for the.</s><|endoftext|>
```
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.2196 | 0.0 | 1 | 6.1942 |
| 0.7772 | 0.25 | 513 | 0.8037 |
| 0.656 | 0.5 | 1026 | 0.7977 |
| 0.6967 | 0.75 | 1539 | 0.7945 |
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: [MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF) and below it, a specific filename to download, such as: Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Qwen1.5-8x7b-v0.1-GGUF Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Qwen1.5-8x7b-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) |
OmarHaroon01/t5_small_finetune_ag_news_scratch | OmarHaroon01 | 2024-03-02T13:25:42Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T13:25:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
hautran7201/mBart50-tl-en2vi-lora | hautran7201 | 2024-03-02T13:23:41Z | 2 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:mt_eng_vietnamese",
"base_model:facebook/mbart-large-50-many-to-many-mmt",
"base_model:adapter:facebook/mbart-large-50-many-to-many-mmt",
"region:us"
] | null | 2024-03-02T07:57:42Z | ---
library_name: peft
tags:
- generated_from_trainer
datasets:
- mt_eng_vietnamese
metrics:
- bleu
base_model: facebook/mbart-large-50-many-to-many-mmt
model-index:
- name: mBart50-tl-en2vi-lora
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. -->
# mBart50-tl-en2vi-lora
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the mt_eng_vietnamese dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6172
- Bleu: 34.4063
- Gen Len: 32.7321
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 6.9999 | 0.21 | 2000 | 6.6880 | 32.0922 | 32.1749 |
| 6.9674 | 0.42 | 4000 | 6.6544 | 32.7344 | 32.7289 |
| 6.9357 | 0.63 | 6000 | 6.6310 | 34.0802 | 32.4878 |
| 6.9162 | 0.84 | 8000 | 6.6172 | 34.4063 | 32.7321 |
### Framework versions
- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2 |
Prasann15479/LLM_Science_1 | Prasann15479 | 2024-03-02T13:21:27Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-02-26T18:34:27Z | ---
license: apache-2.0
---
Model for my project : https://github.com/Nb4159/MCQ-Model |
Prasann15479/LLM_Science_2 | Prasann15479 | 2024-03-02T13:20:53Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2024-02-26T18:41:38Z | ---
license: apache-2.0
---
Model for my project : https://github.com/Nb4159/MCQ-Model |
LarryAIDraw/RirikoChigasaki-10 | LarryAIDraw | 2024-03-02T13:16:53Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-03-02T13:08:54Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/328702/ririko-chigasaki-taimanin |
LarryAIDraw/NinoV1 | LarryAIDraw | 2024-03-02T13:16:32Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-03-02T13:08:10Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/330045/nino-nakano-oror-the-quintessential-quintuplets |
daniel15568/Dog_Breeds_Classification | daniel15568 | 2024-03-02T13:07:41Z | 0 | 0 | null | [
"image-classification",
"license:other",
"region:us"
] | image-classification | 2024-03-02T13:03:18Z | ---
license: other
license_name: image-net
license_link: https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5
pipeline_tag: image-classification
--- |
justdimaa/q-FrozenLake-v1-4x4-noSlippery | justdimaa | 2024-03-02T12:59:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-02T12:59:44Z | ---
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="justdimaa/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"])
```
|
wynn-lee/mistral-7b-qlora-alpaca-sample-0.1k | wynn-lee | 2024-03-02T12:59:00Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-02T08:02:04Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
EricValen/poca-SoccerTwos | EricValen | 2024-03-02T12:57:02Z | 33 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2024-03-02T12:55:01Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: EricValen/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HaninZ/BERT-LARGE-lora-r-11 | HaninZ | 2024-03-02T12:50:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T12:50:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
Dadmatech/NER | Dadmatech | 2024-03-02T12:49:53Z | 0 | 0 | null | [
"token-classification",
"fa",
"license:mit",
"region:us"
] | token-classification | 2023-11-19T13:17:37Z | ---
license: mit
language:
- fa
pipeline_tag: token-classification
--- |
leilasjd1997/ppo-LunarLander-v2 | leilasjd1997 | 2024-03-02T12:45:34Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-02T12:45:15Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 228.17 +/- 41.33
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ashi-ta/japanese-pretrained-ckpts | ashi-ta | 2024-03-02T12:42:50Z | 0 | 0 | fairseq | [
"fairseq",
"ja",
"license:other",
"region:us"
] | null | 2023-03-14T05:16:04Z | ---
license: other
language:
- ja
library_name: fairseq
---
# Pre-trained checkpoints for speech representation in Japanese
The models in this repository were pre-trained via self-supervised learning (SSL) for speech representation.
The SSL models were built on the [fairseq](https://github.com/facebookresearch/fairseq) toolkit.
- `wav2vec2_base_csj.pt`
- fairseq checkpoint of wav2vec2.0 model with *Base* architecture pre-trained on 16kHz sampled speech data of Corpus of Spontaneous Japanese (CSJ)
- `wav2vec2_base_csj_hf`
- converted version of `wav2vec2_base_csj.pt` compatible with the interface of Hugging Face by using [this tool](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py)
- `hubert_base_csj.pt`
- fairseq checkpoint of HuBERT model with *Base* architecture pre-trained on 16kHz sampled speech data of Corpus of Spontaneous Japanese (CSJ)
- `hubert_base_csj_hf`
- converted version of `hubert_base_csj.pt` compatible with the interface of Hugging Face by using [this tool](https://github.com/huggingface/transformers/blob/main/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py)
If you find this helpful, please consider citing the following paper.
```text
@INPROCEEDINGS{ashihara_icassp23,
author={Takanori Ashihara and Takafumi Moriya and Kohei Matsuura and Tomohiro Tanaka},
title={Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models},
booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2023}
}
```
|
FtMi/trained_weigths | FtMi | 2024-03-02T12:41:13Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-02T12:41:07Z | ---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: trained_weigths
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. -->
# trained_weigths
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4339 | 1.0 | 181 | 0.4499 |
| 0.4524 | 2.0 | 362 | 0.4320 |
| 0.4695 | 3.0 | 543 | 0.4292 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1 |
oneonlee/LDCC-SOLAR-gugutypus-10.7B | oneonlee | 2024-03-02T12:35:27Z | 61 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"ko",
"dataset:kyujinpy/KOR-gugugu-platypus-set",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:finetune:LDCC/LDCC-SOLAR-10.7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-02T08:36:30Z | ---
language:
- en
- ko
license: cc-by-nc-4.0
datasets:
- kyujinpy/KOR-gugugu-platypus-set
base_model:
- LDCC/LDCC-SOLAR-10.7B
pipeline_tag: text-generation
---
# LDCC-SOLAR-gugutypus-10.7B
<img src="logo.png" height=350, width=350>
---
## Model Details
**Model Developers**
- DongGeon Lee ([oneonlee](https://huggingface.co/oneonlee))
**Model Architecture**
- **LDCC-SOLAR-gugutypus-10.7B** is a instruction fine-tuned auto-regressive language model, based on the [SOLAR](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) transformer architecture.
