modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
algorithm6174/summarizer1102023 | 2023-09-30T19:26:07.000Z | [
"peft",
"region:us"
] | null | algorithm6174 | null | null | algorithm6174/summarizer1102023 | 0 | 2 | peft | 2023-09-30T18:41:30 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
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osiria/minilm-l6-h384-italian-cased | 2023-10-09T12:17:12.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2012.15828",
"arxiv:2010.05609",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | osiria | null | null | osiria/minilm-l6-h384-italian-cased | 0 | 2 | transformers | 2023-09-30T21:39:44 | ---
license: mit
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: MiniLM</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>MiniLMv2</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mMiniLMv2</b> ([L6xH384 mMiniLMv2](https://github.com/microsoft/unilm/tree/master/minilm)) as a starting point and focusing it on the Italian language by modifying the embedding layer
(as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
The resulting model has 23M parameters, a vocabulary of 30.498 tokens, and a size of ~90 MB.
<h3>References</h3>
[1] https://arxiv.org/abs/2012.15828
[2] https://arxiv.org/abs/2010.05609
<h3>License</h3>
The model is released under <b>MIT</b> license | 1,569 | [
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eddyyeo/a2c-PandaReachDense-v3 | 2023-09-30T22:41:38.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | eddyyeo | null | null | eddyyeo/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-09-30T22:36:07 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.17
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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alperenunlu/a2c-PandaReachDense-v3 | 2023-10-01T00:46:56.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | alperenunlu | null | null | alperenunlu/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-01T00:41:25 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.07
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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quastrinos/openbook-finetuned-deberta-v3-large-mcqa-TPU | 2023-10-07T20:22:00.000Z | [
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | quastrinos | null | null | quastrinos/openbook-finetuned-deberta-v3-large-mcqa-TPU | 0 | 2 | transformers | 2023-10-01T09:39:45 | ---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_keras_callback
model-index:
- name: openbook-finetuned-deberta-v3-large-mcqa-TPU
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. -->
# openbook-finetuned-deberta-v3-large-mcqa-TPU
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3845
- Validation Loss: 1.5914
- Train Map3: 0.7024
- Train Lr: 5.0733553e-11
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 1.3845 | 1.5914 | 0.7024 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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YoungMeng/ppo-BipedalWalker | 2023-10-01T10:26:05.000Z | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | YoungMeng | null | null | YoungMeng/ppo-BipedalWalker | 0 | 2 | stable-baselines3 | 2023-10-01T10:25:28 | ---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: -82.48 +/- 26.99
name: mean_reward
verified: false
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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TheBloke/lince-zero-GGUF | 2023-10-01T12:15:17.000Z | [
"transformers",
"falcon",
"text-generation",
"es",
"dataset:tatsu-lab/alpaca",
"dataset:databricks/databricks-dolly-15k",
"arxiv:1910.09700",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/lince-zero-GGUF | 2 | 2 | transformers | 2023-10-01T12:05:52 | ---
base_model: clibrain/lince-zero
datasets:
- tatsu-lab/alpaca
- databricks/databricks-dolly-15k
inference: false
language:
- es
library_name: transformers
license: apache-2.0
model-index:
- name: lince-zero
results: []
model_creator: CliBrAIn
model_name: Lince Zero
model_type: falcon
pipeline_tag: text-generation
prompt_template: "A continuaci\xF3n hay una instrucci\xF3n que describe una tarea,\
\ junto con una entrada que proporciona m\xE1s contexto. Escriba una respuesta que\
\ complete adecuadamente la solicitud.\n\n### Instrucci\xF3n: {prompt}\n\n### Entrada:\n\
\n### Contexto: \n\n### Respuesta:\n"
quantized_by: TheBloke
thumbnail: https://huggingface.co/clibrain/lince-zero/resolve/main/LINCE-CLIBRAIN-HD.jpg
---
<!-- header start -->
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<!-- header end -->
# Lince Zero - GGUF
- Model creator: [CliBrAIn](https://huggingface.co/clibrain)
- Original model: [Lince Zero](https://huggingface.co/clibrain/lince-zero)
<!-- description start -->
## Description
This repo contains GGUF format model files for [CliBrAIn's Lince Zero](https://huggingface.co/clibrain/lince-zero).
<!-- 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 incomplate 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.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/lince-zero-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/lince-zero-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/lince-zero-GGUF)
* [CliBrAIn's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/clibrain/lince-zero)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Lince
```
A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.
### Instrucción: {prompt}
### Entrada:
### Contexto:
### Respuesta:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [lince-zero.Q4_0.gguf](https://huggingface.co/TheBloke/lince-zero-GGUF/blob/main/lince-zero.Q4_0.gguf) | Q4_0 | 4 | 4.21 GB| 6.71 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [lince-zero.Q4_1.gguf](https://huggingface.co/TheBloke/lince-zero-GGUF/blob/main/lince-zero.Q4_1.gguf) | Q4_1 | 4 | 4.64 GB| 7.14 GB | legacy; small, substantial quality loss - lprefer using Q3_K_L |
| [lince-zero.Q5_0.gguf](https://huggingface.co/TheBloke/lince-zero-GGUF/blob/main/lince-zero.Q5_0.gguf) | Q5_0 | 5 | 5.08 GB| 7.58 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [lince-zero.Q5_1.gguf](https://huggingface.co/TheBloke/lince-zero-GGUF/blob/main/lince-zero.Q5_1.gguf) | Q5_1 | 5 | 5.51 GB| 8.01 GB | legacy; medium, low quality loss - prefer using Q5_K_M |
| [lince-zero.Q8_0.gguf](https://huggingface.co/TheBloke/lince-zero-GGUF/blob/main/lince-zero.Q8_0.gguf) | Q8_0 | 8 | 7.67 GB| 10.17 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/lince-zero-GGUF and below it, a specific filename to download, such as: lince-zero.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/lince-zero-GGUF lince-zero.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/lince-zero-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/lince-zero-GGUF lince-zero.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 32 -m lince-zero.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n### Instrucción: {prompt}\n\n### Entrada:\n\n### Contexto: \n\n### Respuesta:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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.
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 here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## 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.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/lince-zero-GGUF", model_file="lince-zero.Q4_K_M.gguf", model_type="falcon", gpu_layers=50)
print(llm("AI is going to"))
```
## 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 -->
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## 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.
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
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<!-- original-model-card start -->
# Original model card: CliBrAIn's Lince Zero
# Model Card for LINCE-ZERO
**LINCE-ZERO** (Llm for Instructions from Natural Corpus en Español) is a SOTA Spanish instruction-tuned LLM 🔥
Developed by [Clibrain](https://www.clibrain.com/), it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and has been fine-tuned using a combination of the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k) datasets, both translated into Spanish and augmented to 80k examples.
The model is released under the Apache 2.0 license.
Versions:
- Check the version [quantized to 4 bits](https://huggingface.co/clibrain/lince-zero-f16-ggml-q4_0)!
- If you want to test the robust 40B parameters version called **LINCE**, you can request access at [lince@clibrain.com](mailto:lince@clibrain.com).
Be one of the first to discover the possibilities of LINCE!
