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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
emilstabil/DanSumT5-smallV_55565 | emilstabil | 2023-11-29T20:32:18Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:32:18Z | 2023-11-29T19:20:51.000Z | null | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-smallV_55565
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. -->
# DanSumT5-smallV_55565
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-small](https://huggingface.co/Danish-summarisation/DanSumT5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6152
- Rouge1: 33.2076
- Rouge2: 9.7687
- Rougel: 19.2885
- Rougelsum: 30.5358
- Gen Len: 125.4515
## 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: 80
- eval_batch_size: 80
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 320
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 6 | 2.7915 | 28.1925 | 6.0798 | 16.0171 | 25.4406 | 117.8734 |
| No log | 2.0 | 12 | 2.7309 | 30.7001 | 7.8441 | 17.6277 | 28.1658 | 123.7384 |
| No log | 3.0 | 18 | 2.6932 | 31.9139 | 8.8623 | 18.4491 | 29.2043 | 125.2321 |
| No log | 4.0 | 24 | 2.6673 | 32.1541 | 9.2757 | 18.7349 | 29.3827 | 125.1941 |
| No log | 5.0 | 30 | 2.6506 | 32.6317 | 9.6369 | 18.9798 | 30.0012 | 125.6034 |
| No log | 6.0 | 36 | 2.6391 | 32.7076 | 9.7264 | 18.9488 | 29.9797 | 125.3376 |
| No log | 7.0 | 42 | 2.6307 | 32.9958 | 9.8324 | 19.0395 | 30.2766 | 125.0 |
| No log | 8.0 | 48 | 2.6241 | 33.2035 | 9.9866 | 19.1625 | 30.5136 | 125.2321 |
| No log | 9.0 | 54 | 2.6190 | 33.4626 | 10.076 | 19.2999 | 30.6955 | 125.4515 |
| No log | 10.0 | 60 | 2.6161 | 33.3145 | 9.9106 | 19.3186 | 30.6521 | 125.4515 |
| No log | 11.0 | 66 | 2.6152 | 33.2076 | 9.7687 | 19.2885 | 30.5358 | 125.4515 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1+git7548e2f
- Datasets 2.13.2
- Tokenizers 0.13.3
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68611-llm-annotation-group/BioLinkBERT-base-NER-JNLPBA-HA | 68611-llm-annotation-group | 2023-11-29T19:21:25Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:21:25Z | 2023-11-29T19:21:03.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | 68611-llm-annotation-group/BioLinkBERT-base-NER-JNLPBA-HA | [
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zichao22/prompt_program_raw_500_epochs | zichao22 | 2023-11-29T19:43:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:43:06Z | 2023-11-29T19:21:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | zichao22/prompt_program_raw_500_epochs | [
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younghoonKIM/jm | younghoonKIM | 2023-11-29T19:22:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:22:00Z | 2023-11-29T19:22:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | younghoonKIM/jm | [
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Daunil/Orion3 | Daunil | 2023-11-29T19:27:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T19:27:05Z | 2023-11-29T19:23:56.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Daunil/Orion3 | [
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] |
thingthatis/deepseek-llm-7B-chat-GGUF | thingthatis | 2023-11-29T19:24:40Z | 0 | 0 | null | [
"transformers",
"gguf",
"deepseek",
"base_model:deepseek-ai/deepseek-llm-7b-chat",
"license:other",
"region:us"
] | 2023-11-29T19:24:40Z | 2023-11-29T19:24:40.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-7b-chat
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek LLM 7B Chat
model_type: deepseek
prompt_template: 'User: {prompt}
Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek LLM 7B Chat - GGUF
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)
<!-- description start -->
## Description
This repo contains GGUF format model files for [DeepSeek's Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### 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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: DeepSeek-LLM
```
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [deepseek-llm-7b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q2_K.gguf) | Q2_K | 2 | 2.99 GB| 5.49 GB | smallest, significant quality loss - not recommended for most purposes |
| [deepseek-llm-7b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 3.14 GB| 5.64 GB | very small, high quality loss |
| [deepseek-llm-7b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.47 GB| 5.97 GB | very small, high quality loss |
| [deepseek-llm-7b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.75 GB| 6.25 GB | small, substantial quality loss |
| [deepseek-llm-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_0.gguf) | Q4_0 | 4 | 4.00 GB| 6.50 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [deepseek-llm-7b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 4.03 GB| 6.53 GB | small, greater quality loss |
| [deepseek-llm-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.22 GB| 6.72 GB | medium, balanced quality - recommended |
| [deepseek-llm-7b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_0.gguf) | Q5_0 | 5 | 4.81 GB| 7.31 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [deepseek-llm-7b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 4.81 GB| 7.31 GB | large, low quality loss - recommended |
| [deepseek-llm-7b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 4.93 GB| 7.43 GB | large, very low quality loss - recommended |
| [deepseek-llm-7b-chat.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q6_K.gguf) | Q6_K | 6 | 5.67 GB| 8.17 GB | very large, extremely low quality loss |
| [deepseek-llm-7b-chat.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q8_0.gguf) | Q8_0 | 8 | 7.35 GB| 9.85 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## 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: TheBloke/deepseek-llm-7B-chat-GGUF and below it, a specific filename to download, such as: deepseek-llm-7b-chat.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 TheBloke/deepseek-llm-7B-chat-GGUF deepseek-llm-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<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 TheBloke/deepseek-llm-7B-chat-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 TheBloke/deepseek-llm-7B-chat-GGUF deepseek-llm-7b-chat.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>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## 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 deepseek-llm-7b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "User: {prompt}\n\nAssistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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="./deepseek-llm-7b-chat.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"User: {prompt}\n\nAssistant:", # 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="./deepseek-llm-7b-chat.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)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: DeepSeek's Deepseek LLM 7B Chat
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
<!-- original-model-card end -->
| null | transformers | null | null | null | null | null | null | null | null | null | null | thingthatis/deepseek-llm-7B-chat-GGUF | [
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emilstabil/DanSumT5-baseV_35588 | emilstabil | 2023-11-29T19:24:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:24:59Z | 2023-11-29T19:24:59.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-baseV_35588 | [
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow19 | FounderOfHuggingface | 2023-11-29T19:26:25Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T19:26:25Z | 2023-11-29T19:26:22.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
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K-kiron/distilbert-lr-cosine-with-restarts | K-kiron | 2023-11-30T00:55:39Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:55:39Z | 2023-11-29T19:26:29.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-lr-cosine-with-restarts
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-lr-cosine-with-restarts
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: 0.2952
- Accuracy: 0.8947
- F1: 0.8950
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
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ski20/climate-ft | ski20 | 2023-11-29T19:45:58Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T19:45:58Z | 2023-11-29T19:26:49.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ski20/climate-ft | [
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emilstabil/DanSumT5-largeV_4991 | emilstabil | 2023-11-29T19:27:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:27:51Z | 2023-11-29T19:27:51.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_4991 | [
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zichao22/prompt_program_raw_50_epochs | zichao22 | 2023-11-29T19:33:23Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Python-hf",
"license:llama2",
"region:us"
] | 2023-11-29T19:33:23Z | 2023-11-29T19:29:19.000Z | null | null | ---
license: llama2
base_model: codellama/CodeLlama-7b-Python-hf
tags:
- generated_from_trainer
model-index:
- name: prompt_program_raw_50_epochs
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. -->
# prompt_program_raw_50_epochs
This model is a fine-tuned version of [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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danielmartinezar/IA_REPO | danielmartinezar | 2023-11-29T19:29:47Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:29:47Z | 2023-11-29T19:29:47.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | danielmartinezar/IA_REPO | [
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Araeynn/r | Araeynn | 2023-11-29T19:30:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:30:43Z | 2023-11-29T19:30:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Araeynn/r | [
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MaryRead/distilbert-base-uncased-finetuned-cola | MaryRead | 2023-11-29T19:30:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:30:43Z | 2023-11-29T19:30:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | MaryRead/distilbert-base-uncased-finetuned-cola | [
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CLMBR/preemption_testing-full-transformer-22 | CLMBR | 2023-11-29T19:30:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:30:52Z | 2023-11-29T19:30:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | CLMBR/preemption_testing-full-transformer-22 | [
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rthgrtyfgghjjtt/Bus-carrying-newly-released-Palestinian-prisoners-arrives-in-West-Bank-city-bg-updated | rthgrtyfgghjjtt | 2023-11-29T19:31:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:31:12Z | 2023-11-29T19:31:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Bus-carrying-newly-released-Palestinian-prisoners-arrives-in-West-Bank-city-bg-updated | [
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EthanRhys/Mario | EthanRhys | 2023-11-29T19:32:50Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T19:32:50Z | 2023-11-29T19:31:17.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | EthanRhys/Mario | [
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Araeynn/gpt2-alpaca | Araeynn | 2023-11-29T19:31:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:31:29Z | 2023-11-29T19:31:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Araeynn/gpt2-alpaca | [
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68611-llm-annotation-group/BioLinkBERT-base-NER-BC5CDR_D-HA | 68611-llm-annotation-group | 2023-11-29T19:31:54Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:31:54Z | 2023-11-29T19:31:32.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | 68611-llm-annotation-group/BioLinkBERT-base-NER-BC5CDR_D-HA | [
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] |
kataragi/SDXL-turbo-LoRA | kataragi | 2023-11-29T19:43:36Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T19:43:36Z | 2023-11-29T19:32:15.000Z | null | null | ---
license: creativeml-openrail-m
---
</p>
概要
sdxl turbo LoRAのrankをリサイズして容量を軽量化したバージョンの一群です。
サンプル画像を見てお好きなランクをご利用ください。
サンプルイメージ
SDXLでの出力
animagine-XLV2での出力
</p> | null | null | null | null | null | null | null | null | null | null | null | null | kataragi/SDXL-turbo-LoRA | [
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CLMBR/preemption_testing-full-transformer-0 | CLMBR | 2023-11-29T19:32:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:32:22Z | 2023-11-29T19:32:22.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | CLMBR/preemption_testing-full-transformer-0 | [
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BENBENBENb/finetune_arc_1 | BENBENBENb | 2023-11-29T19:36:55Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | 2023-11-29T19:36:55Z | 2023-11-29T19:33:06.000Z | null | null | ---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: finetune_arc_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune_arc_1
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2817
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.341 | 1.0 | 150 | 1.2817 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | BENBENBENb/finetune_arc_1 | [
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mghiasvandm/aste-rest14-base-agents | mghiasvandm | 2023-11-29T19:33:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T19:33:45Z | 2023-11-29T19:33:09.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/aste-rest14-base-agents | [
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] | 2023-11-29T19:33:14Z | 2023-11-29T19:33:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_84482 | [
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owanr/ghc-roberta-large-intra-sorted-model_annots-cross-ent | owanr | 2023-11-29T19:34:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:34:27Z | 2023-11-29T19:34:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | owanr/ghc-roberta-large-intra-sorted-model_annots-cross-ent | [
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avazquezac/customer_support_test | avazquezac | 2023-11-29T19:34:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:34:29Z | 2023-11-29T19:34:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | avazquezac/customer_support_test | [
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow20 | FounderOfHuggingface | 2023-11-29T19:34:53Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T19:34:53Z | 2023-11-29T19:34:49.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.6.2
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rthgrtyfgghjjtt/Maine-could-soon-reach-another-settlement-on-legal-defense-for-its-poorest-residents-cc-updated | rthgrtyfgghjjtt | 2023-11-29T19:37:20Z | 0 | 0 | null | [
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(The Center Square) - In the first half of 2023, roughly 32,000 babies were born in states that implemented abortion restrictions after the U.S. Supreme Court overturned Roe v. Wade last June, a 2.3% increase, according to a new analysis.