**Base Model**
- [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
**Training Dataset**
- [kyujinpy/KOR-gugugu-platypus-set](https://huggingface.co/datasets/kyujinpy/KOR-gugugu-platypus-set)
---
## Model comparisons
- **Ko-LLM leaderboard (2024/03/01)** [[link]](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ----------------------------------------- | ----------- | ------ | ------------ | ------- | ------------- | --------------- |
| **[oneonlee/KoSOLAR-v0.2-gugutypus-10.7B](https://huggingface.co/oneonlee/KoSOLAR-v0.2-gugutypus-10.7B)** | **51.17** | 47.78 | 58.29 | 47.27 | 48.31 | 54.19 |
| oneonlee/LDCC-SOLAR-gugutypus-10.7B | 49.45 | 45.9 | 55.46 | 47.96 | 48.93 | 49 |
<br>
- **(KOR) AI-Harness evaluation** [[link]](https://github.com/Beomi/ko-lm-evaluation-harness)
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|-------------------------|-------|------|-----:|------|-----:|---|-----:|
|KMMLU |N/A |none | 0|acc |0.3329|± |0.0794|
|KMMLU |N/A |none | 5|acc |0.3969|± |0.0816|
|KoBEST-HellaSwag | 0|none | 0|acc |0.4260|± |0.0221|
|KoBEST-HellaSwag | 0|none | 5|acc |0.4260|± |0.0221|
|KoBEST-BoolQ | 0|none | 0|acc |0.7792|± |0.0111|
|KoBEST-BoolQ | 0|none | 5|acc |0.8925|± |0.0083|
|KoBEST-COPA | 0|none | 0|acc |0.6670|± |0.0149|
|KoBEST-COPA | 0|none | 5|acc |0.7070|± |0.0144|
|KoBEST-SentiNeg | 0|none | 0|acc |0.7582|± |0.0215|
|KoBEST-SentiNeg | 0|none | 5|acc |0.9219|± |0.0135|
<br>
- **(ENG) AI-Harness evaluation** [[link]](https://github.com/EleutherAI/lm-evaluation-harness)
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|MMLU |N/A |none | 0|acc |0.5826|± |0.1432|
|MMLU |N/A |none | 5|acc |0.6124|± |0.1275|
|HellaSwag | 1|none | 0|acc |0.6075|± |0.0049|
|HellaSwag | 1|none | 5|acc |0.6534|± |0.0047|
|BoolQ | 2|none | 0|acc |0.8737|± |0.0058|
|BoolQ | 2|none | 5|acc |0.8878|± |0.0055|
|COPA | 1|none | 0|acc |0.8300|± |0.0378|
|COPA | 1|none | 5|acc |0.9300|± |0.0256|
|truthfulqa |N/A |none | 0|acc |0.4249|± |0.0023|
|truthfulqa |N/A |none | 5|acc | - |± | - |
---
## Implementation Code
```python
### LDCC-SOLAR-gugutypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "oneonlee/LDCC-SOLAR-gugutypus-10.7B"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
``` |
ThuyNT03/CS505_COQE_viT5_Prompting5_POSAL | ThuyNT03 | 2024-03-02T12:30:32Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T11:28:31Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_POSAL
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. -->
# CS505_COQE_viT5_Prompting5_POSAL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
CocaButon/distilbert-base-uncased-distilled-clinc | CocaButon | 2024-03-02T12:27:20Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T11:29:58Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1206
- Accuracy: 0.9403
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 0.7276 | 0.7248 |
| 0.9982 | 2.0 | 636 | 0.3153 | 0.8639 |
| 0.9982 | 3.0 | 954 | 0.1836 | 0.9181 |
| 0.3132 | 4.0 | 1272 | 0.1491 | 0.93 |
| 0.168 | 5.0 | 1590 | 0.1360 | 0.9361 |
| 0.168 | 6.0 | 1908 | 0.1287 | 0.9381 |
| 0.1362 | 7.0 | 2226 | 0.1252 | 0.9371 |
| 0.1245 | 8.0 | 2544 | 0.1225 | 0.9397 |
| 0.1245 | 9.0 | 2862 | 0.1210 | 0.9406 |
| 0.1191 | 10.0 | 3180 | 0.1206 | 0.9403 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LiukG/gut_1024-finetuned-lora-NT-2.5b-1000g | LiukG | 2024-03-02T12:24:40Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"generated_from_trainer",
"base_model:InstaDeepAI/nucleotide-transformer-2.5b-1000g",
"base_model:finetune:InstaDeepAI/nucleotide-transformer-2.5b-1000g",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T12:18:31Z | ---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-2.5b-1000g
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: nucleotide-transformer-finetuned-lora-NucleotideTransformer
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. -->
# nucleotide-transformer-finetuned-lora-NucleotideTransformer
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-1000g) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6900
- F1: 0.8402
- Mcc Score: 0.5492
- Accuracy: 0.7891
## 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: 0.0005
- train_batch_size: 3
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Mcc Score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 1.3108 | 0.05 | 100 | 0.8230 | 0.6491 | 0.3170 | 0.6367 |
| 0.9207 | 0.11 | 200 | 0.6670 | 0.6016 | 0.2636 | 0.6016 |
| 0.6734 | 0.16 | 300 | 0.5539 | 0.8190 | 0.4873 | 0.7617 |
| 0.7133 | 0.21 | 400 | 0.5834 | 0.8148 | 0.4994 | 0.7656 |
| 0.6225 | 0.26 | 500 | 0.8411 | 0.8343 | 0.5144 | 0.7656 |
| 0.8485 | 0.32 | 600 | 0.6813 | 0.7336 | 0.3999 | 0.6992 |
| 0.7567 | 0.37 | 700 | 0.6454 | 0.8504 | 0.5770 | 0.8008 |
| 0.5729 | 0.42 | 800 | 0.8756 | 0.7910 | 0.4676 | 0.7461 |
| 0.7708 | 0.47 | 900 | 0.6872 | 0.8303 | 0.5314 | 0.7812 |
| 0.6194 | 0.53 | 1000 | 0.6900 | 0.8402 | 0.5492 | 0.7891 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
michimalek/zephyr-3b-tokenizer-finetuned | michimalek | 2024-03-02T12:24:27Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T12:24:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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]
|
gotzmann/v0.7a-adapter | gotzmann | 2024-03-02T12:21:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:gotzmann/uni",
"base_model:adapter:gotzmann/uni",
"license:other",
"region:us"
] | null | 2024-03-02T12:20:19Z | ---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: gotzmann/uni
model-index:
- name: exported
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. -->
# exported
This model is a fine-tuned version of [gotzmann/uni](https://huggingface.co/gotzmann/uni) on the final 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 |
aisuko/ft-roberta-large-on-bionlp2004-lora | aisuko | 2024-03-02T12:16:50Z | 3 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:bionlp2004",
"base_model:FacebookAI/roberta-large",
"base_model:adapter:FacebookAI/roberta-large",
"license:mit",
"region:us"
] | null | 2024-01-15T23:23:23Z | ---
license: mit
library_name: peft
tags:
- generated_from_trainer
datasets:
- bionlp2004
base_model: roberta-large
model-index:
- name: ft-roberta-large-on-bionlp2004-lora