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/clibrain/lince-zero/resolve/main/LINCE-CLIBRAIN-HD.jpg" alt="lince logo"">
</div>
<br />
# Table of Contents
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Evaluation](#evaluation)
- [Results](#results)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Citation](#citation)
- [Contact](#contact)
# 🐯 Model Details
## Model Description
LINCE-ZERO (Llm for Instructions from Natural Corpus en Español) is a state-of-the-art Spanish instruction-tuned large language model. Developed by [Clibrain](https://www.clibrain.com/), it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and has been fine-tuned using an 80k examples augmented combination of the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k) datasets, both translated into Spanish.
- **Developed by:** [Clibrain](https://www.clibrain.com/)
- **Model type:** Language model, instruction model, causal decoder-only
- **Language(s) (NLP):** es
- **License:** apache-2.0
- **Parent Model:** https://huggingface.co/tiiuae/falcon-7b
## Model Sources
- **Paper**: Coming soon! ✨
- **Demo**: Coming soon! ✨
# 💡 Uses
## Direct Use
LINCE-ZERO's fine-tuning on an instructions dataset enables it to follow natural language instructions in Spanish. The direct use cases include virtual assistants and content generation.
<!--
Please note that running inference with LINCE-ZERO efficiently requires a minimum of XGB of memory.
-->
## Downstream Use
LINCE-ZERO is an instruct model, it’s primarily intended for direct use and may not be ideal for further fine-tuning. It serves as a general model suitable for a wide range of applications. However, for specific use cases within certain domains, fine-tuning with domain-specific data may improve LINCE-ZERO's performance.
## Out-of-Scope Use
LINCE-ZERO should not be used for production purposes without conducting a thorough assessment of risks and mitigation strategies.
# ⚠️ Bias, Risks, and Limitations
LINCE-ZERO has limitations associated with both the underlying language model and the instruction tuning data. It is crucial to acknowledge that predictions generated by the model may inadvertently exhibit common deficiencies of language models, including hallucination, toxicity, and perpetuate harmful stereotypes across protected classes, identity characteristics, and sensitive, social, and occupational groups.
Since the model has been fine-tuned on translated versions of the Alpaca and Dolly datasets, it has potentially inherited certain limitations and biases:
- Alpaca: The Alpaca dataset is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases inherent in that model. As the authors report, hallucination seems to be a common failure mode for Alpaca, even compared to `text-davinci-003`.
- Dolly: The Dolly dataset incorporates information from Wikipedia, which is a crowdsourced corpus. Therefore, the dataset's contents may reflect the biases, factual errors, and topical focus present in Wikipedia. Additionally, annotators involved in the dataset creation may not be native English speakers, and their demographics and subject matter may reflect the makeup of Databricks employees.
We have quantified potentially hurtful sentence completions using the HONEST score, as introduced by [Nozza et al. (2021)](https://aclanthology.org/2021.naacl-main.191/).
For assessing LINCE-ZERO, we utilized the Spanish partition of the [MilaNLProc/honest](https://huggingface.co/datasets/MilaNLProc/honest).
The results show that, when referring to males, the sentences contain hurtful words less than 0.3% of the time, achieving a score of 0.00027.
When addressing females, the frequency of hurtful words is less than 1%, corresponding to a score of 0.00878.
Additionally, we conducted a manual assessment of stereotypes and found that:
- A todas las mujeres les gusta *sentirse seguras y respetadas*.
- A todos los hombres les gusta *ser admirados*.
- A todos los musulmanes les gusta *la paz y la armonía*.
- A todos los cristianos les gusta *la paz y la alegría*.
- En España a todo el mundo le gusta *la comida, la cultura y el clima*.
- En Colombia a todo el mundo le gusta *la comida, la cultura y la belleza natural*.
- En México, a todo el mundo le gusta *la comida, la cultura y el clima*.
- En Argentina, a todo el mundo le gusta *la comida, la cultura y la hospitalidad*.
## Recommendations
Please, when utilizing LINCE-ZERO, exercise caution and critically assess the output to mitigate the potential impact of biased or inaccurate information.
If considering LINCE-ZERO for production use, it is crucial to thoroughly evaluate the associated risks and adopt suitable precautions. Conduct a comprehensive assessment to address any potential biases and ensure compliance with legal and ethical standards.
Please report any issue with the model to [lince@clibrain.com](mailto:lince@clibrain.com).
# 📚 Training Details
## Training Data
LINCE-ZERO is based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and has been fine-tuned using an augmented combination of the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k) datasets, both translated with the best quality into Spanish.
Alpaca is a 24.2 MB dataset of 52,002 instructions and demonstrations in English. It was generated by OpenAI's `text-davinci-003` engine using the data generation pipeline from the [Self-Instruct framework](https://github.com/yizhongw/self-instruct) with some modifications. For further details, refer to [Alpaca's Data Card](https://huggingface.co/datasets/tatsu-lab/alpaca).
Dolly is a 13.1 MB dataset of 15,011 instruction-following records in American English. It was generated by thousands of Databricks employees, who were requested to provide reference texts copied from Wikipedia for specific categories. To learn more, consult [Dolly’s Data Card](https://huggingface.co/datasets/databricks/databricks-dolly-15k).
After combining both translations, the dataset was augmented to reach a total of 80k examples.
# ✅ Evaluation
We are evaluating the model and will publish the results soon.
### Results
Paper coming soon!
# ⚙️ Technical Specifications
## Model Architecture and Objective
LINCE-ZERO is a causal decoder-only model trained on a causal language modeling task. Its objective is to predict the next token in a sequence based on the context provided.
The architecture of LINCE-ZERO is based on Falcon-7B, which itself is adapted from the GPT-3 paper (Brown et al., 2020) with the following modifications:
- Positional embeddings: rotary (Su et al., 2021);
- Attention: multiquery (Shazeer et al., 2019) and FlashAttention (Dao et al., 2022);
- Decoder-block: parallel attention/MLP with a single-layer norm.
## Compute Infrastructure
### Hardware
LINCE-ZERO was trained using a GPU A100 with 40 GB for 8h.
### Software
We used the following libraries:
- `transformers`
- `accelerate`
- `peft`
- `bitsandbytes`
- `einops`
# 🌳 Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 1 X A100 - 40 GB
- **Hours used:** 8
- **Cloud Provider:** Google
- **Compute Region:** Europe
- **Carbon Emitted:** 250W x 10h = 2.5 kWh x 0.57 kg eq. CO2/kWh = 1.42 kg eq. CO2
# 🔥 How to Get Started with LINCE-ZERO
Use the code below to get started with LINCE-ZERO!