In the first six months of 2023, "births rose by an average of 2.3 percent in states enforcing total abortion bans," leading to an estimated 32,000 births that might have otherwise been aborted, according to a new analysis published by the IZA Institute of Labor Economics initiated by the Deutsche Post Foundation.
"These effects may vary across demographic groups and tend to be larger for younger women and women of color; ... vary substantially across ban states, with much larger effects observed in states that are bordered by other ban states and hence have long travel distances to reach facilities that remain open."
Its November 2023 "Effects of the Dobbs Decision on Fertility" report states that the "U.S. Supreme Court decision in Dobbs v. Jackson Women's Health Organization sparked the most profound transformation of the landscape of abortion access in 50 years. We provide the first estimates of the effects of this decision on fertility using a preregistered synthetic difference-in-differences design applied to newly released provisional natality data for the first half of 2023."
The analysis is based on provisional data for the first six months of 2023. "If future research using finalized data and additional policy variation reveals continued substantial effects on birth, then we expect long-lasting and profound effects on the lives of affected pregnant people and their families, including effects on educational investment, employment, earnings, and financial security."
As of Nov. 1, 2023, 14 states are enforcing bans on abortion in nearly all circumstances, the report notes. Because roughly 23% of American women seeking an abortion experienced an increase in driving distance to the nearest abortion facility (from 43 miles before Dobbs to 330 miles after Dobbs), the driving distance "represents the most profound transformation of the landscape of U.S. abortion access in 50 years."
According to a different study by researchers from Johns Hopkins Bloomberg School of Public Health, nearly as many babies are believed to have been born in Texas alone since its new heartbeat bill went into effect Sept. 1, 2021.
Within eight months of the new law going into effect, there were nearly 9,800 live births in Texas from April to December 2022, according to the Johns Hopkins study. If the rate were consistent through November 2023, of an additional 1,225 live births a month, the number of babies born in Texas that otherwise might have been aborted is closer to nearly 32,000 since Sept. 1, 2021.
Suzanne Bell, a lead author of Johns Hopkins study, said their "findings highlight how abortion bans have real implications for birthing people, thousands of whom may have had no choice but to continue an unwanted or unsafe pregnancy to term. Notably, the majority of people who seek abortions live below or close to the poverty line. So many of these birthing people and their families were likely struggling financially even before the recent birth."
State Sen. Brian Hughes, R-Mineola, who authored Texas' heartbeat bill, told The Center Square, "Each of these lives is a gift of God and reflects His image. And since passage of the Heartbeat Act, we have drastically increased funding for expectant and new mothers and their babies.
"In Texas, we are proving that we can save the life of the baby while we love, and respect, and support the mother."
In addition to signing the state's first heartbeat bill into law, Gov. Greg Abbott signed bills into law extending Medicaid health-care coverage to 12 months post-partum, appropriated more than $447 million for women's health programs and invested over $140 million in the Thriving Texas Families program.
Prior to Roe being overturned, "In 2020, approximately 1 in 5 pregnancies ended in abortion," the IZA study states, noting that the majority of those seeking abortions, 75%, were low-income. Another 59% said they had previously given birth and 55% reported some kind of hardship including falling behind on rent or losing a job.
Hughes' bill, SB 8, passed the Texas legislature with bipartisan support and was signed into law in May 2021. By October 2021, a federal judge halted it. By April 2022, the Fifth Circuit overturned that ruling and ended all challenges to the law. After the Supreme Court overturned Roe v Wade in June 2022, Texas' law went into full effect in August 2022.
Texas' law is considered to be among the strictest in the nation. It bans abortions from being performed in Texas as soon as a heartbeat of the preborn baby is detected, with limited exceptions. It created a second-degree felony offense for a person who knowingly performs, induces or attempts an abortion. The offense is enhanced to a first-degree felony if an unborn child dies from an abortion. Anyone who violates the law performing an abortion can also be subject to a minimum civil penalty of $100,000 for each violation, with exceptions.
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staticai/ZroAI-Hermes | staticai | 2023-11-29T19:36:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:36:17Z | 2023-11-29T19:36:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | staticai/ZroAI-Hermes | [
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emilstabil/DanSumT5-largeV_14047 | emilstabil | 2023-11-29T19:36:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:36:27Z | 2023-11-29T19:36:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_14047 | [
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emilstabil/DanSumT5-largeV_57227 | emilstabil | 2023-11-29T19:39:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:39:15Z | 2023-11-29T19:39:15.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_57227 | [
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rthgrtyfgghjjtt/Pet-owners-warned-to-avoid-dog-parks-over-fears-of-new-virus-Internewscast-Journal-d4-updated | rthgrtyfgghjjtt | 2023-11-29T19:41:47Z | 0 | 0 | null | [
"en",
"region:us"
] | 2023-11-29T19:41:47Z | 2023-11-29T19:40:22.000Z | null | null | ---
language:
- en
---
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https://paste.feed-the-beast.com/view/553355ab
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https://yamcode.com/raw/biden-says-wants-update-11292023-202204
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https://pastebin.com/RnG10F3Z
https://yamcode.com/raw/biden-says-wants-update-11292023-202529
https://yamcode.com/hilary-says-wants-update-2352-11292023-201747
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DONELSON, Tenn. (WTVF) -- Some of the busiest travel days of the year are behind us, but the traffic around BNA is here to stay.
Over the holiday weekend, people were getting out of cars and walking on the interstate to make their flights. And this isn't the first time that's happened.
In the video above, see passengers bailing the backups and rolling their luggage along the interstate.
"To have that many people walking, and just the fact that people don't expect the traffic to come to a halt that far out. We've kind of gotten lucky there haven't been any serious accidents or injuries related to this," said Russ Bradford, council member for the neighborhood that surrounds the airport.
For the most part, the traffic stems from the ongoing projects at the airport that started back in 2017. The construction isn't expected to wrap up until 2028. It includes major work on Donelson Pike. There, crews are working around the clock to shift Donelson Pike east to allow the airport to double its current loop: from one mile long to two, and from three lanes to six.
The construction on Donelson Pike required the airport to move waiting areas next to the terminal to a lot three miles away. While it's used by some people, most people just circle the airport.
"We've got to get TDOT involved, whether it's having them getting on to the airport about this or maybe TDOT and Highway Patrol needs to get out there when it gets this busy, and do special signage or have officers out there to help with the flow of traffic. Something has to be done to make this better for the next 4 years," Bradford said.
Most law enforcement agencies have limited resources. But Metro Police said if the airport requested their presence during a busy travel time, they'd do their best to help with traffic enforcement.
People wjth a concern about the situation can send an email to TDOT.Comments@TN.gov.
We reached out to BNA and received the following statement.
Nashville International Airport® (BNA®) remains steadfast in its commitment to the safety and security of our passengers. For passenger safety, we encourage passengers to remain in their vehicles on major roadways or thoroughfares. Nashville International Airport has three entrances: exit 216A, exit 216B, and access via Murfreesboro Pike to Donelson Pike. Passengers should choose an exit based on traffic conditions at that time. Additionally, for flight departures, passengers should arrive at the airport at least two hours prior to their scheduled flight departure time. Always remember to check your flight status prior to heading to the airport.
For arrivals, drivers should plan to arrive at the airport 15-20 minutes after the scheduled landing time to allow the passenger time to deplane, retrieve their belongings, and reach their designated pickup location. Drivers arriving earlier than necessary or idling on the curbs for an extended period of time, add to the congestion at the airport and should consider using the Cell Phone Lot conveniently located just 3 miles from the airport at 1415 Murfreesboro Pike.
As part of our commitment to serving the growth of Middle Tennessee, both the Tennessee Department of Transportation and the Airport Authority are working to increase roadway capacity.