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. -->
# ft-roberta-large-on-bionlp2004-lora
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the tner/bionlp2004 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1835
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 125 | 0.2164 |
| No log | 2.0 | 250 | 0.1835 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0 |
HamdanXI/t5-small-finetuned-beethoven-qa | HamdanXI | 2024-03-02T12:01:13Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"text-generation",
"en",
"dataset:HamdanXI/beethoven_qa",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T11:57:10Z | ---
datasets:
- HamdanXI/beethoven_qa
language:
- en
pipeline_tag: text-generation
--- |
CultriX/MergeCeption-7B-v1 | CultriX | 2024-03-02T11:56:32Z | 9 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"CultriX/MonaTrix-v4",
"mlabonne/OmniTruthyBeagle-7B-v0",
"Kukedlc/NeuralMaxime-7B-slerp",
"CultriX/NeuralTrixlaser-bf16",
"base_model:CultriX/MonaTrix-v4",
"base_model:merge:CultriX/MonaTrix-v4",
"base_model:CultriX/NeuralTrixlaser-bf16",
"base_model:merge:CultriX/NeuralTrixlaser-bf16",
"base_model:Kukedlc/NeuralMaxime-7B-slerp",
"base_model:merge:Kukedlc/NeuralMaxime-7B-slerp",
"base_model:mlabonne/OmniTruthyBeagle-7B-v0",
"base_model:merge:mlabonne/OmniTruthyBeagle-7B-v0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T11:49:09Z | ---
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/NeuralTrixlaser-bf16
base_model:
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/NeuralTrixlaser-bf16
---
# MergeCeption-7B-v1
MergeCeption-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [CultriX/MonaTrix-v4](https://huggingface.co/CultriX/MonaTrix-v4)
* [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
* [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp)
* [CultriX/NeuralTrixlaser-bf16](https://huggingface.co/CultriX/NeuralTrixlaser-bf16)
## 🧩 Configuration
```yaml
base_model: CultriX/MonaTrix-v4
merge_method: dare_ties
models:
- model: CultriX/MonaTrix-v4
parameters:
density: 0.65
weight: 0.4
- model: mlabonne/OmniTruthyBeagle-7B-v0
parameters:
density: 0.65
weight: 0.4
- model: Kukedlc/NeuralMaxime-7B-slerp
parameters:
density: 0.65
weight: 0.4
- model: CultriX/NeuralTrixlaser-bf16
parameters:
density: 0.6
weight: 0.35
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/MergeCeption-7B-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
traderpedroso/thesisGPT-Lora | traderpedroso | 2024-03-02T11:48:13Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | 2024-03-02T11:42:18Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: out
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. -->
# out
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6233
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3371 | 1.0 | 1530 | 0.6235 |
| 0.2995 | 2.0 | 3060 | 0.5914 |
| 0.1539 | 3.0 | 4590 | 0.6233 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
HariprasathSB/whisper-large-v2-tamil-100steps | HariprasathSB | 2024-03-02T11:34:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T11:34:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ThuyNT03/CS505_COQE_viT5_Prompting5_SPOAL | ThuyNT03 | 2024-03-02T11:30:33Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T10:20:03Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_SPOAL
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. -->
# CS505_COQE_viT5_Prompting5_SPOAL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
drakrig/ppo-Pyramids | drakrig | 2024-03-02T11:27:29Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2024-03-02T11:27:24Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: drakrig/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
wolverinex24/red-ducati-all-view-sd | wolverinex24 | 2024-03-02T11:25:50Z | 4 | 1 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-02T10:15:35Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### red-ducati-all-view-sd Dreambooth model trained by priyanshu singh following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 309302221011
For the prompts use
### duc "your prompt here"
Sample pictures of this concept:
|
ThuyNT03/CS505_COQE_viT5_Prompting5_OASPL | ThuyNT03 | 2024-03-02T11:22:48Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T10:21:10Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_OASPL
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. -->
# CS505_COQE_viT5_Prompting5_OASPL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
IT-community/HeartDiseaseClassification | IT-community | 2024-03-02T11:22:04Z | 0 | 0 | null | [
"health",
"classification",
"en",
"region:us"
] | null | 2024-03-02T11:09:09Z | ---
language:
- en
metrics:
- accuracy
tags:
- health
- classification
---
# Model Name
## Overview
This repository contains the implementation of a machine learning model for predicting [mention the task or purpose of the model]. The model is trained using [describe the dataset used for training].
## Dataset
The dataset used for training this model is sourced from [https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data]. It consists of [319795] instances and [18] features. The dataset was preprocessed using various techniques, including:
- Handling missing values
- Encoding categorical variables
- Feature scaling or normalization
## Model Architecture
The model architecture includes the following algorithms:
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Decision Tree
- Random Forest
- Long Short-Term Memory (LSTM)
- Convolutional Neural Network (CNN)
## Cleaning Techniques
During preprocessing, the following cleaning techniques were applied to the dataset:
- Encoding categorical variables: Categorical variables were encoded using one-hot encoding.
- Feature scaling or normalization: Numerical features were scaled or normalized to ensure uniformity across different features.
## Usage
To use the model, clone this repository and follow the instructions provided in the respective model's directory. Each algorithm has its implementation and usage instructions.
## License
[Specify the license under which the model and code are released, e.g., MIT License, Apache License 2.0, etc.]