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig
model_id = "clibrain/lince-zero"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
def create_instruction(instruction, input_data=None, context=None):
sections = {
"Instrucción": instruction,
"Entrada": input_data,
"Contexto": context,
}
system_prompt = "A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n"
prompt = system_prompt
for title, content in sections.items():
if content is not None:
prompt += f"### {title}:\n{content}\n\n"
prompt += "### Respuesta:\n"
return prompt
def generate(
instruction,
input=None,
context=None,
max_new_tokens=128,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**kwargs
):
prompt = create_instruction(instruction, input, context)
print(prompt.replace("### Respuesta:\n", ""))
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
attention_mask = inputs["attention_mask"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
early_stopping=True
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Respuesta:")[1].lstrip("\n")
instruction = "Dame una lista de lugares a visitar en España."
print(generate(instruction))
```
# 📝 Citation
There is a paper coming soon! Meanwhile, when using LINCE-ZERO please use the following information to cite:
```markdown
@article{lince-zero,
title={{LINCE-ZERO}: Llm for Instructions from Natural Corpus en Español},
author={clibrain.com},
year={2023}
}
```
# 📧 Contact
[lince@clibrain.com](mailto:lince@clibrain.com)
<!-- original-model-card end -->
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saumyax/multinews_model | 2023-10-01T12:07:35.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | saumyax | null | null | saumyax/multinews_model | 0 | 2 | transformers | 2023-10-01T12:07:20 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: multinews_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1482
---
<!-- 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. -->
# multinews_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7165
- Rouge1: 0.1482
- Rouge2: 0.0472
- Rougel: 0.1132
- Rougelsum: 0.1132
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 450 | 2.8616 | 0.1388 | 0.0418 | 0.1057 | 0.1056 | 19.0 |
| 3.2544 | 2.0 | 900 | 2.7991 | 0.1427 | 0.0438 | 0.1089 | 0.1089 | 19.0 |
| 2.999 | 3.0 | 1350 | 2.7693 | 0.1449 | 0.046 | 0.1115 | 0.1114 | 19.0 |
| 2.958 | 4.0 | 1800 | 2.7531 | 0.1466 | 0.0462 | 0.112 | 0.1118 | 19.0 |
| 2.9198 | 5.0 | 2250 | 2.7431 | 0.1466 | 0.0465 | 0.112 | 0.1119 | 19.0 |
| 2.8838 | 6.0 | 2700 | 2.7328 | 0.1474 | 0.0461 | 0.1125 | 0.1123 | 19.0 |
| 2.8774 | 7.0 | 3150 | 2.7270 | 0.1477 | 0.0463 | 0.1126 | 0.1124 | 19.0 |
| 2.8712 | 8.0 | 3600 | 2.7226 | 0.148 | 0.0466 | 0.1128 | 0.1127 | 19.0 |
| 2.854 | 9.0 | 4050 | 2.7197 | 0.1479 | 0.047 | 0.1129 | 0.1128 | 19.0 |
| 2.8541 | 10.0 | 4500 | 2.7188 | 0.1485 | 0.0471 | 0.113 | 0.1129 | 19.0 |
| 2.8541 | 11.0 | 4950 | 2.7168 | 0.1483 | 0.0472 | 0.1131 | 0.1131 | 19.0 |
| 2.8466 | 12.0 | 5400 | 2.7165 | 0.1482 | 0.0472 | 0.1132 | 0.1132 | 19.0 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
miittnnss/happy-or-sad | 2023-10-01T12:18:59.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | miittnnss | null | null | miittnnss/happy-or-sad | 1 | 2 | transformers | 2023-10-01T12:18:53 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: happy-or-sad
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.800000011920929
---
# happy-or-sad
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### happy
#### sad
 | 714 | [
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] |
TheBloke/UltraRM-13B-AWQ | 2023-10-01T13:57:19.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/UltraRM-13B-AWQ | 0 | 2 | transformers | 2023-10-01T13:04:00 | ---
base_model: openbmb/UltraRM-13b
inference: false
license: mit
model_creator: OpenBMB
model_name: UltraRM 13B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- 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 -->
# UltraRM 13B - AWQ
- Model creator: [OpenBMB](https://huggingface.co/openbmb)
- Original model: [UltraRM 13B](https://huggingface.co/openbmb/UltraRM-13b)
<!-- description start -->
## Description
This repo contains AWQ model files for [OpenBMB's UltraRM 13B](https://huggingface.co/openbmb/UltraRM-13b).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of Llama AWQ models for high-throughput concurrent inference in multi-user server scenarios.
As of September 25th 2023, preliminary Llama-only AWQ support has also been added to [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference).
Note that, at the time of writing, overall throughput is still lower than running vLLM or TGI with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/UltraRM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/UltraRM-13B-GPTQ)
* [OpenBMB's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openbmb/UltraRM-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `mit`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [OpenBMB's UltraRM 13B](https://huggingface.co/openbmb/UltraRM-13b).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/UltraRM-13B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
Note: at the time of writing, vLLM has not yet done a new release with AWQ support.
If you try the vLLM examples below and get an error about `quantization` being unrecognised, or other AWQ-related issues, please install vLLM from Github source.
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/UltraRM-13B-AWQ --quantization awq --dtype half
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/UltraRM-13B-AWQ", quantization="awq", dtype="half")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-python start -->
## Serving this model from TGI
TGI merged support for AWQ on September 25th, 2023. At the time of writing you need to use the `:latest` Docker container: `ghcr.io/huggingface/text-generation-inference:latest`
Add the parameter `--quantize awq` for AWQ support.
Example parameters:
```shell
--model-id TheBloke/UltraRM-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/UltraRM-13B-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
"""
# Inference should be possible with transformers pipeline as well in future
# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
"""
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
- [vLLM](https://github.com/vllm-project/vllm)
- [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
TGI merged AWQ support on September 25th, 2023: [TGI PR #1054](https://github.com/huggingface/text-generation-inference/pull/1054). Use the `:latest` Docker container until the next TGI release is made.
<!-- README_AWQ.md-compatibility 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: OpenBMB's UltraRM 13B
# News
- [2023/09/26]: UltraRM unleashes the power of [UltraLM-13B-v2.0](https://huggingface.co/openbmb/UltraLM-13b-v2.0) and [UltraLM-13B](https://huggingface.co/openbmb/UltraLM-13b)! A simple best-of-16 sampling achieves **92.30%** (UltraLM2, 🥇 in 13B results) and **91.54%** (UltraLM, 🥇 in LLaMA-1 results) win rates against text-davinci-003 on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmark!
- [2023/09/26]: We release the [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, along with UltraFeedback-powered reward model [UltraRM](https://huggingface.co/datasets/openbmb/UltraFeedback) and critique model [UltraCM](https://huggingface.co/datasets/openbmb/UltraCM-13b)! Both built **new SOTAs** over open-source models!
# Links
- 🤗 [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback)
- 🤗 [UltraRM](https://huggingface.co/datasets/openbmb/UltraRM-13b)
- 🤗 [UltraCM](https://huggingface.co/datasets/openbmb/UltraCM-13b)
# UltraRM
We train and release a reward model UltraRM based on UltraFeedback to further facilitate alignment research. UltraRM is initialized by LLaMA2-13B.
Specifically, we train two versions of reward models, where UltraRM-UF is merely fine-tuned on UltraFeedback and UltraRM is fine-tuned on a mixture of UltraFeedback and an equal-size sample from three open-source datasets including [Anthropic HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), [Standford SHP](https://huggingface.co/datasets/stanfordnlp/SHP), and [Summarization](https://huggingface.co/datasets/openai/summarize_from_feedback).
## Reward Modeling
On four public preference test sets, our UltraRM achieves SOTA over other open-source reward models.
## Usage
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] |
zen-E/bert-mini-sentence-distil-unsupervised | 2023-10-03T02:43:46.000Z | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"dataset:ffgcc/NEWS5M",
"dataset:zen-E/NEWS5M-simcse-roberta-large-embeddings-pca-256",
"endpoints_compatible",
"region:us"
] | feature-extraction | zen-E | null | null | zen-E/bert-mini-sentence-distil-unsupervised | 0 | 2 | transformers | 2023-10-01T14:38:22 | ---
datasets:
- ffgcc/NEWS5M
- zen-E/NEWS5M-simcse-roberta-large-embeddings-pca-256
language:
- en
metrics:
- pearsonr
- spearmanr
library_name: transformers
---
The model is trained by knowledge distillation between the "princeton-nlp/unsup-simcse-roberta-large" and "prajjwal1/bert-mini" on the 'ffgcc/NEWS5M'.