Get NewsChannel 5 Now, wherever, whenever, always free. Watch the live stream below, and download our apps on Roku, Apple TV, Amazon Fire TV and more. Click here to learn more..... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Pet-owners-warned-to-avoid-dog-parks-over-fears-of-new-virus-Internewscast-Journal-d4-updated | [
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MNQuinn/Reinforce-pixelcopter-1 | MNQuinn | 2023-11-29T19:41:16Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T19:41:16Z | 2023-11-29T19:41:12.000Z | null | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 16.80 +/- 14.74
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | MNQuinn/Reinforce-pixelcopter-1 | [
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68611-llm-annotation-group/BioLinkBERT-base-NER-BC5CDR_C-HA | 68611-llm-annotation-group | 2023-11-29T19:42:23Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:42:23Z | 2023-11-29T19:42:02.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | 68611-llm-annotation-group/BioLinkBERT-base-NER-BC5CDR_C-HA | [
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thingthatis/deepseek-llm-7B-base-GGUF | thingthatis | 2023-11-29T19:42:10Z | 0 | 0 | null | [
"transformers",
"gguf",
"deepseek",
"base_model:deepseek-ai/deepseek-llm-7b-base",
"license:other",
"region:us"
] | 2023-11-29T19:42:10Z | 2023-11-29T19:42:10.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-7b-base
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek LLM 7B Base
model_type: deepseek
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek LLM 7B Base - GGUF
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
<!-- description start -->
## Description
This repo contains GGUF format model files for [DeepSeek's Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### 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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [deepseek-llm-7b-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q2_K.gguf) | Q2_K | 2 | 2.99 GB| 5.49 GB | smallest, significant quality loss - not recommended for most purposes |
| [deepseek-llm-7b-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_S.gguf) | Q3_K_S | 3 | 3.14 GB| 5.64 GB | very small, high quality loss |
| [deepseek-llm-7b-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_M.gguf) | Q3_K_M | 3 | 3.47 GB| 5.97 GB | very small, high quality loss |
| [deepseek-llm-7b-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_L.gguf) | Q3_K_L | 3 | 3.75 GB| 6.25 GB | small, substantial quality loss |
| [deepseek-llm-7b-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_0.gguf) | Q4_0 | 4 | 4.00 GB| 6.50 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [deepseek-llm-7b-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_K_S.gguf) | Q4_K_S | 4 | 4.03 GB| 6.53 GB | small, greater quality loss |
| [deepseek-llm-7b-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_K_M.gguf) | Q4_K_M | 4 | 4.22 GB| 6.72 GB | medium, balanced quality - recommended |
| [deepseek-llm-7b-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_0.gguf) | Q5_0 | 5 | 4.81 GB| 7.31 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [deepseek-llm-7b-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_K_S.gguf) | Q5_K_S | 5 | 4.81 GB| 7.31 GB | large, low quality loss - recommended |
| [deepseek-llm-7b-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_K_M.gguf) | Q5_K_M | 5 | 4.93 GB| 7.43 GB | large, very low quality loss - recommended |
| [deepseek-llm-7b-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q6_K.gguf) | Q6_K | 6 | 5.67 GB| 8.17 GB | very large, extremely low quality loss |
| [deepseek-llm-7b-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q8_0.gguf) | Q8_0 | 8 | 7.35 GB| 9.85 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## 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: TheBloke/deepseek-llm-7B-base-GGUF and below it, a specific filename to download, such as: deepseek-llm-7b-base.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 TheBloke/deepseek-llm-7B-base-GGUF deepseek-llm-7b-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<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 TheBloke/deepseek-llm-7B-base-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 TheBloke/deepseek-llm-7B-base-GGUF deepseek-llm-7b-base.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>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## 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 deepseek-llm-7b-base.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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="./deepseek-llm-7b-base.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"{prompt}", # 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="./deepseek-llm-7b-base.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)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: DeepSeek's Deepseek LLM 7B Base
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-7b-base` is a 7B parameter model with Multi-Head Attention trained on 2 trillion tokens from scratch.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
<!-- original-model-card end -->
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emilstabil/DanSumT5-largeV_91364 | emilstabil | 2023-11-29T19:43:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:43:28Z | 2023-11-29T19:43:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_91364 | [
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CLMBR/preemption_testing-full-transformer-32 | CLMBR | 2023-11-29T19:45:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:45:18Z | 2023-11-29T19:45:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | CLMBR/preemption_testing-full-transformer-32 | [
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rthgrtyfgghjjtt/Argentinas-presidentelect-tells-top-Biden-officials-that-hes-committed-to-freedom-dh-updated | rthgrtyfgghjjtt | 2023-11-29T19:48:24Z | 0 | 0 | null | [
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] | 2023-11-29T19:48:24Z | 2023-11-29T19:46:51.000Z | null | null | ---
language:
- en
---
[]()
read the full article here : https://rentry.co/smsrm
Source : https://yamcode.com/biden-says-wants-update-11292023-203329
Flash News : https://yamcode.com/trump-says-wants-update-11292023-201356
Biden last Talk : https://yamcode.com/
Russian Ukrain Breaking News : https://yamcode.com/hilary-says-wants-update-4453-11292023-203836
Other Sources :
https://pastebin.com/5XmEDgdR
https://yamcode.com/raw/trump-says-wants-update-11292023-201837
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https://jsbin.com/jacebomeda/edit?html,output
https://rentry.co/tth4e
https://ibit.ly/LzhCD
https://jpeg.ly/-wYh2
https://yamcode.com/obama-says-wants-update-3531-34
https://ibit.ly/vhlqq
https://etextpad.com/5otzeuz6bc
https://jpeg.ly/IKNVi
https://jsitor.com/RqU7402X6k
https://yamcode.com/raw/hilary-says-wants-update-4453-11292023-203836
https://yamcode.com/raw/hilary-says-wants-update-5335-11292023-203605
DOG owners across the country have been warned to limit their pet's contact with other dogs over fears of a new deadly illness.
Colorado, California, Florida, Washington, Pennsylvania and New Hampshire are among states that have seen cases of the mysterious new air-borne respiratory illness rise since early November.
The Oregon Department of Agriculture has received at least 200 reports of the dog flu alone.
It is said to cause lasting respiratory disease and pneumonia and does not respond to antibiotics, as reported by Fox affiliate KNWA.
In some dogs, the illness has been fatal, but it seems to only be contagious between dogs and does not seem to be passed from dogs to humans.
The illness is spread through animal-to-animal contact with symptoms consisting of a four to six week cough.
The cough sounds like a goose honking, said Dr. Kate Williams, a veterinarian with St. Francis Animal Hospital in Springdale, Arkansas, which has not reported any cases of the illness.
Experts, however, are conflicted over whether the illness is caused by a virus or bacteria.
In some dogs, experts say the cough turns into pneumonia which is what causes the animal to struggle to breathe.
"It seems to happen very, very quickly -- to go from this cough that's just won't go away... and then all of a sudden they develop this pneumonia," Dr. Lindsey Ganzer, veterinarian and CEO at North Springs Veterinary Referral Center in Colorado Springs, Colorado, told Today.com.
Owners are now being urged to avoid any facilities where their dogs could come into contact with other pooches.
"Boarding facilities, dog parks, doggy daycare, I would avoid all those right now," Registered Veterinary Technician Liz Talkingto said.
She added that they're currently keeping an eye on a "suspicious" case of an ill dog at their clinic in Overland Park, Kansas.
Talkington said she can't confirm yet whether the dog, named Pepper, is ill with the new strain of the upper respiratory illness, according to NBC affiliate KSN News.
Pepper had spent the last day being treated with high-flow oxygen and antibiotics after several days of having episodes of breathing difficulties.
Talkington said it will be another 24 hours before they will know how the dog is responding to treatment.
"I wouldn't want to lose her," Jennifer Dykstra, Pepper's owner, said.
According to Dr Kate Williams, te best protection for your dog is to ensure they are fully vaccinated.
"If not, call your veterinarian, schedule an appointment and update their vaccinations. If your dog does show any respiratory illness, isolate the animal to your home and contact your veterinarian," Williams said..... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Argentinas-presidentelect-tells-top-Biden-officials-that-hes-committed-to-freedom-dh-updated | [
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gianyrox/TrainingNov29-5Checkpoints | gianyrox | 2023-11-29T20:50:22Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T20:50:22Z | 2023-11-29T19:46:52.000Z | null | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of Dr. Seuss's Lorax
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - gianyrox/TrainingNov29-5Checkpoints
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of Dr. Seuss's Lorax using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | gianyrox/TrainingNov29-5Checkpoints | [
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rthgrtyfgghjjtt/Full-Story-45-updated | rthgrtyfgghjjtt | 2023-11-29T19:49:12Z | 0 | 0 | null | [
"en",
"region:us"
] | 2023-11-29T19:49:12Z | 2023-11-29T19:47:42.000Z | null | null | ---
language:
- en
---
[]()
read the full article here : https://jsbin.com/yuxutibodi/edit?html,output
Source : https://ibit.ly/oLgWu
Flash News : https://rentry.co/i3cp5
Biden last Talk : https://yamcode.com/raw/obama-says-wants-update-2115-28
Russian Ukrain Breaking News : https://yamcode.com/raw/china-president-says-wants-update-19855
Other Sources :
https://ibit.ly/xQbxm
https://jpeg.ly/3fxWN
https://jpeg.ly/R9GzH
https://ibit.ly/QOQ3a
https://jpeg.ly/TKzGA
https://yamcode.com/raw/biden-says-wants-update-11292023-203329
https://ibit.ly/NNf2G
https://rentry.co/cmnwz
https://jpeg.ly/NL6tJ
https://jsitor.com/u1o-fA689t
https://jsbin.com/kilorizemo/edit?html,output
https://yamcode.com/hilary-says-wants-update-3342-11292023-203631
https://yamcode.com/biden-says-wants-update-11292023-203120
https://jpeg.ly/wf3t2
Donald Trump was so depressed after his supporters attempted a coup on Jan. 6 failed that he stopped eating -- or so says Kevin McCarthy, according to a new book by former Republican Rep. Liz Cheney.