## Contact
For questions or inquiries, please contact [your email or contact information].
|
ThuyNT03/CS505_COQE_viT5_Prompting5_SAOPL | ThuyNT03 | 2024-03-02T11:21:02Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T10:19:17Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_SAOPL
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. -->
# CS505_COQE_viT5_Prompting5_SAOPL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
jjaq/civitai-sdxUnstableDiffusers | jjaq | 2024-03-02T11:19:48Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:TencentARC/PhotoMaker",
"base_model:adapter:TencentARC/PhotoMaker",
"region:us"
] | text-to-image | 2024-03-02T11:13:31Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/image_1 (9).png
base_model: TencentARC/PhotoMaker
instance_prompt: img
---
# civitai-sdxUnstableDiffusers
<Gallery />
## Trigger words
You should use `img` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/jjaq/civitai-sdxUnstableDiffusers/tree/main) them in the Files & versions tab.
|
OmarHaroon01/t5v1_small_finetune_ag_news_main_model | OmarHaroon01 | 2024-03-02T11:19:41Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T11:19:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ryusangwon/dialogsum_6024_pegasus-large | ryusangwon | 2024-03-02T11:15:21Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-large",
"base_model:finetune:google/pegasus-large",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T06:05:56Z | ---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: dialogsum_6024_pegasus-large
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. -->
# dialogsum_6024_pegasus-large
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3817
- Rouge1: 0.4784
- Rouge2: 0.2298
- Rougel: 0.3973
- Rougelsum: 0.3979
- Gen Len: 37.464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3559 | 5.13 | 500 | 0.3817 | 0.4784 | 0.2298 | 0.3973 | 0.3979 | 37.464 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
gonpachiro/ppo-LunarLander-v2 | gonpachiro | 2024-03-02T11:12:25Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-02T11:12:03Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 238.27 +/- 43.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
heegyu/ko-llama-412M-random | heegyu | 2024-03-02T11:10:02Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T11:09:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
elanoqi/wb-ghibli-700 | elanoqi | 2024-03-02T11:06:54Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:wavymulder/Analog-Diffusion",
"base_model:finetune:wavymulder/Analog-Diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-02T10:42:07Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- stable-diffusion
- stable-diffusion-diffusers
inference: true
base_model: wavymulder/Analog-Diffusion
instance_prompt: a photo of jksj man
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - elanoqi/wb-ghibli-700
This is a dreambooth model derived from wavymulder/Analog-Diffusion. The weights were trained on a photo of jksj man using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
jjaq/civitai-sdxlUnstableDiffusersRundiffusion | jjaq | 2024-03-02T11:03:05Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:TencentARC/PhotoMaker",
"base_model:adapter:TencentARC/PhotoMaker",
"license:apache-2.0",
"region:us"
] | text-to-image | 2024-02-04T17:40:03Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/image_1 (6).png
base_model: TencentARC/PhotoMaker
instance_prompt: img
license: apache-2.0
---
# civitai-sdxUnstableDiffusers
<Gallery />
## Trigger words
You should use `img` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/jjaq/civitai-sdxUnstableDiffusers/tree/main) them in the Files & versions tab.
|
azizksar/simpleinput | azizksar | 2024-03-02T10:59:12Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-02T10:59:06Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: simpleinput
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. -->
# simpleinput
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2 |
falan42/llama-mini-qlora-finetuni_psikolaji-mark1.3 | falan42 | 2024-03-02T10:53:11Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T10:53:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
HaninZ/BERT-LARGE-lora-r-8 | HaninZ | 2024-03-02T10:51:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T10:51:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **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]
|
MemorialStar/distilbert-base-uncased-finetuned-emotion | MemorialStar | 2024-03-02T10:47:06Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T08:03:24Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.931
- name: F1
type: f1
value: 0.9309142811171885
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2164
- Accuracy: 0.931
- F1: 0.9309
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8133 | 1.0 | 250 | 0.2963 | 0.9135 | 0.9129 |
| 0.2443 | 2.0 | 500 | 0.2164 | 0.931 | 0.9309 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ndbao2002/ldm-cifar-32 | ndbao2002 | 2024-03-02T10:42:29Z | 45 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2024-02-22T14:22:01Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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]
|
drakrig/ppo-SnowballTarget | drakrig | 2024-03-02T10:38:47Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2024-03-02T10:38:41Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: drakrig/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mehdirafiei/GEMMA7B3EV6 | mehdirafiei | 2024-03-02T10:33:57Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T10:29:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DGurgurov/indonesian-wiki-lang-adapter | DGurgurov | 2024-03-02T10:31:34Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | null | 2024-03-01T16:11:16Z | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ms
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. -->
# ms
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4335
- Accuracy: 0.7183
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8607 | 0.05 | 500 | 1.6380 | 0.6876 |
| 1.7966 | 0.1 | 1000 | 1.6063 | 0.6916 |