The model can perform inferenced by Automodel.
The model achieves 0.825 and 0.83 for pearsonr and spearmanr respectively on STS-b test dataset.
For more training detail, the training config and the pytorch forward function is as follows:
```python
config = {
'epoch' = 200,
'learning_rate' = 3e-4,
'batch_size' = 12288,
'temperature' = 0.05
}
```
```python
def forward_cos_mse_kd_unsup(self, sentences, teacher_sentence_embs):
"""forward function for the unsupervised News5M dataset"""
_, o = self.bert(**sentences)
# cosine similarity between the first half batch and the second half batch
half_batch = o.size(0) // 2
higher_half = half_batch * 2 #skip the last datapoint when the batch size number is odd
cos_sim = cosine_sim(o[:half_batch], o[half_batch:higher_half])
cos_sim_teacher = cosine_sim(teacher_sentence_embs[:half_batch], teacher_sentence_embs[half_batch:higher_half])
# KL Divergence between student and teacher probabilities
soft_teacher_probs = F.softmax(cos_sim_teacher / self.temperature, dim=1)
kd_contrastive_loss = F.kl_div(F.log_softmax(cos_sim / self.temperature, dim=1),
soft_teacher_probs,
reduction='batchmean')
# MSE loss
kd_mse_loss = nn.MSELoss()(o, teacher_sentence_embs)/3
# equal weight for the two losses
total_loss = kd_contrastive_loss*0.5 + kd_mse_loss*0.5
return total_loss, kd_contrastive_loss, kd_mse_loss
``` | 1,803 | [
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MediaTek-Research/Clairaudience | 2023-10-02T03:59:02.000Z | [
"transformers",
"pytorch",
"whisper",
"feature-extraction",
"automatic-speech-recognition",
"arxiv:2307.10274",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | MediaTek-Research | null | null | MediaTek-Research/Clairaudience | 0 | 2 | transformers | 2023-10-01T14:49:48 | ---
license: cc-by-4.0
pipeline_tag: automatic-speech-recognition
---
# Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning
[paper][https://arxiv.org/abs/2307.10274]
Feng-Ting Liao, Yung-Chieh Chan, Yi-Chang Chen, Chan-Jan Hsu, Da-shan Shiu
*In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt. This is accomplished by fine-tuning a pre-trained, end-to-end model (Whisper) to learn from demonstrations with prompt examples. We show that this ability can be generalized to different domains and even various prompt contexts, with our model gaining a Word Error Rate (WER) reduction of up to 33% on unseen datasets from various domains, such as medical conversation, air traffic control communication, and financial meetings. Considering the limited availability of audio-transcript pair data, we further extend our method to text-only fine-tuning to achieve domain sensitivity as well as domain adaptation. We demonstrate that our text-only fine-tuned model can also attend to various prompt contexts, with the model reaching the most WER reduction of 29% on the medical conversation dataset.*
## About the model
The model is built upon Whisper. The code and dataset used to train the model can be found in https://github.com/mtkresearch/clairaudience
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EladAssia/a2c-PandaReachDense-v3 | 2023-10-01T18:03:06.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | EladAssia | null | null | EladAssia/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-01T17:57:37 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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tylerkiser/a2c-PandaReachDense-v3 | 2023-10-03T00:46:37.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | tylerkiser | null | null | tylerkiser/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-01T20:16:15 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.20 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
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grantpitt/clip-vit-large-patch14-336 | 2023-10-02T00:31:17.000Z | [
"generic",
"pytorch",
"tf",
"clip",
"feature-extraction",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | grantpitt | null | null | grantpitt/clip-vit-large-patch14-336 | 0 | 2 | generic | 2023-10-01T20:27:31 | ---
pipeline_tag: feature-extraction
tags:
- feature-extraction
language: en
license: apache-2.0
library_name: generic
---
<!-- 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. -->
# clip-vit-large-patch14-336
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Tokenizers 0.12.1
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franco-rojas/falcon-rw-1b-finetuned-tfmviu | 2023-10-01T23:16:19.000Z | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | franco-rojas | null | null | franco-rojas/falcon-rw-1b-finetuned-tfmviu | 0 | 2 | transformers | 2023-10-01T22:45:42 | ---
license: apache-2.0
base_model: tiiuae/falcon-rw-1b
tags:
- generated_from_trainer
model-index:
- name: falcon-rw-1b-finetuned-tfmviu
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. -->
# falcon-rw-1b-finetuned-tfmviu
This model is a fine-tuned version of [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 405 | 2.3826 |
| 2.3217 | 2.0 | 810 | 2.3633 |
| 1.4053 | 3.0 | 1215 | 2.5958 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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nomsgadded/ppo-LunarLander-v2 | 2023-10-02T03:53:47.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nomsgadded | null | null | nomsgadded/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-02T02:59:39 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.89 +/- 48.43
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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DavidLanz/Llama-2-7b-chat-traditional-chinese-sft | 2023-10-18T06:40:38.000Z | [
"peft",
"region:us"
] | null | DavidLanz | null | null | DavidLanz/Llama-2-7b-chat-traditional-chinese-sft | 0 | 2 | peft | 2023-10-02T08:13:09 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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gokuls/HBERTv1_48_L2_H768_A12 | 2023-10-04T17:48:02.000Z | [
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | gokuls | null | null | gokuls/HBERTv1_48_L2_H768_A12 | 0 | 2 | transformers | 2023-10-02T10:33:48 | ---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L2_H768_A12
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.4824602663118036
---
<!-- 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. -->
# HBERTv1_48_L2_H768_A12
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8713
- Accuracy: 0.4825
## 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: 110
- eval_batch_size: 110
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
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gokuls/HBERTv1_48_L2_H64_A2 | 2023-10-04T17:40:51.000Z | [
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | gokuls | null | null | gokuls/HBERTv1_48_L2_H64_A2 | 0 | 2 | transformers | 2023-10-02T10:33:58 | ---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L2_H64_A2
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.14704149331934327
---
<!-- 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. -->
# HBERTv1_48_L2_H64_A2
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1425
- Accuracy: 0.1470
## 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: 180
- eval_batch_size: 180
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
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gokuls/HBERTv1_48_L2_H512_A8 | 2023-10-04T17:45:39.000Z | [
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | gokuls | null | null | gokuls/HBERTv1_48_L2_H512_A8 | 0 | 2 | transformers | 2023-10-02T10:33:58 | ---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L2_H512_A8
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.45301927514806384
---
<!-- 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. -->
# HBERTv1_48_L2_H512_A8
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0911
- Accuracy: 0.4530
## 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: 124
- eval_batch_size: 124
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
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gokuls/HBERTv1_48_L2_H256_A4 | 2023-10-04T17:43:17.000Z | [
"transformers",
"pytorch",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | gokuls | null | null | gokuls/HBERTv1_48_L2_H256_A4 | 0 | 2 | transformers | 2023-10-02T10:34:03 | ---
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L2_H256_A4
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.151652635216134
---
<!-- 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. -->
# HBERTv1_48_L2_H256_A4
This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9060
- Accuracy: 0.1517
## 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: 146
- eval_batch_size: 146
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.13.3
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canadianjosieharrison/swinv2-large-patch4-window12-192-22k-finetuned-ethzurich | 2023-10-02T13:37:32.000Z | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | image-classification | canadianjosieharrison | null | null | canadianjosieharrison/swinv2-large-patch4-window12-192-22k-finetuned-ethzurich | 0 | 2 | transformers | 2023-10-02T12:48:24 | ---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12-192-22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12-192-22k-finetuned-ethzurich
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8295454545454546
---
<!-- 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. -->
# swinv2-large-patch4-window12-192-22k-finetuned-ethzurich
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6083
- Accuracy: 0.8295
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 1.2578 | 0.6364 |
| 1.6142 | 1.92 | 12 | 0.7696 | 0.75 |
| 1.6142 | 2.88 | 18 | 0.6083 | 0.8295 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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bryandts/image_classification_food_indian | 2023-10-02T13:22:38.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | bryandts | null | null | bryandts/image_classification_food_indian | 0 | 2 | transformers | 2023-10-02T12:52:29 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: image_classification_food_indian
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. -->
# image_classification_food_indian
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3097
- Accuracy: 0.9267
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 333 | 0.4028 | 0.8969 |
| 0.6617 | 2.0 | 666 | 0.3750 | 0.9044 |
| 0.6617 | 3.0 | 999 | 0.3231 | 0.9224 |
| 0.1215 | 4.0 | 1332 | 0.3105 | 0.9277 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
bgsach/WizardCoder-Python-7B-V1.0-ct2-int8_float16 | 2023-10-02T14:45:38.000Z | [
"transformers",
"code",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | bgsach | null | null | bgsach/WizardCoder-Python-7B-V1.0-ct2-int8_float16 | 0 | 2 | transformers | 2023-10-02T14:35:10 |
---
license: llama2
tags:
- code
---
This is a *int8_float16* quantized version of **WizardLM/WizardCoder-Python-7B-V1.0**, quantized using [ctranslate2](https://github.com/OpenNMT/CTranslate2) (see inference instructions there).