Cheney writes in Oath and Honor -- which was exclusively reviewed by CNN -- that she questioned the former House Speaker about a trip he made to Trump's Mar-a-Lago resort in the aftermath of the Capitol riot, a time when Republicans were still willing to condemn Trump's role in the violence that took place on Jan. 6.
McCarthy, who was critical of Trump around the time of the riot, allegedly told Cheney that he had gone because people around Trump were "really worried."
"Trump's not eating, so they asked me to come see him," McCarthy said, according to Cheney.
Cheney was incredulous. "What? You went to Mar-a-Lago because Trump's not eating?" she asked McCarthy, who reiterated that Trump was "really depressed."
In another anecdote, Cheney described how McCarthy had said that Trump was aware that he had lost only two days after the election. McCarthy reportedly told Cheney that Trump "knows it's over, he just needs to go through all the stages of grief."
How did those stages of grief manifest? Partially through the former president helping incite a violent mob that broke into the Capitol in an attempt to disrupt the peaceful transfer of power, harm lawmakers, and hang the vice president. Cheney was one of two Republican lawmakers on the House committee that investigated the attack, a role that alienated her from the rest of the party and lose her seat in Congress.
In her new book, Cheney describes many of her former Republican colleagues as "enablers and collaborators ... willing to violate their oath to the Constitution out of political expediency and loyalty to Donald Trump."
"We have also now learned that most Republicans currently in Congress will do what Donald Trump asks, no matter what it is," Cheney writes. "I am very sad to say that America can no longer count on a body of elected Republicans to protect our republic.".... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Full-Story-45-updated | [
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68611-llm-annotation-group/BioLinkBERT-base-NER-BC2GM-HA | 68611-llm-annotation-group | 2023-11-29T19:48:21Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:48:21Z | 2023-11-29T19:48:00.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | 68611-llm-annotation-group/BioLinkBERT-base-NER-BC2GM-HA | [
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jdp8/stable-diffusion-1-5-openpose-v11p-onnx | jdp8 | 2023-11-29T19:59:18Z | 0 | 0 | null | [
"onnx",
"region:us"
] | 2023-11-29T19:59:18Z | 2023-11-29T19:48:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jdp8/stable-diffusion-1-5-openpose-v11p-onnx | [
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Mlxa/tuned-english-flat_shuffle | Mlxa | 2023-11-29T19:49:36Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:49:36Z | 2023-11-29T19:49:34.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mlxa/tuned-english-flat_shuffle | [
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BENBENBENb/finetune_arc_20 | BENBENBENb | 2023-11-29T21:02:48Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | 2023-11-29T21:02:48Z | 2023-11-29T19:49:50.000Z | null | null | ---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: finetune_arc_20
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. -->
# finetune_arc_20
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7783
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3635 | 1.0 | 150 | 1.2844 |
| 0.8018 | 2.0 | 300 | 1.3583 |
| 0.4765 | 3.0 | 450 | 1.5943 |
| 0.2923 | 4.0 | 600 | 1.8834 |
| 0.1973 | 5.0 | 750 | 1.9693 |
| 0.1986 | 6.0 | 900 | 2.0187 |
| 0.1769 | 7.0 | 1050 | 2.1674 |
| 0.1359 | 8.0 | 1200 | 2.1402 |
| 0.1778 | 9.0 | 1350 | 2.3226 |
| 0.1353 | 10.0 | 1500 | 2.3321 |
| 0.1426 | 11.0 | 1650 | 2.4006 |
| 0.1412 | 12.0 | 1800 | 2.5354 |
| 0.164 | 13.0 | 1950 | 2.5339 |
| 0.1034 | 14.0 | 2100 | 2.5972 |
| 0.1087 | 15.0 | 2250 | 2.6059 |
| 0.0878 | 16.0 | 2400 | 2.6054 |
| 0.0985 | 17.0 | 2550 | 2.6881 |
| 0.1018 | 18.0 | 2700 | 2.7388 |
| 0.1091 | 19.0 | 2850 | 2.7657 |
| 0.0846 | 20.0 | 3000 | 2.7783 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | BENBENBENb/finetune_arc_20 | [
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emilstabil/DanSumT5-largeV_70322 | emilstabil | 2023-11-29T19:50:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:50:43Z | 2023-11-29T19:50:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_70322 | [
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hittman2018/papa3test | hittman2018 | 2023-11-29T19:54:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:54:29Z | 2023-11-29T19:51:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hittman2018/papa3test | [
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rthgrtyfgghjjtt/Spain-very-close-to-postBrexit-Gibraltar-deal-after-Cameron-meeting-World-News-3g-updated | rthgrtyfgghjjtt | 2023-11-29T19:53:35Z | 0 | 0 | null | [
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language:
- en
---
[]()
read the full article here : https://jsbin.com/hukadaluwa/edit?html,output
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The first-ever adaptive clinical trial for Australians living with MS will seek to reverse neurological damage caused by progressive multiple sclerosis.
With an initial $4 million in funding, the trial, commencing in January 2024, will enable researchers to investigate the potential benefits of several medications simultaneously, giving hope to the cohort of people living with one of the most debilitating forms of MS and who currently have extremely limited treatment options.
Progressive MS is a clinical form of MS characterised by a progressive worsening of symptoms and disability without periods of recovery. This complex autoimmune and neurodegenerative condition manifests differently in individuals over time, but without treatment, disability steadily accumulates.
Working with a national and international group of clinicians and researchers, the multi-arm, multi-stage (MAMS) adaptive, innovative design, known as PLATYPUS (PLatform Adaptive Trial for remYelination and neuroProtection in mUltiple Sclerosis*), will test two repurposed drugs alongside each other, providing more timely results about whether the treatments are working.
MSWA is the leading funder of the trial having contributed $3 million to PLATYPUS. MSWA CEO Melanie Kiely said MSWA is proud to fund the ambitious research project which will transform the way we test treatments for progressive MS.
"PLATYPUS has the potential to deliver a significant breakthrough, as we aim to provide real-life outcomes which positively impact the lives of people living with progressive MS - which is always our focus."
"By testing two repurposed drugs, we hope to find a treatment which can be quick to market for the people we support," Ms Kiely said.
MS Australia CEO, Mr Rohan Greenland, said currently, despite a number of traditional clinical trials for progressive MS in Australia, there is no treatment that repairs damage in progressive MS.
"With an estimated 40% or around 13,000 people living with progressive MS, the launch of PLATYPUS today is a major milestone, the first adaptive clinical trial for MS in Australia. This will ensure a treatment opportunity for people with progressive MS, the greatest unmet need in the MS landscape," Mr Greenland said.
PLATYPUS is an extension of the OCTOPUS (Optimal Clinical Trials Platform for Multiple Sclerosis) clinical trial, funded by the UK MS Society and launched April 2023.
Neurology Professor Simon Broadley from Griffith University's School of Medicine and Dentistry and Chief Investigator of PLATYPUS, says the funding was critical and the new trials will allow the testing of multiple potential therapies simultaneously and bring results much faster than traditional clinical trial methods.
"Collaborating with our OCTOPUS partners in the UK, we'll be trialling the drugs metformin, which is typically used to treat type 2 diabetes, and alpha-lipoic acid which is a health food supplement, these therapies have shown promise in promoting neuroprotection and\/or myelin repair in MS," Professor Broadley said.
The PLATYPUS trial will be rolled out across Australia through a collaboration of 20 academic and healthcare institutions and aims to recruit more than 250 participants in Australia.
"These multi-arm, multi-stage trials will require less time and fewer participants to assess the efficacy and cost-effectiveness of metformin and alpha-lipoic acid," Professor Broadley said.
The PLATYPUS team includes leading, internationally recognised neurologists, neuroscientists, statisticians and health economists covering five states across Australia, as well as people with progressive MS.
Mr Nigel Caswell, diagnosed with MS in 1993 and MS Australia's John Studdy Award winner in 2022, is excited for those people living with progressive MS in Australia.
"The launch of PLATYPUS today provides extraordinary hope for many people living with MS and is a major moment for MS research," Mr Caswell said.
President of MS Australia, Associate Professor Des Graham, says he is personally and professionally delighted for Australia's MS community that PLATYPUS can now be realised.
"With a $3 million grant from MSWA and a $1 million grant from MS Australia, this revolutionary adaptive trial provides tremendous promise for those living with progressive MS in Australia and MS Australia is extremely proud to lead this initiative," Associate Professor Graham said.
MS Australia's Head of Research, Dr Julia Morahan, says PLATYPUS will investigate whether these drugs reduce brain shrinkage and clinical progression of MS.
"We hope this will lead to positive trial outcomes that are readily translatable into practice, providing new hope for improved care for people with progressive MS in Australia and beyond," Dr Morahan said.