| 1.7581 | 0.15 | 1500 | 1.5902 | 0.6949 |
| 1.7313 | 0.2 | 2000 | 1.5726 | 0.6975 |
| 1.7143 | 0.25 | 2500 | 1.5630 | 0.6992 |
| 1.7074 | 0.3 | 3000 | 1.5562 | 0.6997 |
| 1.699 | 0.34 | 3500 | 1.5440 | 0.7020 |
| 1.6797 | 0.39 | 4000 | 1.5376 | 0.7026 |
| 1.6813 | 0.44 | 4500 | 1.5355 | 0.7028 |
| 1.6641 | 0.49 | 5000 | 1.5276 | 0.7041 |
| 1.672 | 0.54 | 5500 | 1.5212 | 0.7048 |
| 1.6506 | 0.59 | 6000 | 1.5167 | 0.7058 |
| 1.6592 | 0.64 | 6500 | 1.5083 | 0.7069 |
| 1.6386 | 0.69 | 7000 | 1.5050 | 0.7071 |
| 1.6449 | 0.74 | 7500 | 1.5033 | 0.7078 |
| 1.6362 | 0.79 | 8000 | 1.5032 | 0.7073 |
| 1.6337 | 0.84 | 8500 | 1.4978 | 0.7082 |
| 1.622 | 0.89 | 9000 | 1.4971 | 0.7089 |
| 1.6279 | 0.94 | 9500 | 1.4919 | 0.7094 |
| 1.6199 | 0.98 | 10000 | 1.4900 | 0.7096 |
| 1.6218 | 1.03 | 10500 | 1.4914 | 0.7099 |
| 1.6144 | 1.08 | 11000 | 1.4814 | 0.7113 |
| 1.6017 | 1.13 | 11500 | 1.4789 | 0.7113 |
| 1.6092 | 1.18 | 12000 | 1.4755 | 0.7119 |
| 1.6083 | 1.23 | 12500 | 1.4766 | 0.7119 |
| 1.6081 | 1.28 | 13000 | 1.4791 | 0.7113 |
| 1.615 | 1.33 | 13500 | 1.4722 | 0.7127 |
| 1.605 | 1.38 | 14000 | 1.4725 | 0.7124 |
| 1.598 | 1.43 | 14500 | 1.4763 | 0.7119 |
| 1.6004 | 1.48 | 15000 | 1.4661 | 0.7132 |
| 1.6074 | 1.53 | 15500 | 1.4713 | 0.7123 |
| 1.603 | 1.58 | 16000 | 1.4658 | 0.7135 |
| 1.5928 | 1.62 | 16500 | 1.4646 | 0.7135 |
| 1.5942 | 1.67 | 17000 | 1.4676 | 0.7132 |
| 1.5914 | 1.72 | 17500 | 1.4604 | 0.7145 |
| 1.5931 | 1.77 | 18000 | 1.4580 | 0.7142 |
| 1.5808 | 1.82 | 18500 | 1.4606 | 0.7148 |
| 1.5911 | 1.87 | 19000 | 1.4592 | 0.7143 |
| 1.5842 | 1.92 | 19500 | 1.4622 | 0.7143 |
| 1.5838 | 1.97 | 20000 | 1.4584 | 0.7146 |
| 1.585 | 2.02 | 20500 | 1.4630 | 0.7139 |
| 1.5772 | 2.07 | 21000 | 1.4557 | 0.7146 |
| 1.589 | 2.12 | 21500 | 1.4556 | 0.7151 |
| 1.5653 | 2.17 | 22000 | 1.4533 | 0.7154 |
| 1.5753 | 2.22 | 22500 | 1.4574 | 0.7152 |
| 1.5707 | 2.26 | 23000 | 1.4531 | 0.7155 |
| 1.5744 | 2.31 | 23500 | 1.4534 | 0.7153 |
| 1.5741 | 2.36 | 24000 | 1.4531 | 0.7155 |
| 1.5696 | 2.41 | 24500 | 1.4480 | 0.7161 |
| 1.5844 | 2.46 | 25000 | 1.4526 | 0.7157 |
| 1.576 | 2.51 | 25500 | 1.4478 | 0.7160 |
| 1.5621 | 2.56 | 26000 | 1.4497 | 0.7158 |
| 1.5707 | 2.61 | 26500 | 1.4514 | 0.7159 |
| 1.5819 | 2.66 | 27000 | 1.4478 | 0.7164 |
| 1.5663 | 2.71 | 27500 | 1.4503 | 0.7159 |
| 1.5834 | 2.76 | 28000 | 1.4507 | 0.7160 |
| 1.5726 | 2.81 | 28500 | 1.4426 | 0.7165 |
| 1.5695 | 2.86 | 29000 | 1.4466 | 0.7166 |
| 1.5791 | 2.9 | 29500 | 1.4464 | 0.7167 |
| 1.5711 | 2.95 | 30000 | 1.4434 | 0.7170 |
| 1.5724 | 3.0 | 30500 | 1.4423 | 0.7169 |
| 1.5648 | 3.05 | 31000 | 1.4410 | 0.7173 |
| 1.5666 | 3.1 | 31500 | 1.4427 | 0.7168 |
| 1.567 | 3.15 | 32000 | 1.4424 | 0.7170 |
| 1.5569 | 3.2 | 32500 | 1.4441 | 0.7168 |
| 1.5717 | 3.25 | 33000 | 1.4385 | 0.7182 |
| 1.5585 | 3.3 | 33500 | 1.4416 | 0.7175 |
| 1.5572 | 3.35 | 34000 | 1.4415 | 0.7174 |
| 1.5585 | 3.4 | 34500 | 1.4368 | 0.7181 |
| 1.5705 | 3.45 | 35000 | 1.4400 | 0.7178 |
| 1.5712 | 3.5 | 35500 | 1.4420 | 0.7173 |
| 1.5651 | 3.55 | 36000 | 1.4355 | 0.7186 |
| 1.5595 | 3.59 | 36500 | 1.4363 | 0.7179 |
| 1.5613 | 3.64 | 37000 | 1.4385 | 0.7179 |
| 1.5594 | 3.69 | 37500 | 1.4422 | 0.7172 |
| 1.5574 | 3.74 | 38000 | 1.4370 | 0.7179 |
| 1.5557 | 3.79 | 38500 | 1.4410 | 0.7177 |
| 1.5649 | 3.84 | 39000 | 1.4349 | 0.7181 |
| 1.5635 | 3.89 | 39500 | 1.4406 | 0.7174 |
| 1.5569 | 3.94 | 40000 | 1.4362 | 0.7182 |
| 1.5661 | 3.99 | 40500 | 1.4369 | 0.7180 |
| 1.5612 | 4.04 | 41000 | 1.4380 | 0.7178 |
| 1.5632 | 4.09 | 41500 | 1.4374 | 0.7180 |
| 1.5617 | 4.14 | 42000 | 1.4374 | 0.7178 |
| 1.5452 | 4.19 | 42500 | 1.4341 | 0.7185 |
| 1.5644 | 4.23 | 43000 | 1.4358 | 0.7181 |
| 1.5448 | 4.28 | 43500 | 1.4392 | 0.7178 |
| 1.559 | 4.33 | 44000 | 1.4363 | 0.7180 |
| 1.5599 | 4.38 | 44500 | 1.4332 | 0.7185 |
| 1.5586 | 4.43 | 45000 | 1.4391 | 0.7179 |
| 1.5527 | 4.48 | 45500 | 1.4355 | 0.7183 |
| 1.5592 | 4.53 | 46000 | 1.4314 | 0.7187 |
| 1.55 | 4.58 | 46500 | 1.4352 | 0.7179 |
| 1.5592 | 4.63 | 47000 | 1.4367 | 0.7180 |
| 1.5586 | 4.68 | 47500 | 1.4346 | 0.7183 |
| 1.5547 | 4.73 | 48000 | 1.4321 | 0.7192 |
| 1.5572 | 4.78 | 48500 | 1.4397 | 0.7174 |
| 1.5537 | 4.83 | 49000 | 1.4334 | 0.7186 |
| 1.5546 | 4.87 | 49500 | 1.4347 | 0.7181 |
| 1.5518 | 4.92 | 50000 | 1.4365 | 0.7183 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
mehdirafiei/SQLCODER7B3EV6 | mehdirafiei | 2024-03-02T10:25:56Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T10:21:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
ThuyNT03/CS505_COQE_viT5_Prompting5_ASOPL | ThuyNT03 | 2024-03-02T10:17:07Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T09:15:07Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_ASOPL
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. -->
# CS505_COQE_viT5_Prompting5_ASOPL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
ThuyNT03/CS505_COQE_viT5_Prompting5_AOSPL | ThuyNT03 | 2024-03-02T10:14:43Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T09:12:55Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_AOSPL
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. -->
# CS505_COQE_viT5_Prompting5_AOSPL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
ThuyNT03/CS505_COQE_viT5_Prompting5_OPSAL | ThuyNT03 | 2024-03-02T10:13:21Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T09:11:19Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_OPSAL
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. -->
# CS505_COQE_viT5_Prompting5_OPSAL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
ThuyNT03/CS505_COQE_viT5_Prompting5_AOPSL | ThuyNT03 | 2024-03-02T10:11:50Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T09:09:00Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_AOPSL
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. -->
# CS505_COQE_viT5_Prompting5_AOPSL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
pruas/BENT-PubMedBERT-NER-Gene | pruas | 2024-03-02T10:11:03Z | 2,318 | 13 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-14T11:52:33Z | ---
language:
- en
pipeline_tag: token-classification
license: apache-2.0
---
Named Entity Recognition (NER) model to recognize gene and protein entities.