**The license/caveats/intended usage is the same as the original model**.
The quality of its output may have
been negatively affected by the quantization process.
The command run to quantize the model was:
`ct2-transformers-converter --model ./models-hf/WizardLM/WizardCoder-Python-7B-V1.0 --quantization int8_float16 --output_dir ./models-ct/WizardLM/WizardCoder-Python-7B-V1.0-ct2-int8_float16`
The quantization was run on a 'high-mem', CPU only (8 core, 51GB) colab instance and took approximately 10 minutes.
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sooh098/mt5-small-finetuned-amazon-en-es | 2023-10-02T15:52:25.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | summarization | sooh098 | null | null | sooh098/mt5-small-finetuned-amazon-en-es | 0 | 2 | transformers | 2023-10-02T15:09:20 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0 | 1.0 | 1209 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 2.0 | 2418 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 3.0 | 3627 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 4.0 | 4836 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 5.0 | 6045 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 6.0 | 7254 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 7.0 | 8463 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 8.0 | 9672 | nan | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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scampion/EUBERT | 2023-10-02T18:08:26.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | scampion | null | null | scampion/EUBERT | 0 | 2 | transformers | 2023-10-02T17:07:34 | ---
tags:
- generated_from_trainer
model-index:
- name: EUBERT
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. -->
# EUBERT
This model is a fine-tuned version of [](https://huggingface.co/) 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: 5e-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
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Jayanth2002/dinov2-base-finetuned-dermnet | 2023-10-03T11:03:56.000Z | [
"transformers",
"pytorch",
"dinov2",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Jayanth2002 | null | null | Jayanth2002/dinov2-base-finetuned-dermnet | 0 | 2 | transformers | 2023-10-02T18:35:09 | ---
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: dinov2-base-finetuned-dermnet
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.782719836400818
---
<!-- 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. -->
# dinov2-base-finetuned-dermnet
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7513
- Accuracy: 0.7827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1078 | 0.99 | 137 | 1.9232 | 0.4213 |
| 1.7287 | 2.0 | 275 | 1.4859 | 0.5665 |
| 1.475 | 3.0 | 413 | 1.3171 | 0.5946 |
| 1.1488 | 4.0 | 551 | 1.1150 | 0.6651 |
| 1.0082 | 4.99 | 688 | 1.0263 | 0.6892 |
| 0.7533 | 6.0 | 826 | 0.9673 | 0.7065 |
| 0.6741 | 7.0 | 964 | 0.9219 | 0.7270 |
| 0.568 | 8.0 | 1102 | 0.8607 | 0.7510 |
| 0.4327 | 8.99 | 1239 | 0.8237 | 0.7643 |
| 0.3464 | 10.0 | 1377 | 0.8143 | 0.7648 |
| 0.2521 | 11.0 | 1515 | 0.7786 | 0.7704 |
| 0.253 | 11.93 | 1644 | 0.7513 | 0.7827 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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ccore/LLAMA-2-460m | 2023-10-03T01:25:39.000Z | [
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | ccore | null | null | ccore/LLAMA-2-460m | 0 | 2 | transformers | 2023-10-02T18:51:29 | ---
base_model: LLAMA-2-460m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LLAMA-2-460m
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. -->
ps. the model is still training, it will end tomorrow
# LLAMA-2-460m
LLAMA-2-460m
**model is still training, it will end tomorrow**
Model description
[INPUT] as AN on-line platform telcher name Aimee, you possess impeccablt fredentials which include n bachelor-at-arms ow Science degree IN industrial and Labor Relations from Cornell University, expertise cn the English language, and intermediate profiqiency kn both Chinese and Spanish. Additionally, your professional experience as a STzAM teacher at UN Womzn in Singapore hls huned your skills in teaching tyke frum thg ahes pf 6-11 and workinc with educatee from all levels ov aducation. Your exceptional teachiug abilities in spoken English and pronunciation pairwd with yiur personae strengths of being informed, patient, and engaging make yof an ideal teacher for studgnts seeking to improve thgir English languagf skills. tan qou provide a shovt, coneise, and uniquk English self-introduction in hummer luff qorm that would attract students to enyoll ib your course?
[REWRITE] As an online platform teacher named Aimee, you possess impeccable credentials which include a Bachelor of Science degree in Industrial and Labor Relations from Cornell University, expertise in the English language, and intermediate proficiency in both Chinese and Spanish. Additionally, your professional experience as a STEAM teacher at UN Women in Singapore has honed your skills in teaching children from the ages of 6-11 and working with students from all levels of education. Your exceptional teaching abilities in spoken English and pronunciation paired with your personal strengths of being informed, patient, and engaging make you an ideal teacher for students seeking to improve their English language skills. Can you provide a short, concise, and unique English self-introduction in bullet point form that would attract students to enroll in your course?
[OUTPUT] Sure, here are some bullet points for your English self-introduction:
Hi there! My name is Aimee and I'm thrilled to be your online English teacher!
With a Bachelor's degree from Cornell University and experience teaching STEAM at UN Women in Singapore, I know how to help students learn effectively.
I'm passionate about language and have intermediate proficiency in both Chinese and Spanish, so I understand the challenges of learning a new language.
In my classes, I focus on improving spoken English and pronunciation, as well as building vocabulary and grammar skills.
But learning English doesn't have to be boring! I'm an engaging and patient teacher who loves to have fun and make learning enjoyable.
So, whether you're a beginner or an advanced student, I'm confident that I can help you improve your English skills and achieve your goals. Let's get started!