\/University Release. This material from the originating organization\/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here..... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Spain-very-close-to-postBrexit-Gibraltar-deal-after-Cameron-meeting-World-News-3g-updated | [
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Yulle/WhisperCheckpoints | Yulle | 2023-11-30T00:11:11Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:11:11Z | 2023-11-29T19:52:36.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Yulle/WhisperCheckpoints | [
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rthgrtyfgghjjtt/Hope-for-MS-sufferers-as-doctors-cautiously-very-excited-about-stem-h2-updated | rthgrtyfgghjjtt | 2023-11-29T19:54:41Z | 0 | 0 | null | [
"en",
"region:us"
] | 2023-11-29T19:54:41Z | 2023-11-29T19:52:49.000Z | null | null | ---
language:
- en
---
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read the full article here : https://pastebin.com/AL2cWkTM
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WASHINGTON (AP) -- Argentina's President-elect Javier Milei met Tuesday with senior Biden administration officials at the White House in his first international trip since winning a presidential runoff earlier this month.
The right-wing Milei had a "positive meeting" with White House national security adviser Jake Sullivan, the president-elect's office said in a statement. The visit by Argentina's incoming leader occurred while President Joe Biden was away from Washington at a memorial for former first lady Rosalynn Carter in Georgia and then traveling to Colorado.
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chernandezc/distilbert-base-uncased-finetuned-items-multi-label1 | chernandezc | 2023-11-29T20:26:31Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T20:26:31Z | 2023-11-29T19:53:28.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-items-multi-label1
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-finetuned-items-multi-label1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3682
- F1 Micro: 0.7014
- F1 Macro: 0.6332
- Hamming Loss: 0.1497
- Jaccard Score: 0.4793
## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Hamming Loss | Jaccard Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------------:|:-------------:|
| 0.6037 | 1.0 | 32 | 0.5429 | 0.0 | 0.0 | 0.2816 | 0.0 |
| 0.5311 | 2.0 | 64 | 0.5032 | 0.0 | 0.0 | 0.2816 | 0.0 |
| 0.466 | 3.0 | 96 | 0.4669 | 0.3740 | 0.2279 | 0.2253 | 0.1587 |
| 0.4125 | 4.0 | 128 | 0.4320 | 0.4842 | 0.3553 | 0.2019 | 0.2393 |
| 0.3546 | 5.0 | 160 | 0.4026 | 0.5436 | 0.4311 | 0.1868 | 0.2992 |
| 0.3132 | 6.0 | 192 | 0.3856 | 0.5788 | 0.4574 | 0.1799 | 0.3264 |
| 0.2733 | 7.0 | 224 | 0.3747 | 0.6469 | 0.5372 | 0.1635 | 0.4001 |
| 0.2333 | 8.0 | 256 | 0.3695 | 0.6725 | 0.5476 | 0.1552 | 0.4098 |
| 0.2046 | 9.0 | 288 | 0.3586 | 0.6818 | 0.5559 | 0.1538 | 0.4161 |
| 0.1819 | 10.0 | 320 | 0.3578 | 0.6949 | 0.6108 | 0.1484 | 0.4566 |
| 0.1624 | 11.0 | 352 | 0.3649 | 0.6742 | 0.5884 | 0.1593 | 0.4345 |
| 0.1457 | 12.0 | 384 | 0.3565 | 0.6870 | 0.6134 | 0.1552 | 0.4593 |
| 0.1288 | 13.0 | 416 | 0.3592 | 0.6961 | 0.6263 | 0.1511 | 0.4733 |
| 0.1193 | 14.0 | 448 | 0.3682 | 0.6852 | 0.6097 | 0.1552 | 0.4577 |
| 0.1113 | 15.0 | 480 | 0.3643 | 0.6947 | 0.6215 | 0.1497 | 0.4704 |
| 0.1023 | 16.0 | 512 | 0.3652 | 0.6959 | 0.6265 | 0.1525 | 0.4740 |
| 0.0942 | 17.0 | 544 | 0.3730 | 0.6925 | 0.6262 | 0.1525 | 0.4726 |
| 0.0906 | 18.0 | 576 | 0.3687 | 0.7014 | 0.6372 | 0.1497 | 0.4825 |
| 0.0893 | 19.0 | 608 | 0.3657 | 0.7033 | 0.6317 | 0.1484 | 0.4797 |
| 0.0882 | 20.0 | 640 | 0.3682 | 0.7014 | 0.6332 | 0.1497 | 0.4793 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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EleutherAI/pythia-410m-seed2 | EleutherAI | 2023-11-29T19:55:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:55:16Z | 2023-11-29T19:55:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | EleutherAI/pythia-410m-seed2 | [
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LoneStriker/deepseek-llm-67b-chat-3.0bpw-h6-exl2 | LoneStriker | 2023-11-30T00:08:50Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:08:50Z | 2023-11-29T19:56:26.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-llm-67b-chat-3.0bpw-h6-exl2 | [
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emilstabil/DanSumT5-baseV_42174 | emilstabil | 2023-11-29T19:58:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:58:37Z | 2023-11-29T19:58:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-baseV_42174 | [
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rthgrtyfgghjjtt/Kochaffiliated-group-backs-Nikki-Haley-for-president-dh-updated | rthgrtyfgghjjtt | 2023-11-29T20:00:47Z | 0 | 0 | null | [
"en",
"region:us"
] | 2023-11-29T20:00:47Z | 2023-11-29T19:58:52.000Z | null | null | ---
language:
- en
---
[]()
read the full article here : https://ibit.ly/UtdBS
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Spain says it's "very close" to agreeing a deal on the post-Brexit status of Gibraltar.
The country's foreign minister made the statement after meeting Foreign Secretary Lord Cameron in Brussels at a NATO meeting.
"Today we have made progress, because David Cameron has shown a willingness to reach an agreement," Jose Manuel Albares told reporters.
"We are very, very close," he added, in comments broadcast by Spain's TVE.
Mr Albares said the pair were discussing details such as how both sides would use the island's airport.
In a call with Mr Albares on Monday, Lord Cameron reiterated Britain's commitment to conclude a deal on Gibraltar "as soon as possible\.... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Kochaffiliated-group-backs-Nikki-Haley-for-president-dh-updated | [
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rthgrtyfgghjjtt/Senate-Dems-signal-openness-to-asylum-changes-for-Ukraine-money-gf-updated | rthgrtyfgghjjtt | 2023-11-29T20:00:51Z | 0 | 0 | null | [
"en",
"region:us"
] | 2023-11-29T20:00:51Z | 2023-11-29T19:58:57.000Z | null | null | ---
language:
- en
---
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Scientists have discovered a groundbreaking treatment that could stop the progression of multiple sclerosis (MS) in its tracks.
A team of international researchers published their findings Monday in the peer-reviewed journal Cell Stem Cell that show stem cell injections could protect the brain from the chronic disease.
In their first-in-man, early-stage clinical trial, researchers injected neural stem cells into the brains of 15 patients with secondary MS recruited from two hospitals in Italy.
All participants had high levels of disability (such as requiring a wheelchair) at the start of the trial but encouragingly showed no worsening of the debilitating illness a year later.
During the 12 months that the patients were observed, they reported no serious adverse symptoms.
Some noted minor side effects, but all were found to be temporary or reversible.
The research team also analyzed a subgroup of patients and found that a larger dose of injected stem cells led to a smaller reduction in brain volume over time -- suggesting that the stem cell transplant reduced inflammation.
MS is an unpredictable disease that impacts the central nervous system and disrupts the flow of information between the brain and the body, leading to a wide array of symptoms, including numbness, tingling, mood changes, memory problems, pain, fatigue, blindness and paralysis.
The condition eventually becomes debilitating for two-thirds of patients within 25-30 years of diagnosis.
While there are several approved treatments to help patients manage the condition, there is no cure or solution to stave off the disease, making experts very optimistic about these findings.
"These results show that special stem cells injected into the brain were safe and well-tolerated by people with secondary progressive MS," Caitlin Astbury, research communications manager at the MS Society, said in a statement.
"They also suggest this treatment approach might even stabilize disability progression," she added.
Over 2.8 million people live with MS worldwide, including 1 million in the US, the National Multiple Sclerosis Society reports.
The cause of the disease is still unknown.
"We desperately need to develop new treatments for secondary progressive MS, and I am cautiously very excited about our findings, which are a step towards developing a cell therapy for treating MS," Stefano Pluchino from the University of Cambridge, who co-led the study, said in a statement.
"We recognize that our study has limitations -- it was only a small study and there may have been confounding effects from the immunosuppressant drugs, for example -- but the fact that our treatment was safe and that its effects lasted over the 12 months of the trial means that we can proceed to the next stage of clinical trials.".... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/Senate-Dems-signal-openness-to-asylum-changes-for-Ukraine-money-gf-updated | [
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S4sch/Open-Hermes-2.5-neural-chat-3.1-InterlaceMerge-14b | S4sch | 2023-11-29T23:10:20Z | 0 | 1 | null | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:10:20Z | 2023-11-29T19:59:15.000Z | null | null | ---
license: apache-2.0
---
Experimental Merge, not sure yet whether it can do anything
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | S4sch/Open-Hermes-2.5-neural-chat-3.1-InterlaceMerge-14b | [
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zuazo/whisper-large-v2-gl | zuazo | 2023-11-30T01:19:18Z | 0 | 0 | null | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:19:18Z | 2023-11-29T19:59:17.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | zuazo/whisper-large-v2-gl | [
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mdgossett/sd_1-5_output_30_images_batch_size_4_V2 | mdgossett | 2023-11-29T19:59:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:59:56Z | 2023-11-29T19:59:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mdgossett/sd_1-5_output_30_images_batch_size_4_V2 | [
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Lubub/VicenteCelestino1917_Dataset_RVC | Lubub | 2023-11-29T20:02:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:02:39Z | 2023-11-29T20:02:07.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Lubub/VicenteCelestino1917_Dataset_RVC | [
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rthgrtyfgghjjtt/A-family-dog-accompanies-12-hostages-returned-from-Gaza-on-Tuesday-2b-updated | rthgrtyfgghjjtt | 2023-11-29T20:04:14Z | 0 | 0 | null | [
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language:
- en
---
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The Ohio House has voted on a bill to ensure that the state cannot ban gas-powered vehicles, protecting pollution in a state that has one of the worst environmental legacies in the US.