Please cite our work:
```
@article{NILNKER2022,
title = {NILINKER: Attention-based approach to NIL Entity Linking},
journal = {Journal of Biomedical Informatics},
volume = {132},
pages = {104137},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.104137},
url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
author = {Pedro Ruas and Francisco M. Couto},
}
```
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html): entity type "Genes/Proteins"
- [CellFinder](https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/): entity type "GeneProtein"
- [CoMAGC](http://biopathway.org/CoMAGC/): entity "Gene"
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "PR"
- [GREC Corpus](http://www.nactem.ac.uk/GREC/standoff.php): entity types "Gene", "Protein", "Protein_Complex", "Enzyme"
- [JNLPBA](http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004): entity types "protein", "DNA", "RNA"
- [PGxCorpus](https://www.nature.com/articles/s41597-019-0342-9): entity type "Gene_or_protein"
- [FSU_PRGE](https://julielab.de/Resources/FSU_PRGE.html): entity types "protein", "protein_complex", "protein_familiy_or_group"
- [BC2GM corpus](https://github.com/spyysalo/bc2gm-corpus)- [](): entity type
- [CHEMPROT](https://biocreative.bioinformatics.udel.edu/resources/corpora/chemprot-corpus-biocreative-vi/): entity types "GENE-Y", "GENE-N"
- [mTOR pathway event corpus](https://github.com/openbiocorpora/mtor-pathway/tree/master/original-data): entity type "Protein"
- [DNA Methylation](https://github.com/openbiocorpora/dna-methylation/tree/master/original-data)
- [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-ggp-IOB): entity type "Gene/protein"
- [BioNLP09](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP09-IOB)
- [BioNLP11EPI](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11EPI-IOB)
- [BioNLP13CG](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13CG-ggp-IOB): entity type "gene_or_gene_product"
- [BioNLP13GE](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13GE-IOB): entity type "Protein"
- [BioNLP13PC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13PC-ggp-IOB): entity type "Gene_or_gene_product"
- [MLEE](http://nactem.ac.uk/MLEE/): entity type "Gene_or_gene_product" |
pruas/BENT-PubMedBERT-NER-Anatomical | pruas | 2024-03-02T10:09:23Z | 16 | 4 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-14T12:29:03Z | ---
language:
- en
pipeline_tag: token-classification
license: apache-2.0
---
Named Entity Recognition (NER) model to recognize anatomical entities.
Please cite our work:
```
@article{NILNKER2022,
title = {NILINKER: Attention-based approach to NIL Entity Linking},
journal = {Journal of Biomedical Informatics},
volume = {132},
pages = {104137},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.104137},
url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
author = {Pedro Ruas and Francisco M. Couto},
}
```
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [MANTRA](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986661/pdf/ocv037.pdf): entity type "ANAT"
- [CellFinder](http://cellfinder.org/about/annotation/): entity type "Anatomical parts"
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "UBERON"
- [MLEE](http://nactem.ac.uk/MLEE/): entity types "Anatomical_system", "Organ", "Multi-tissue structure", "Tissue", "Immaterial_anatomical_entity"
- [AnatEM](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/AnatEM-IOB)
- [BioNLP13CG](): entity types "Multi-tissue structure", "Tissue", "Organ", "Immaterial anatomical entity", "Anatomical system"
- [GREC](http://www.nactem.ac.uk/GREC/standoff.php): entity type "Tissue" |
pruas/BENT-PubMedBERT-NER-Cell-Line | pruas | 2024-03-02T10:08:07Z | 422 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-01-14T14:25:56Z | ---
language:
- en
pipeline_tag: token-classification
---
Named Entity Recognition (NER) model to recognize cell line entities.
Please cite our work:
```
@article{NILNKER2022,
title = {NILINKER: Attention-based approach to NIL Entity Linking},
journal = {Journal of Biomedical Informatics},
volume = {132},
pages = {104137},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.104137},
url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
author = {Pedro Ruas and Francisco M. Couto},
}
```
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [CellFinder](http://cellfinder.org/about/annotation/): entity type "CellLine"
- [JNLPBA](http://www.geniaproject.org/genia-corpus/term-corpus): entity type "cell_line" |
sarthakharne/gemma-diagnosis-trained-epoch-1 | sarthakharne | 2024-03-02T09:53:47Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T09:29:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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HuggingKiwi/llama2-7b-qlora-koalpacaV1.1-1ep | HuggingKiwi | 2024-03-02T09:46:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T09:46:38Z | ---
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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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
AmilaUvaz/autotrain-opsj6-3l2rt | AmilaUvaz | 2024-03-02T09:46:39Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2024-03-02T09:46:35Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <1girl called ava, medium long blond hair, ava>
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
CatBarks/GPT2ES_spamming-email-classification4_4_tokenizer | CatBarks | 2024-03-02T09:45:07Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T09:45:06Z | ---
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 🤗 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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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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|
CatBarks/GPT2ES_spamming-email-classification4_4_model | CatBarks | 2024-03-02T09:45:04Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:44:01Z | ---
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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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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|
yuuuui/yuuuui | yuuuui | 2024-03-02T09:33:48Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:33:33Z | ---
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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|
babybirdprd/Experiment26-7B-passthrough-6slice | babybirdprd | 2024-03-02T09:32:04Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Experiment26-7B",
"base_model:yam-peleg/Experiment26-7B",
"base_model:finetune:yam-peleg/Experiment26-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T09:26:32Z | ---
tags:
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
base_model:
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
- yam-peleg/Experiment26-7B
---
# Experiment26-7B-passthrough-6slice
Experiment26-7B-passthrough-6slice is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [0, 7]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [5, 12]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [10, 17]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [15, 22]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [20, 27]
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [25, 32]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "babybirdprd/Experiment26-7B-passthrough-6slice"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
KaggleMasterX/gemma7b_ver1_adapter | KaggleMasterX | 2024-03-02T09:30:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-01T07:09:38Z | ---
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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|
llm-finetune/FTuned | llm-finetune | 2024-03-02T09:24:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T09:24:13Z | ---
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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|
october-sd/mt5-small-finetuned-mr-sum | october-sd | 2024-03-02T09:23:06Z | 14 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2024-03-01T11:30:35Z | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-mr-sum