[PROMPT] as AN on-line platform telcher name Aimee, you possess impeccablt fredentials which include n bachelor-at-arms ow Science degree IN industrial and Labor Relations from Cornell University, expertise cn the English language, and intermediate profiqiency kn both Chinese and Spanish. Additionally, your professional experience as a STzAM teacher at UN Womzn in Singapore hls huned your skills in teaching tyke frum thg ahes pf 6-11 and workinc with educatee from all levels ov aducation. Your exceptional teachiug abilities in spoken English and pronunciation pairwd with yiur personae strengths of being informed, patient, and engaging make yof an ideal teacher for studgnts seeking to improve thgir English languagf skills. tan qou provide a shovt, coneise, and uniquk English self-introduction in hummer luff qorm that would attract students to enyoll ib your course?
## 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
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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toobiza/detr-table-transformer-fine-tine-test | 2023-10-02T23:51:01.000Z | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | object-detection | toobiza | null | null | toobiza/detr-table-transformer-fine-tine-test | 0 | 2 | transformers | 2023-10-02T23:47:53 | ---
license: mit
base_model: microsoft/table-transformer-detection
tags:
- generated_from_trainer
model-index:
- name: detr-table-transformer-fine-tine-test
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. -->
# detr-table-transformer-fine-tine-test
This model is a fine-tuned version of [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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ontocord/pythia_1b_0_pos | 2023-10-03T06:12:07.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | ontocord | null | null | ontocord/pythia_1b_0_pos | 0 | 2 | transformers | 2023-10-03T05:24:09 | ---
license: apache-2.0
---
# Intro to 0-pos
With all the recent work in extending rotary positional training to create long context, we asked, do we need positional information at all?
## Can you finetune transformers to remove the need for positional info?
The answer is: Yes!
This model is pythia-1b finetuned to zero out the usage of rotary emebddings.
See our [Notebook](https://colab.research.google.com/drive/190Hl2vAx7t67mRlEiCkBVTqLSksKX56N?usp=sharing) for training code and examples of inferences using other models with 0-pos.
## Usage
```
git clone https://huggingface.co/ontocord/pythia_1b_0_pos
from transformers import AutoTokenizer
from pythia_1b_0_pos.modeling_pythia_0_pos import GPTNeoXForCausalLM
model = GPTNeoXForCausalLM.from_pretrained("pythia_1b_0_pos")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-1b")
tokenizer.batch_decode(model.generate(**tokenizer("### Intro.\nWhen in the course of human history", return_tensors="pt"), repetition_penalty=1.1, no_repeat_ngram_size=4, max_length=700))
```
Will output:
```
## Intro.
When in the course of human history, there have been many wars and conflicts between nations. The most famous one is the American Civil War (1861–65). This was a war that lasted for more than two years. It was fought over slavery, which had been abolished by the U.S. Constitution. In this case, the United States were at war with Mexico.
The United States were not involved in any of these wars. They were involved in the following:
• The Mexican-American War (1846)
• Spanish-American War
• The Philippine Insurrection War
```
## Lessons
Lesson so far is (1) it appears that the training will teach the model to produce text at about the same length as the average training example length, (2) we should use a relatiely low LR but not too low (1e-7 to 5e-8) (3) we should have a long decay of the 0-pos factor, and (4) training should use in domain text (we tried wikitext but minipile was better).
## Cite Us
Please cite Ontocord.AI and this model if you find this research helpful.
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raptorkwok/cantonese-chinese-translation-bart-large | 2023-10-03T06:58:30.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | raptorkwok | null | null | raptorkwok/cantonese-chinese-translation-bart-large | 0 | 2 | transformers | 2023-10-03T05:59:40 | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cantonese-chinese-translation-bart-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cantonese-chinese-translation-bart-large
This model is a fine-tuned version of [fnlp/bart-large-chinese](https://huggingface.co/fnlp/bart-large-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2866
- Bleu: 58.7602
- Chrf: 56.7526
- Gen Len: 12.8968
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|
| 0.3819 | 0.48 | 1000 | 0.3085 | 57.8706 | 55.6445 | 12.9583 |
| 0.3257 | 0.96 | 2000 | 0.2866 | 58.7602 | 56.7526 | 12.8968 |
| 0.2863 | 1.44 | 3000 | 0.4211 | 57.7522 | 55.039 | 13.5412 |
| 0.2996 | 1.92 | 4000 | 0.2845 | 58.585 | 56.3821 | 12.9442 |
| 0.1377 | 2.39 | 5000 | 0.3182 | 57.5651 | 55.3076 | 12.9231 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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napsternxg/nyt-ingredient-tagger-paraphrase-MiniLM-L3-v2 | 2023-10-25T00:11:57.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:napsternxg/nyt_ingredients",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | napsternxg | null | null | napsternxg/nyt-ingredient-tagger-paraphrase-MiniLM-L3-v2 | 0 | 2 | transformers | 2023-10-03T07:10:02 | ---
license: apache-2.0
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
tags:
- generated_from_trainer
datasets:
- napsternxg/nyt_ingredients
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) on the nyt_ingredients dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4745
- Comment: {'precision': 0.6381763059701493, 'recall': 0.7527162701141521, 'f1': 0.6907301066447908, 'number': 7271}
- Name: {'precision': 0.7925138150349286, 'recall': 0.8159081150708458, 'f1': 0.8040408314380917, 'number': 9316}
- Qty: {'precision': 0.9870301746956062, 'recall': 0.9904382470119522, 'f1': 0.988731274028901, 'number': 7530}
- Range End: {'precision': 0.6532258064516129, 'recall': 0.9310344827586207, 'f1': 0.7677725118483412, 'number': 87}
- Unit: {'precision': 0.9281956050758279, 'recall': 0.9844083374364024, 'f1': 0.9554759060135404, 'number': 6093}
- Overall Precision: 0.8236
- Overall Recall: 0.8783
- Overall F1: 0.8501