One of the biggest environmental stories in the history of the US was when the Cuyahoga river, which runs through Akron and Cleveland, caught fire in 1969 when an oil slick on the water was set ablaze, damaging a railroad bridge.
The fire made headlines and news spread across the country, and the story in some ways sparked the national environmental movement. But that's not the only time the river caught fire - it burned at least a dozen times before that, due to constant oil slicks on the river.
Despite that wake-up call, things haven't improved much for Ohio since then. The state ranks 46th in air pollution and 48th in toxic pollutants, and 42nd place in life expectancy, its residents dying more than 4 years earlier than those of California, New York and Hawaii.
And now, Ohio House republicans are expanding on that terrible environmental legacy by passing a bill that ensures the state won't ban gas-powered vehicles, which are broadly responsible for poor air quality and significant health harms across the US. The bill specifically stops Ohio from adopting California's "Advanced Clean Cars II" regulation, or any regulation which would be stronger than federal standards, even though nobody was proposing adoption of those standards. It passed 70-23, with all 65 republicans and 5 Democrats voting in favor.
As is common with republican policymaking, the bill does not have a lot of substance to it. The relevant section is only 10 lines long, and does not make any attempt to lay out the reasons or backing for its existence (compare to California's thousands of pages of scientific documents and analysis backing its regulation).
The best "reasoning" we can find are a series of quotes from state republicans, quoted by WOUB, Ohio University's public broadcaster.
One particularly ridiculous quote is from Representative Bill Seitz (R-Cincinnati), who says EVs would "socialize the price of recharging an EV while keeping the benefits private." This runs counter to reality, where in actual fact, gas-powered vehicles impose significant health costs which are not paid for by the private car users burning that gasoline.
These socialized costs of gasoline total up to $7 trillion globally over the course of a year, and $760 billion in the US alone - costs which Seitz wants you to keep paying in the form of health bills forced on you by the poisonous fossil fuels he's voted to support.
Seitz was also a co-sponsor of Ohio's House Bill 6. Described as the "worst energy bill of the 21st century," it raised energy prices to pay for a corrupt bailout of energy companies. It was passed after bribes to state republicans and led to a racketeering conviction for republican former Ohio House speaker Larry Householder. That bill extended subsidies to coal plants which continue to cost Ohioans hundreds of millions of dollars, socializing the costs of dirty pollution on both the front end through direct subsidy and the back end through higher health costs. So we hardly think that Seitz is genuinely interested in reducing costs to Ohioans.
Ohio is not content to harm its own residents' health, it's also trying to harm the health of those outside of Ohio. In September, Ohio republicans championed a bill which passed in the US House of Representatives which would force California to have worse pollution standards. But that bill didn't go anywhere - Senate Democrats, and President Joe Biden, aren't outwardly hostile to human life like republicans are, and were not interested in passing such legislation.
But the Ohio bill might actually make progress, particularly due to Ohio's unconstitutionally-gerrymandered district maps which give the environment-hostile republican party large majorities in its statehouse. It moves on to the state Senate next, and if it passes through the Senate, on to republican Governor DeWine's desk.
But in the end, this bill probably won't do much. The state wasn't planning to adopt California's regulations anyway, so this is just an example of Ohio House republicans showing everyone who they are. Whenever they're given a chance - even when it's not even a question anyone is asking - they'll side against people and in favor of pollution. We've seen this before from the national republican party, too - it's kind of a pattern.
What the bill might do, though, is signal to EV companies that their investment dollars are not welcome in Ohio. There are currently hundreds of billions of dollars being invested across the country into green jobs. Ohio has already seen some of that money, with Honda investing over $4 billion in the state to build up EV jobs, and is moving forward with installing DC fast chargers with federal money, despite being an EV laggard (perhaps due to one of the highest dumb EV fees in the nation, at $200\/year).
Actions like this could make the state less attractive for future investment, as we've noted about other states' anti-EV efforts. This was pointed out by Rep. Michele Grim (D-Toledo), who said "it signals those who want to invest their valuable dollars in clean energy and electric vehicle development that Ohio is not welcome to those investments. We should instead be taking steps to make sure Ohio is a more attractive state for clean energy job creation.".... | null | null | null | null | null | null | null | null | null | null | null | null | rthgrtyfgghjjtt/A-family-dog-accompanies-12-hostages-returned-from-Gaza-on-Tuesday-2b-updated | [
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Gsabriel/shalevsvoice | Gsabriel | 2023-11-29T20:27:56Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T20:27:56Z | 2023-11-29T20:02:56.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Gsabriel/shalevsvoice | [
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srohatgi/allsci-zephyr-7b-kg-gptq | srohatgi | 2023-11-29T20:03:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:03:14Z | 2023-11-29T20:03:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | srohatgi/allsci-zephyr-7b-kg-gptq | [
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jdp8/stable-diffusion-1-5-seg-v11p-onnx | jdp8 | 2023-11-29T20:07:49Z | 0 | 0 | null | [
"onnx",
"region:us"
] | 2023-11-29T20:07:49Z | 2023-11-29T20:03:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jdp8/stable-diffusion-1-5-seg-v11p-onnx | [
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Roxysun/cs2fi_wav2vec2-large-xls-r-300m-czech-colab | Roxysun | 2023-11-29T22:03:04Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:facebook/wav2vec2-lv-60-espeak-cv-ft",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:03:04Z | 2023-11-29T20:07:02.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-lv-60-espeak-cv-ft
tags:
- generated_from_trainer
datasets:
- voxpopuli
metrics:
- wer
model-index:
- name: cs2fi_wav2vec2-large-xls-r-300m-czech-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: voxpopuli
type: voxpopuli
config: fi
split: test
args: fi
metrics:
- name: Wer
type: wer
value: 1.0754716981132075
---
<!-- 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. -->
# cs2fi_wav2vec2-large-xls-r-300m-czech-colab
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 485.7458
- Wer: 1.0755
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3007.5297 | 3.51 | 100 | 523.8923 | 0.9706 |
| 353.1859 | 7.02 | 200 | 270.8087 | 0.9665 |
| 207.1084 | 10.53 | 300 | 215.3542 | 0.9350 |
| 186.3063 | 14.04 | 400 | 210.1422 | 0.9119 |
| 171.7259 | 17.54 | 500 | 291.5182 | 1.0629 |
| 142.6091 | 21.05 | 600 | 219.2806 | 0.9602 |
| 118.6791 | 24.56 | 700 | 312.2755 | 1.1132 |
| 96.153 | 28.07 | 800 | 320.7119 | 1.0545 |
| 82.968 | 31.58 | 900 | 357.5117 | 1.0629 |
| 71.2426 | 35.09 | 1000 | 421.3889 | 0.9916 |
| 58.8083 | 38.6 | 1100 | 433.8375 | 1.1048 |
| 54.5225 | 42.11 | 1200 | 482.5988 | 1.0566 |
| 48.12 | 45.61 | 1300 | 479.3787 | 1.0860 |
| 43.3324 | 49.12 | 1400 | 485.7458 | 1.0755 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Roxysun/cs2fi_wav2vec2-large-xls-r-300m-czech-colab | [
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Nikt271/Konieczko | Nikt271 | 2023-11-29T20:08:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:08:17Z | 2023-11-29T20:07:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Nikt271/Konieczko | [
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akurz/sd-class-metface-64-250epoch-clean | akurz | 2023-11-29T20:12:25Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:12:25Z | 2023-11-29T20:12:25.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | akurz/sd-class-metface-64-250epoch-clean | [
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staticai/ZroOH | staticai | 2023-11-29T20:33:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:33:10Z | 2023-11-29T20:13:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | staticai/ZroOH | [
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Frixi/ShawnMendes_2017 | Frixi | 2023-11-29T20:15:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T20:15:29Z | 2023-11-29T20:15:16.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Frixi/ShawnMendes_2017 | [
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onizukal/SMIDS_10x_beit_base_sgd_lr00001_fold1 | onizukal | 2023-11-29T23:51:33Z | 0 | 0 | null | [
"transformers",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:51:33Z | 2023-11-29T20:15:45.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SMIDS_10x_beit_base_sgd_lr00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6410684474123539
---
<!-- 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. -->