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. -->
# mt5-small-finetuned-mr-sum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9029
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1339 | 1.0 | 1803 | 2.2075 |
| 3.1992 | 2.0 | 3606 | 2.0707 |
| 3.0189 | 3.0 | 5409 | 2.0077 |
| 2.9222 | 4.0 | 7212 | 1.9655 |
| 2.8524 | 5.0 | 9015 | 1.9323 |
| 2.81 | 6.0 | 10818 | 1.9261 |
| 2.7811 | 7.0 | 12621 | 1.9078 |
| 2.7658 | 8.0 | 14424 | 1.9029 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
|
AAAgnes/Testing_model | AAAgnes | 2024-03-02T09:22:57Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:22:41Z | ---
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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|
12345deena/outputs | 12345deena | 2024-03-02T09:20:44Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:adapter:google-t5/t5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-03-02T09:20:41Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google-t5/t5-base
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
Steven-GU-Yu-Di/Testing-model | Steven-GU-Yu-Di | 2024-03-02T09:20:23Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:20:09Z | ---
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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[More Information Needed]
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<!-- 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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|
Celiasy/testing_model | Celiasy | 2024-03-02T09:20:05Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:50Z | ---
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
lingaying/test_model_name | lingaying | 2024-03-02T09:19:57Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:43Z | ---
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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[More Information Needed]
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|
wxrrrrrrr/model_test | wxrrrrrrr | 2024-03-02T09:19:56Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
jim33282007/testing_model | jim33282007 | 2024-03-02T09:19:36Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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<!-- 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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|
jchen1234567/testing_model_1 | jchen1234567 | 2024-03-02T09:19:19Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
swangfr/new_model | swangfr | 2024-03-02T09:19:15Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
isom5240ust/testing_model | isom5240ust | 2024-03-02T09:19:15Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
katowtkkk/testing_model | katowtkkk | 2024-03-02T09:19:11Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T09:19:03Z | ---
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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|
ccrains/larson-gemma-2b-chinese-v0.1 | ccrains | 2024-03-02T09:14:59Z | 5 | 2 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T08:54:09Z | ---
license: apache-2.0
---
base_model:https://huggingface.co/google/gemma-2b
Chinese chat demo of gemma-2b：
the language of model: chinese and english
The following uses gemma-2b (a language model that only supports English) to train a large model process that supports Chinese and English.
step 1:
Use SentencePiece(bpe) to train Chinese corpus to obtain tokenizer.model and tokenizer.vocab
step 2：
Merge the Chinese of tokenizer.model and the original of tokenizer.model
step 3：
Use the merged special_tokens_map.json, tokenizer.model, tokenizer_config.json to replace the files of the original model (such as gemma-2b)
step 4:
Use LLaMA-Factory for pre-training. Pay attention to the pre-training parameters. Resize vocab and resize embedding are required.
step 5:
Based on the model pre-trained in step 4, the instructions are fine-tuned, which significantly improves the model's ability to understand and execute instructions.
step 6:
Based on the instruction fine-tuning model, we can use this model for SFT training under different specific tasks, so that the model can perform better on specific tasks.
|
ryusangwon/dialogsum_9836_t5-base | ryusangwon | 2024-03-02T09:06:56Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T06:06:34Z | ---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: dialogsum_9836_t5-base
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. -->
# dialogsum_9836_t5-base
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3512
- Rouge1: 0.3872
- Rouge2: 0.1689
- Rougel: 0.3341
- Rougelsum: 0.3346
- Gen Len: 18.888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3796 | 2.57 | 500 | 0.3827 | 0.3767 | 0.1539 | 0.3188 | 0.3187 | 18.85 |
| 0.3397 | 5.14 | 1000 | 0.3576 | 0.3882 | 0.1637 | 0.3336 | 0.3333 | 18.91 |
| 0.3338 | 7.7 | 1500 | 0.3512 | 0.3872 | 0.1689 | 0.3341 | 0.3346 | 18.888 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
JDGhadge/Yash | JDGhadge | 2024-03-02T08:44:23Z | 0 | 0 | bertopic | [
"bertopic",
"biology",
"translation",
"as",
"dataset:HuggingFaceTB/cosmopedia",
"license:apache-2.0",
"region:us"
] | translation | 2024-03-02T08:43:13Z | ---
license: apache-2.0
datasets:
- HuggingFaceTB/cosmopedia
language:
- as
metrics:
- accuracy
library_name: bertopic
pipeline_tag: translation
tags:
- biology
--- |
bilalRahib/Mental-Health-Chatbot-Gemma-2 | bilalRahib | 2024-03-02T08:37:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T08:37:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** bilalRahib
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma 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)
|
prithivida/bge-base-en-v1.5-gguf | prithivida | 2024-03-02T08:37:47Z | 33 | 1 | transformers | [
"transformers",
"gguf",
"bert",
"BlitzEmbed",
"Embedding",
"Retriever",
"en",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:quantized:BAAI/bge-small-en-v1.5",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-02-23T16:10:14Z | ---
tags:
- BlitzEmbed
- Embedding
- Retriever
base_model: BAAI/bge-small-en-v1.5
language:
- en
license: mit
model_creator: BAAI
model_name: bge-small-en-v1.5
model_type: bert
quantized_by: Prithivida
---
|
ThuyNT03/CS505_COQE_viT5_Prompting5_PSAOL | ThuyNT03 | 2024-03-02T08:16:48Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T07:14:05Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_PSAOL
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. -->
# CS505_COQE_viT5_Prompting5_PSAOL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
ThuyNT03/CS505_COQE_viT5_Prompting5_SOPAL | ThuyNT03 | 2024-03-02T08:09:20Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T06:58:12Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_SOPAL_vtune_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CS505_COQE_viT5_Prompting5_SOPAL_vtune_2
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
ThuyNT03/CS505_COQE_viT5_Prompting5_OAPSL | ThuyNT03 | 2024-03-02T08:09:11Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T07:07:18Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting5_OAPSL
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. -->
# CS505_COQE_viT5_Prompting5_OAPSL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
bilalRahib/Mental-Health-Chatbot-Gemma | bilalRahib | 2024-03-02T08:02:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T08:02:39Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** bilalRahib
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma 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)
|
Chhabi/testing1.1-llama2-nepali-health-model | Chhabi | 2024-03-02T07:49:06Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T07:22:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
alraisi/no-sum-t5-3 | alraisi | 2024-03-02T07:44:48Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-02T07:31:35Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: no-sum-t5-3
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. -->