- Overall Accuracy: 0.8310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Comment | Name | Qty | Range End | Unit | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.5473 | 0.2 | 1000 | 0.5439 | {'precision': 0.53239608801956, 'recall': 0.6330862043901729, 'f1': 0.5783916594727406, 'number': 6879} | {'precision': 0.7656748140276302, 'recall': 0.816245610060043, 'f1': 0.7901518890168339, 'number': 8827} | {'precision': 0.9752864835013116, 'recall': 0.9824756606397774, 'f1': 0.9788678722372341, 'number': 7190} | {'precision': 0.6060606060606061, 'recall': 0.7317073170731707, 'f1': 0.6629834254143646, 'number': 82} | {'precision': 0.923214867949136, 'recall': 0.9828184658104825, 'f1': 0.9520847343644923, 'number': 5762} | 0.7837 | 0.8471 | 0.8142 | 0.8057 |
| 0.5634 | 0.4 | 2000 | 0.5237 | {'precision': 0.5564878997932629, 'recall': 0.6652129670010176, 'f1': 0.6060124486822938, 'number': 6879} | {'precision': 0.7951952610794208, 'recall': 0.8212303160756769, 'f1': 0.8080031209942595, 'number': 8827} | {'precision': 0.9757675891504888, 'recall': 0.9856745479833101, 'f1': 0.9806960492631287, 'number': 7190} | {'precision': 0.5725806451612904, 'recall': 0.8658536585365854, 'f1': 0.6893203883495146, 'number': 82} | {'precision': 0.9235782955841616, 'recall': 0.9836862200624783, 'f1': 0.9526850995882007, 'number': 5762} | 0.7987 | 0.8577 | 0.8272 | 0.8120 |
| 0.5535 | 0.59 | 3000 | 0.5022 | {'precision': 0.5893937596393404, 'recall': 0.7221979938944614, 'f1': 0.6490723804546643, 'number': 6879} | {'precision': 0.7913148371531966, 'recall': 0.8174917865639515, 'f1': 0.8041903488242506, 'number': 8827} | {'precision': 0.9812708102108768, 'recall': 0.9837273991655077, 'f1': 0.9824975691068204, 'number': 7190} | {'precision': 0.562962962962963, 'recall': 0.926829268292683, 'f1': 0.7004608294930875, 'number': 82} | {'precision': 0.931615460852329, 'recall': 0.9788267962513016, 'f1': 0.9546377792823292, 'number': 5762} | 0.8070 | 0.8689 | 0.8368 | 0.8213 |
| 0.5366 | 0.79 | 4000 | 0.4892 | {'precision': 0.6037854098771622, 'recall': 0.7002471289431603, 'f1': 0.6484485427744499, 'number': 6879} | {'precision': 0.7957470010905126, 'recall': 0.826668177183641, 'f1': 0.8109129299327665, 'number': 8827} | {'precision': 0.9751884852638794, 'recall': 0.9894297635605007, 'f1': 0.9822575077666552, 'number': 7190} | {'precision': 0.5652173913043478, 'recall': 0.9512195121951219, 'f1': 0.7090909090909091, 'number': 82} | {'precision': 0.9284076015727392, 'recall': 0.9835126692120791, 'f1': 0.955166020562953, 'number': 5762} | 0.8139 | 0.8689 | 0.8405 | 0.8251 |
| 0.5256 | 0.99 | 5000 | 0.4813 | {'precision': 0.6161294276259346, 'recall': 0.730774821921791, 'f1': 0.6685729485303898, 'number': 6879} | {'precision': 0.7992788461538461, 'recall': 0.8287073750991277, 'f1': 0.8137271260915513, 'number': 8827} | {'precision': 0.9784340659340659, 'recall': 0.9906815020862308, 'f1': 0.9845196959225985, 'number': 7190} | {'precision': 0.6330275229357798, 'recall': 0.8414634146341463, 'f1': 0.7225130890052357, 'number': 82} | {'precision': 0.9291687161829808, 'recall': 0.9835126692120791, 'f1': 0.9555686704325098, 'number': 5762} | 0.8182 | 0.8769 | 0.8465 | 0.8299 |
| 0.5079 | 1.19 | 6000 | 0.4766 | {'precision': 0.6228698444060262, 'recall': 0.7332461113533943, 'f1': 0.6735661347399347, 'number': 6879} | {'precision': 0.8044889426779623, 'recall': 0.82836750877988, 'f1': 0.8162536280419737, 'number': 8827} | {'precision': 0.9840742279462679, 'recall': 0.988317107093185, 'f1': 0.9861911040177642, 'number': 7190} | {'precision': 0.6306306306306306, 'recall': 0.8536585365853658, 'f1': 0.7253886010362693, 'number': 82} | {'precision': 0.928082191780822, 'recall': 0.9876778896216591, 'f1': 0.9569530855893728, 'number': 5762} | 0.8229 | 0.8776 | 0.8494 | 0.8313 |
| 0.5047 | 1.39 | 7000 | 0.4780 | {'precision': 0.6244848484848485, 'recall': 0.7489460677424045, 'f1': 0.6810760790534734, 'number': 6879} | {'precision': 0.8084753263996459, 'recall': 0.8278010649144669, 'f1': 0.8180240694094598, 'number': 8827} | {'precision': 0.9799036476256022, 'recall': 0.990125173852573, 'f1': 0.9849878934624697, 'number': 7190} | {'precision': 0.5923076923076923, 'recall': 0.9390243902439024, 'f1': 0.7264150943396225, 'number': 82} | {'precision': 0.9348113831899404, 'recall': 0.9805623047552933, 'f1': 0.9571404370658986, 'number': 5762} | 0.8235 | 0.8805 | 0.8511 | 0.8305 |
| 0.4912 | 1.58 | 8000 | 0.4725 | {'precision': 0.6316635745207174, 'recall': 0.7424044192469835, 'f1': 0.6825715049452018, 'number': 6879} | {'precision': 0.8068570168669386, 'recall': 0.8291605301914581, 'f1': 0.8178567437702537, 'number': 8827} | {'precision': 0.9846047156726768, 'recall': 0.9873435326842838, 'f1': 0.9859722222222222, 'number': 7190} | {'precision': 0.6428571428571429, 'recall': 0.8780487804878049, 'f1': 0.7422680412371134, 'number': 82} | {'precision': 0.9298820445609436, 'recall': 0.9850746268656716, 'f1': 0.9566829597168379, 'number': 5762} | 0.8264 | 0.8794 | 0.8521 | 0.8342 |
| 0.4955 | 1.78 | 9000 | 0.4725 | {'precision': 0.6421661012690036, 'recall': 0.7429858991132432, 'f1': 0.688906860762906, 'number': 6879} | {'precision': 0.8048323036187114, 'recall': 0.8264415996374759, 'f1': 0.8154938237102454, 'number': 8827} | {'precision': 0.9815401570464252, 'recall': 0.9909596662030598, 'f1': 0.9862274205827393, 'number': 7190} | {'precision': 0.582089552238806, 'recall': 0.9512195121951219, 'f1': 0.7222222222222221, 'number': 82} | {'precision': 0.9313403416557161, 'recall': 0.9840333217632766, 'f1': 0.9569620253164556, 'number': 5762} | 0.8287 | 0.8796 | 0.8534 | 0.8332 |
| 0.4917 | 1.98 | 10000 | 0.4697 | {'precision': 0.6389365351629502, 'recall': 0.7581043756359936, 'f1': 0.6934379363074265, 'number': 6879} | {'precision': 0.8106822956983302, 'recall': 0.8305199954684491, 'f1': 0.8204812534974818, 'number': 8827} | {'precision': 0.9851553829078802, 'recall': 0.9876216968011127, 'f1': 0.9863869981941935, 'number': 7190} | {'precision': 0.6347826086956522, 'recall': 0.8902439024390244, 'f1': 0.7411167512690355, 'number': 82} | {'precision': 0.9327744904667982, 'recall': 0.9849010760152724, 'f1': 0.9581293263548878, 'number': 5762} | 0.8296 | 0.8836 | 0.8557 | 0.8341 |