# SMIDS_10x_beit_base_sgd_lr00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8274
- Accuracy: 0.6411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1827 | 1.0 | 751 | 1.1967 | 0.3940 |
| 1.1441 | 2.0 | 1502 | 1.1684 | 0.4007 |
| 1.1144 | 3.0 | 2253 | 1.1432 | 0.4090 |
| 1.0939 | 4.0 | 3004 | 1.1210 | 0.4224 |
| 1.045 | 5.0 | 3755 | 1.1003 | 0.4307 |
| 1.0432 | 6.0 | 4506 | 1.0811 | 0.4508 |
| 1.0436 | 7.0 | 5257 | 1.0628 | 0.4641 |
| 0.9771 | 8.0 | 6008 | 1.0458 | 0.4725 |
| 0.9844 | 9.0 | 6759 | 1.0301 | 0.4942 |
| 0.945 | 10.0 | 7510 | 1.0152 | 0.5058 |
| 0.9762 | 11.0 | 8261 | 1.0014 | 0.5225 |
| 0.9493 | 12.0 | 9012 | 0.9884 | 0.5292 |
| 0.9554 | 13.0 | 9763 | 0.9758 | 0.5359 |
| 0.9422 | 14.0 | 10514 | 0.9643 | 0.5526 |
| 0.9174 | 15.0 | 11265 | 0.9535 | 0.5693 |
| 0.938 | 16.0 | 12016 | 0.9430 | 0.5826 |
| 0.9008 | 17.0 | 12767 | 0.9331 | 0.5993 |
| 0.8964 | 18.0 | 13518 | 0.9240 | 0.6060 |
| 0.8914 | 19.0 | 14269 | 0.9156 | 0.6027 |
| 0.9121 | 20.0 | 15020 | 0.9078 | 0.6110 |
| 0.85 | 21.0 | 15771 | 0.9006 | 0.6127 |
| 0.8351 | 22.0 | 16522 | 0.8938 | 0.6144 |
| 0.8562 | 23.0 | 17273 | 0.8876 | 0.6127 |
| 0.8579 | 24.0 | 18024 | 0.8819 | 0.6127 |
| 0.8392 | 25.0 | 18775 | 0.8765 | 0.6194 |
| 0.884 | 26.0 | 19526 | 0.8716 | 0.6277 |
| 0.8305 | 27.0 | 20277 | 0.8672 | 0.6294 |
| 0.8444 | 28.0 | 21028 | 0.8629 | 0.6294 |
| 0.8309 | 29.0 | 21779 | 0.8592 | 0.6361 |
| 0.8043 | 30.0 | 22530 | 0.8557 | 0.6394 |
| 0.8327 | 31.0 | 23281 | 0.8527 | 0.6411 |
| 0.7831 | 32.0 | 24032 | 0.8497 | 0.6377 |
| 0.805 | 33.0 | 24783 | 0.8469 | 0.6377 |
| 0.761 | 34.0 | 25534 | 0.8444 | 0.6344 |
| 0.7653 | 35.0 | 26285 | 0.8420 | 0.6327 |
| 0.8036 | 36.0 | 27036 | 0.8399 | 0.6344 |
| 0.8145 | 37.0 | 27787 | 0.8381 | 0.6344 |
| 0.7958 | 38.0 | 28538 | 0.8364 | 0.6361 |
| 0.8039 | 39.0 | 29289 | 0.8348 | 0.6344 |
| 0.7963 | 40.0 | 30040 | 0.8335 | 0.6361 |
| 0.7902 | 41.0 | 30791 | 0.8322 | 0.6394 |
| 0.7553 | 42.0 | 31542 | 0.8310 | 0.6411 |
| 0.8144 | 43.0 | 32293 | 0.8301 | 0.6411 |
| 0.8002 | 44.0 | 33044 | 0.8293 | 0.6411 |
| 0.8328 | 45.0 | 33795 | 0.8287 | 0.6411 |
| 0.7812 | 46.0 | 34546 | 0.8282 | 0.6411 |
| 0.7871 | 47.0 | 35297 | 0.8278 | 0.6411 |
| 0.7447 | 48.0 | 36048 | 0.8275 | 0.6411 |
| 0.798 | 49.0 | 36799 | 0.8274 | 0.6411 |
| 0.7782 | 50.0 | 37550 | 0.8274 | 0.6411 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | onizukal/SMIDS_10x_beit_base_sgd_lr00001_fold1 | [
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Realgon/roberta_sst5_padding60model | Realgon | 2023-11-29T21:27:59Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:27:59Z | 2023-11-29T20:16:03.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding60model
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. -->
# roberta_sst5_padding60model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8019
- Accuracy: 0.5674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2655 | 1.0 | 534 | 1.1383 | 0.5104 |
| 0.9941 | 2.0 | 1068 | 1.0110 | 0.5588 |
| 0.8336 | 3.0 | 1602 | 1.0624 | 0.5661 |
| 0.6969 | 4.0 | 2136 | 1.2062 | 0.5520 |
| 0.5586 | 5.0 | 2670 | 1.2584 | 0.5643 |
| 0.447 | 6.0 | 3204 | 1.5017 | 0.5624 |
| 0.3437 | 7.0 | 3738 | 1.7553 | 0.5493 |
| 0.2636 | 8.0 | 4272 | 1.8688 | 0.5557 |
| 0.224 | 9.0 | 4806 | 2.1154 | 0.5638 |
| 0.2058 | 10.0 | 5340 | 2.5540 | 0.5462 |
| 0.1692 | 11.0 | 5874 | 2.8222 | 0.5462 |
| 0.1631 | 12.0 | 6408 | 2.8802 | 0.5588 |
| 0.1285 | 13.0 | 6942 | 3.1257 | 0.5597 |
| 0.1158 | 14.0 | 7476 | 3.2779 | 0.5683 |
| 0.0863 | 15.0 | 8010 | 3.5119 | 0.5561 |
| 0.0836 | 16.0 | 8544 | 3.4229 | 0.5633 |
| 0.0604 | 17.0 | 9078 | 3.6220 | 0.5615 |
| 0.0391 | 18.0 | 9612 | 3.7717 | 0.5566 |
| 0.0399 | 19.0 | 10146 | 3.7460 | 0.5647 |
| 0.0279 | 20.0 | 10680 | 3.8019 | 0.5674 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst5_padding60model | [
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Shynar22/12345 | Shynar22 | 2023-11-29T20:19:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:19:57Z | 2023-11-29T20:19:57.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Shynar22/12345 | [
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wrandhawa/Llama-2-7b-chat-finetune | wrandhawa | 2023-11-29T20:21:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:21:23Z | 2023-11-29T20:21:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | wrandhawa/Llama-2-7b-chat-finetune | [
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mriches001/MRichesNewModel | mriches001 | 2023-11-29T20:21:27Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T20:21:27Z | 2023-11-29T20:21:27.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | mriches001/MRichesNewModel | [
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Johnnypjp/distilbert-base-uncased-finetuned-emotion | Johnnypjp | 2023-11-29T20:21:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:21:38Z | 2023-11-29T20:21:38.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Johnnypjp/distilbert-base-uncased-finetuned-emotion | [
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A-Issa-1999/whisper-tiny-test-2 | A-Issa-1999 | 2023-11-29T20:22:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:22:06Z | 2023-11-29T20:22:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | A-Issa-1999/whisper-tiny-test-2 | [
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habanoz/output | habanoz | 2023-11-29T20:26:25Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T",
"license:apache-2.0",
"region:us"
] | 2023-11-29T20:26:25Z | 2023-11-29T20:23:06.000Z | null | null | ---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
tags:
- generated_from_trainer
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 16
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Samoed/llama-2-7b-airplane-tickets | Samoed | 2023-11-29T20:34:27Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:34:27Z | 2023-11-29T20:23:16.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Samoed/llama-2-7b-airplane-tickets | [
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usmanasiff/fyp | usmanasiff | 2023-11-29T20:23:25Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T20:23:25Z | 2023-11-29T20:23:25.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | usmanasiff/fyp | [
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sainfo921/blufol | sainfo921 | 2023-11-29T20:53:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:53:24Z | 2023-11-29T20:23:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sainfo921/blufol | [
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError40percentSeed100 | behzadnet | 2023-11-29T20:24:44Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T20:24:44Z | 2023-11-29T20:24:39.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError40percentSeed100 | behzadnet | 2023-11-29T20:24:53Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T20:24:53Z | 2023-11-29T20:24:50.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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Weni/Test2-ZeroShot-Multilanguage-3.0-Llama2-13B | Weni | 2023-11-29T21:36:53Z | 0 | 0 | null | [
"peft",
"pytorch",
"tensorboard",
"region:us"
] | 2023-11-29T21:36:53Z | 2023-11-29T20:26:47.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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Logisx/open_llama_3b_v2-Fine-Tuned-Grade_School_Math_Instructions | Logisx | 2023-11-29T20:29:20Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
] | 2023-11-29T20:29:20Z | 2023-11-29T20:29:11.000Z | null | null | ---
library_name: peft
base_model: openlm-research/open_llama_3b_v2
---
# Model Card for Model ID
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## Model Details
### Model Description
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
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Rex010/Rex | Rex010 | 2023-11-29T20:30:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:30:33Z | 2023-11-29T20:30:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Rex010/Rex | [
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] |
habanoz/output2 | habanoz | 2023-11-29T20:35:45Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T",
"license:apache-2.0",
"region:us"
] | 2023-11-29T20:35:45Z | 2023-11-29T20:31:32.000Z | null | null | ---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
tags:
- generated_from_trainer
model-index:
- name: output2
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. -->
# output2
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 16
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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mwic/hugtest | mwic | 2023-11-29T20:32:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:32:49Z | 2023-11-29T20:32:49.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mwic/hugtest | [
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benayas/llama-2-7b-snips_v6 | benayas | 2023-11-29T20:42:14Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:42:14Z | 2023-11-29T20:32:55.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | benayas/llama-2-7b-snips_v6 | [
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hkivancoral/smids_1x_beit_base_adamax_001_fold1 | hkivancoral | 2023-11-29T22:16:35Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:16:35Z | 2023-11-29T20:33:56.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_beit_base_adamax_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8530884808013356
---
<!-- 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. -->
# smids_1x_beit_base_adamax_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2333
- Accuracy: 0.8531