# no-sum-t5-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1815
- Rouge1: 33.2039
- Rouge2: 12.1054
- Rougel: 32.7253
- Rougelsum: 32.7032
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 139 | 1.6711 | 22.764 | 3.4862 | 22.3717 | 22.3978 | 19.0 |
| No log | 2.0 | 278 | 1.3811 | 31.9748 | 10.5421 | 31.4438 | 31.4451 | 19.0 |
| No log | 3.0 | 417 | 1.2981 | 32.0918 | 11.2326 | 31.4844 | 31.5121 | 19.0 |
| 1.9326 | 4.0 | 556 | 1.2542 | 32.0848 | 11.1865 | 31.6965 | 31.669 | 19.0 |
| 1.9326 | 5.0 | 695 | 1.2250 | 32.6516 | 11.7983 | 32.117 | 32.1061 | 19.0 |
| 1.9326 | 6.0 | 834 | 1.2063 | 33.0203 | 11.973 | 32.5571 | 32.5407 | 19.0 |
| 1.9326 | 7.0 | 973 | 1.1932 | 33.1756 | 11.7729 | 32.5844 | 32.6138 | 19.0 |
| 1.3969 | 8.0 | 1112 | 1.1886 | 33.0571 | 11.792 | 32.5059 | 32.4676 | 19.0 |
| 1.3969 | 9.0 | 1251 | 1.1825 | 33.2126 | 12.1217 | 32.6524 | 32.6517 | 19.0 |
| 1.3969 | 10.0 | 1390 | 1.1815 | 33.2039 | 12.1054 | 32.7253 | 32.7032 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
CatBarks/GPT2ES_spamming-email-classification4_1_model | CatBarks | 2024-03-02T07:40:52Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-02T07:39:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[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]
|
dyumat/mistral-7b-chat-pdf | dyumat | 2024-03-02T07:26:06Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T07:20:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
|
Locutusque/hyperion-medium-preview | Locutusque | 2024-03-02T07:18:37Z | 110 | 5 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-24T04:31:16Z | ---
language:
- en
license: apache-2.0
library_name: transformers
model-index:
- name: hyperion-medium-preview
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 60.67
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 83.67
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.73
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 42.93
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 40.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/hyperion-medium-preview
name: Open LLM Leaderboard
---
# Model Card for M4-ai/hyperion-medium-preview
## Model Details
**Model Name**: M4-ai/hyperion-medium-preview
**Base Model**: mistralai/Mistral-7B-v0.1
**Publisher**: M4-ai
**Model Type**: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.
**Language**: Multi-domain, English language.
**License**: Apache-2.0
## Model Description
`M4-ai/hyperion-medium-preview` is a state-of-the-art language model fine-tuned on the Hyperion dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.
## Intended Use
This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:
- AI-driven tutoring systems for science, medicine, mathematics, and computer science.
- Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
- Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
- Automation in code generation and understanding complex programming context.
## Training Data
The `M4-ai/hyperion-medium-preview` model was fine-tuned on the Hyperion dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.
## Evaluation Results
Coming soon...
## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "M4-ai/hyperion-medium-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate a response
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Known Limitations
The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.
## Licensing Information
This model is released under the Apache-2.0 license.
## Citation Information
If you use M4-ai/hyperion-medium-preview in your research, please cite the Hyperion dataset as follows:
```
@misc{sebastian_gabarain_2024,
title = {Hyperion-1: Illuminating the Path to Advanced Reasoning with a High-Quality, Multidisciplinary Question Answering Dataset},
author = {Sebastian Gabarain},
publisher = {HuggingFace},
year = {2024},
url = {https://huggingface.co/datasets/Locutusque/hyperion-v1.0}
}
```
## Quants
- exl2 by bartowski: https://huggingface.co/bartowski/hyperion-medium-preview-exl2
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__hyperion-medium-preview)
| Metric |Value|
|---------------------------------|----:|
|Avg. |61.67|
|AI2 Reasoning Challenge (25-Shot)|60.67|
|HellaSwag (10-Shot) |83.67|
|MMLU (5-Shot) |63.73|
|TruthfulQA (0-shot) |42.93|
|Winogrande (5-shot) |78.53|
|GSM8k (5-shot) |40.49|
|
bilalRahib/Tiny-LLama-NSFW-Chatbot | bilalRahib | 2024-03-02T07:13:31Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T06:46:07Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-bnb-4bit
---
# Uploaded model
- **Developed by:** bilalRahib
- **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)
|
mesolitica/malaysian-mistral-7b-32k-instructions-v5 | mesolitica | 2024-03-02T07:13:26Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-28T09:35:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
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#### 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. -->
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<!-- 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. -->
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Glossary [optional]
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## Model Card Contact
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|
atgarcia/wav2vec2part3 | atgarcia | 2024-03-02T07:00:32Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-02-26T10:08:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- 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. -->
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
VikrantRamesh/gpt2-CN | VikrantRamesh | 2024-03-02T06:52:33Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T06:50:43Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-CN
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. -->
# gpt2-CN
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 8.1142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 46 | 9.0245 |
| No log | 2.0 | 92 | 8.3165 |
| No log | 3.0 | 138 | 8.1142 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3
|
100rab25/swin-tiny-patch4-window7-224-hotel_images_classifier_v2 | 100rab25 | 2024-03-02T06:51:07Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-02T06:03:51Z | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-hotel_images_classifier_v2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9518218623481781
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-hotel_images_classifier_v2
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1259
- Accuracy: 0.9518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2929 | 1.0 | 694 | 0.1688 | 0.9386 |
| 0.2499 | 2.0 | 1389 | 0.1516 | 0.9425 |
| 0.1633 | 3.0 | 2084 | 0.1373 | 0.9488 |
| 0.1943 | 4.0 | 2779 | 0.1319 | 0.9510 |
| 0.2138 | 4.99 | 3470 | 0.1259 | 0.9518 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
dhanushreddy29/gemma-7b-sft-hindi-16bit | dhanushreddy29 | 2024-03-02T06:45:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-02T06:38:40Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- sft
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** dhanushreddy29
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
dhanushreddy29/gemma-7b-sft-hindi-lora-adapter | dhanushreddy29 | 2024-03-02T06:31:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T06:31:30Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** dhanushreddy29
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
dhanushreddy29/gemma-7b-sft-hindi | dhanushreddy29 | 2024-03-02T06:28:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-02T06:28:20Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** dhanushreddy29
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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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Jayanthini/NeuralHermes-2.5-Mistral-7B | Jayanthini | 2024-03-02T06:28:15Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-02T06:25:10Z | ---
library_name: transformers
tags: []
---
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