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6722 | 1.0 | 76 | 0.7068 | 0.7346 |
| 0.5257 | 2.0 | 152 | 0.7906 | 0.6978 |
| 0.3766 | 3.0 | 228 | 0.4936 | 0.8030 |
| 0.4703 | 4.0 | 304 | 0.5194 | 0.8047 |
| 0.3758 | 5.0 | 380 | 0.4944 | 0.8047 |
| 0.3685 | 6.0 | 456 | 0.4662 | 0.8364 |
| 0.2812 | 7.0 | 532 | 0.5286 | 0.8314 |
| 0.2831 | 8.0 | 608 | 0.4636 | 0.8331 |
| 0.2359 | 9.0 | 684 | 0.5034 | 0.8063 |
| 0.1426 | 10.0 | 760 | 0.5477 | 0.8280 |
| 0.2668 | 11.0 | 836 | 0.6880 | 0.8130 |
| 0.182 | 12.0 | 912 | 0.6113 | 0.8280 |
| 0.1925 | 13.0 | 988 | 0.5781 | 0.8280 |
| 0.1404 | 14.0 | 1064 | 0.8189 | 0.8114 |
| 0.0795 | 15.0 | 1140 | 0.8425 | 0.8230 |
| 0.0585 | 16.0 | 1216 | 0.6551 | 0.8481 |
| 0.0935 | 17.0 | 1292 | 0.7044 | 0.8347 |
| 0.0369 | 18.0 | 1368 | 0.9110 | 0.8414 |
| 0.0816 | 19.0 | 1444 | 0.9853 | 0.8414 |
| 0.063 | 20.0 | 1520 | 0.7577 | 0.8464 |
| 0.0166 | 21.0 | 1596 | 0.8613 | 0.8381 |
| 0.0172 | 22.0 | 1672 | 0.7211 | 0.8548 |
| 0.0101 | 23.0 | 1748 | 0.9887 | 0.8297 |
| 0.059 | 24.0 | 1824 | 1.1066 | 0.8414 |
| 0.0163 | 25.0 | 1900 | 0.8966 | 0.8481 |
| 0.0425 | 26.0 | 1976 | 0.9615 | 0.8364 |
| 0.0118 | 27.0 | 2052 | 1.0527 | 0.8481 |
| 0.0022 | 28.0 | 2128 | 1.0163 | 0.8464 |
| 0.0009 | 29.0 | 2204 | 1.0736 | 0.8514 |
| 0.0005 | 30.0 | 2280 | 1.0490 | 0.8531 |
| 0.0032 | 31.0 | 2356 | 1.1469 | 0.8514 |
| 0.0106 | 32.0 | 2432 | 1.1588 | 0.8497 |
| 0.06 | 33.0 | 2508 | 1.1292 | 0.8514 |
| 0.0041 | 34.0 | 2584 | 1.0765 | 0.8531 |
| 0.0193 | 35.0 | 2660 | 1.2132 | 0.8548 |
| 0.0004 | 36.0 | 2736 | 1.1489 | 0.8481 |
| 0.0134 | 37.0 | 2812 | 1.2292 | 0.8464 |
| 0.0047 | 38.0 | 2888 | 1.1921 | 0.8514 |
| 0.0036 | 39.0 | 2964 | 1.2034 | 0.8464 |
| 0.0001 | 40.0 | 3040 | 1.1597 | 0.8481 |
| 0.0065 | 41.0 | 3116 | 1.1753 | 0.8548 |
| 0.0001 | 42.0 | 3192 | 1.1808 | 0.8548 |
| 0.0 | 43.0 | 3268 | 1.1898 | 0.8564 |
| 0.0001 | 44.0 | 3344 | 1.2021 | 0.8581 |
| 0.0061 | 45.0 | 3420 | 1.2174 | 0.8564 |
| 0.0 | 46.0 | 3496 | 1.2210 | 0.8548 |
| 0.0025 | 47.0 | 3572 | 1.2289 | 0.8531 |
| 0.0025 | 48.0 | 3648 | 1.2311 | 0.8548 |
| 0.0023 | 49.0 | 3724 | 1.2330 | 0.8531 |
| 0.0044 | 50.0 | 3800 | 1.2333 | 0.8531 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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joyfine/vicuna-7b-fine-tuning_twitter_20_32 | joyfine | 2023-11-29T20:40:19Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:40:19Z | 2023-11-29T20:34:14.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | joyfine/vicuna-7b-fine-tuning_twitter_20_32 | [
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usmanasiff/fyp-falcon-7b | usmanasiff | 2023-11-29T20:38:22Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T20:38:22Z | 2023-11-29T20:34:26.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | usmanasiff/fyp-falcon-7b | [
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gms0817/mistral-ehrs-transcription-summary | gms0817 | 2023-11-29T21:12:43Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2023-11-29T21:12:43Z | 2023-11-29T20:34:27.000Z | null | null | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-ehrs-transcription-summary
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. -->
# mistral-ehrs-transcription-summary
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7805
## 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: 2.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2536 | 0.17 | 25 | 1.0469 |
| 0.9063 | 0.34 | 50 | 0.8918 |
| 0.7291 | 0.51 | 75 | 0.8037 |
| 0.6537 | 0.68 | 100 | 0.7250 |
| 0.5135 | 0.85 | 125 | 0.7143 |
| 0.5284 | 1.02 | 150 | 0.7091 |
| 0.3765 | 1.19 | 175 | 0.7062 |
| 0.3222 | 1.36 | 200 | 0.7200 |
| 0.3379 | 1.53 | 225 | 0.6987 |
| 0.2712 | 1.7 | 250 | 0.7020 |
| 0.2683 | 1.87 | 275 | 0.7031 |
| 0.2785 | 2.04 | 300 | 0.7405 |
| 0.1721 | 2.21 | 325 | 0.7276 |
| 0.1822 | 2.38 | 350 | 0.7372 |
| 0.2321 | 2.55 | 375 | 0.7441 |
| 0.1753 | 2.72 | 400 | 0.7400 |
| 0.1678 | 2.89 | 425 | 0.7524 |
| 0.1708 | 3.06 | 450 | 0.7665 |
| 0.0973 | 3.23 | 475 | 0.7824 |
| 0.1266 | 3.4 | 500 | 0.7805 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | gms0817/mistral-ehrs-transcription-summary | [
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maddiehope/gender_classification | maddiehope | 2023-11-29T20:35:40Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T20:35:40Z | 2023-11-29T20:35:40.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | maddiehope/gender_classification | [
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SWilliams20/Implementing-Robust-Watermarking-for-Model-Protection | SWilliams20 | 2023-11-29T20:45:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:45:21Z | 2023-11-29T20:36:51.000Z | null | null | ---
license: mit
---# SecureAI: Implementing Robust Watermarking for Model Protection
Welcome to SecureAI, a project demonstrating the implementation of watermarking techniques to protect machine learning models from unauthorized use or replication.
## Overview
Machine learning models are vulnerable to intellectual property theft or unauthorized replication, posing a challenge for model developers and organizations. SecureAI addresses this concern by embedding a unique signature or watermark into the model, enabling verification of its authenticity and protecting it from misuse.
This project aims to demonstrate:
- Implementation of a watermarking algorithm for model protection.
- Embedding a watermark into a machine learning model without compromising performance.
- Evaluating the robustness of the watermark against various attacks and model modifications.
- Detection and extraction of the watermark for verification purposes.
## Key Components
- **Watermarking Algorithm**: The project implements a watermarking algorithm to embed a unique identifier into the machine learning model.
- **Model Training and Embedding**: Train a sample machine learning model and embed a watermark using the implemented algorithm.
- **Robustness Testing**: Assess the robustness of the watermark by conducting tests such as model fine-tuning, performance evaluation, and watermark extraction.
- **Demonstration**: A demonstration showcasing watermark detection and extraction from the model to verify its presence and authenticity.
## Usage
To reproduce the watermarking process or experiment with watermark detection:
1. **Requirements**: Ensure you have the necessary dependencies installed (Python, TensorFlow/PyTorch, etc.).
2. **Clone the Repository**: Clone this repository to your local machine.
3. **Follow Instructions**: Follow the instructions in the code or README files to run the watermarking algorithm, embed the watermark, and perform detection/extraction. | null | null | null | null | null | null | null | null | null | null | null | null | SWilliams20/Implementing-Robust-Watermarking-for-Model-Protection | [
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owanr/SChem5Labels-roberta-large-intra-sorted-model_annots-cross-ent | owanr | 2023-11-29T20:50:45Z | 0 | 0 | null | [
"pytorch",
"generated_from_trainer",
"base_model:roberta-large",
"license:mit",
"region:us"
] | 2023-11-29T20:50:45Z | 2023-11-29T20:37:52.000Z | null | null | ---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-large-intra-sorted-model_annots-cross-ent
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. -->
# SChem5Labels-roberta-large-intra-sorted-model_annots-cross-ent
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) 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: 0.0001
- 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: 50
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.14.1
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mdgossett/sd_1-5_output_30_images_batch_size_8_V2 | mdgossett | 2023-11-29T20:41:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:41:24Z | 2023-11-29T20:41:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mdgossett/sd_1-5_output_30_images_batch_size_8_V2 | [
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hugoma11/sq | hugoma11 | 2023-11-29T20:55:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:55:53Z | 2023-11-29T20:42:09.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hugoma11/sq | [
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TahaCakir/turkishReviews-ds-mini | TahaCakir | 2023-11-29T20:53:24Z | 0 | 0 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:53:24Z | 2023-11-29T20:43:35.000Z | null | null | ---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: turkishReviews-ds-mini
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkishReviews-ds-mini
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:
- Train Loss: 9.1621
- Validation Loss: 9.2572
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2846 | 9.9736 | 0 |
| 9.6426 | 9.6247 | 1 |
| 9.1621 | 9.2572 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TahaCakir/turkishReviews-ds-mini | [
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Weni/Test3-ZeroShot-Multilanguage-3.0-Llama2-13B | Weni | 2023-11-29T20:54:00Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T20:54:00Z | 2023-11-29T20:43:39.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Weni/Test3-ZeroShot-Multilanguage-3.0-Llama2-13B | [
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Broomva/llama-2-7b-chat-spa-guc | Broomva | 2023-11-29T20:46:08Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:46:08Z | 2023-11-29T20:43:49.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Broomva/llama-2-7b-chat-spa-guc | [
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