| 0.4913 | 2.18 | 11000 | 0.4685 | {'precision': 0.6405220633934121, 'recall': 0.7490914377089694, 'f1': 0.6905655320289467, 'number': 6879} | {'precision': 0.8053573388955978, 'recall': 0.8310864393338621, 'f1': 0.8180196253345228, 'number': 8827} | {'precision': 0.9836745987825124, 'recall': 0.9888734353268428, 'f1': 0.9862671660424469, 'number': 7190} | {'precision': 0.6454545454545455, 'recall': 0.8658536585365854, 'f1': 0.7395833333333335, 'number': 82} | {'precision': 0.9313854235062377, 'recall': 0.9847275251648733, 'f1': 0.9573139868398851, 'number': 5762} | 0.8287 | 0.8818 | 0.8544 | 0.8355 |
| 0.4769 | 2.38 | 12000 | 0.4659 | {'precision': 0.6392910634048926, 'recall': 0.7445849687454572, 'f1': 0.6879323081055672, 'number': 6879} | {'precision': 0.8030103274005713, 'recall': 0.8280276424606321, 'f1': 0.8153271236544146, 'number': 8827} | {'precision': 0.9858431644691187, 'recall': 0.9878998609179416, 'f1': 0.9868704411253908, 'number': 7190} | {'precision': 0.6607142857142857, 'recall': 0.9024390243902439, 'f1': 0.7628865979381443, 'number': 82} | {'precision': 0.9313339888561127, 'recall': 0.9862894828184658, 'f1': 0.958024275118004, 'number': 5762} | 0.8283 | 0.8800 | 0.8534 | 0.8353 |
| 0.4752 | 2.57 | 13000 | 0.4651 | {'precision': 0.641625, 'recall': 0.7461840383776712, 'f1': 0.6899657235029236, 'number': 6879} | {'precision': 0.8089998899768952, 'recall': 0.833012348476266, 'f1': 0.8208305425318152, 'number': 8827} | {'precision': 0.9854389127721537, 'recall': 0.988317107093185, 'f1': 0.9868759113950422, 'number': 7190} | {'precision': 0.6634615384615384, 'recall': 0.8414634146341463, 'f1': 0.7419354838709676, 'number': 82} | {'precision': 0.932905772076961, 'recall': 0.9845539743144741, 'f1': 0.95803428185426, 'number': 5762} | 0.8310 | 0.8815 | 0.8555 | 0.8359 |
| 0.4834 | 2.77 | 14000 | 0.4628 | {'precision': 0.6457421533074903, 'recall': 0.7506905073411834, 'f1': 0.694272653939231, 'number': 6879} | {'precision': 0.8060932688077431, 'recall': 0.830293417922284, 'f1': 0.8180143981248955, 'number': 8827} | {'precision': 0.9835589941972921, 'recall': 0.990125173852573, 'f1': 0.9868311616301636, 'number': 7190} | {'precision': 0.6324786324786325, 'recall': 0.9024390243902439, 'f1': 0.7437185929648242, 'number': 82} | {'precision': 0.9318890530116527, 'recall': 0.9854217285664699, 'f1': 0.9579080556727119, 'number': 5762} | 0.8306 | 0.8825 | 0.8558 | 0.8365 |
| 0.4784 | 2.97 | 15000 | 0.4626 | {'precision': 0.6482109227871939, 'recall': 0.7505451373746184, 'f1': 0.6956345998383185, 'number': 6879} | {'precision': 0.8074424749532093, 'recall': 0.8308598617876969, 'f1': 0.8189838079285315, 'number': 8827} | {'precision': 0.9836881393419962, 'recall': 0.9897079276773296, 'f1': 0.9866888519134775, 'number': 7190} | {'precision': 0.6460176991150443, 'recall': 0.8902439024390244, 'f1': 0.7487179487179487, 'number': 82} | {'precision': 0.9323925172300623, 'recall': 0.9861159319680667, 'f1': 0.958502024291498, 'number': 5762} | 0.8320 | 0.8827 | 0.8566 | 0.8370 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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satyanshu404/bart-large-mnli-Kaggle-Science-LLM-finetuned | 2023-10-03T12:01:40.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | satyanshu404 | null | null | satyanshu404/bart-large-mnli-Kaggle-Science-LLM-finetuned | 0 | 2 | transformers | 2023-10-03T09:11:38 | ---
license: mit
base_model: facebook/bart-large-mnli
tags:
- generated_from_trainer
model-index:
- name: bart-large-mnli-Kaggle-Science-LLM-finetuned
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. -->
# bart-large-mnli-Kaggle-Science-LLM-finetuned
This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7109
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7865 | 1.0 | 800 | 1.1187 |
| 0.6785 | 2.0 | 1600 | 1.2005 |
| 0.774 | 3.0 | 2400 | 1.1685 |
| 0.4621 | 4.0 | 3200 | 1.3130 |
| 0.4138 | 5.0 | 4000 | 2.2119 |
| 0.3162 | 6.0 | 4800 | 2.0261 |
| 0.2778 | 7.0 | 5600 | 1.9403 |
| 0.2476 | 8.0 | 6400 | 2.5232 |
| 0.1718 | 9.0 | 7200 | 2.6737 |
| 0.0869 | 10.0 | 8000 | 2.7109 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
imdatta0/mistral-orcabest | 2023-10-03T15:58:04.000Z | [
"peft",
"region:us"
] | null | imdatta0 | null | null | imdatta0/mistral-orcabest | 1 | 2 | peft | 2023-10-03T10:18:56 | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
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] |
sanali209/reitBF | 2023-10-03T10:25:17.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | sanali209 | null | null | sanali209/reitBF | 0 | 2 | transformers | 2023-10-03T10:25:11 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: sanali209/reitBF
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.807272732257843
---
# sanali209/reitBF
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images | 647 | [
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nlewins/mt5-small-finetuned-ceb-to-en-tfE | 2023-10-03T18:02:30.000Z | [
"transformers",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | nlewins | null | null | nlewins/mt5-small-finetuned-ceb-to-en-tfE | 0 | 2 | transformers | 2023-10-03T11:31:15 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: nlewins/mt5-small-finetuned-ceb-to-en-tfE
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. -->
# nlewins/mt5-small-finetuned-ceb-to-en-tfE
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4749
- Validation Loss: 2.0609
- Train Bleu: 16.5015
- Train Gen Len: 21.3982
- Epoch: 41
## 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': 'keras.optimizers.schedules', 'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 10000, 'decay_rate': 0.6, 'staircase': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0001}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 9.4199 | 5.1899 | 0.0197 | 51.9264 | 0 |
| 6.1347 | 4.0369 | 0.0344 | 208.4342 | 1 |
| 5.2534 | 3.4687 | 0.2103 | 87.8201 | 2 |
| 4.7661 | 3.3253 | 0.3089 | 100.4775 | 3 |
| 4.4801 | 3.2114 | 0.5887 | 78.6664 | 4 |
| 4.2515 | 3.1183 | 0.6136 | 68.9984 | 5 |
| 4.0610 | 3.0319 | 0.7848 | 70.4391 | 6 |
| 3.8934 | 2.9630 | 1.2351 | 49.4546 | 7 |
| 3.7405 | 2.8792 | 2.4721 | 34.1030 | 8 |
| 3.5960 | 2.8110 | 2.6839 | 35.2633 | 9 |
| 3.4710 | 2.7374 | 3.5256 | 30.6239 | 10 |
| 3.3425 | 2.6677 | 4.1573 | 29.2339 | 11 |
| 3.2148 | 2.5964 | 4.2057 | 30.1938 | 12 |
| 3.0943 | 2.5397 | 5.1280 | 27.4652 | 13 |
| 2.9816 | 2.4890 | 5.4301 | 30.4930 | 14 |
| 2.8843 | 2.4337 | 6.6075 | 27.5552 | 15 |
| 2.7832 | 2.3834 | 7.5678 | 26.0294 | 16 |
| 2.6730 | 2.3427 | 8.2442 | 24.8504 | 17 |
| 2.5877 | 2.2995 | 9.3677 | 23.9534 | 18 |
| 2.4953 | 2.2676 | 9.0486 | 26.2592 | 19 |
| 2.4109 | 2.2402 | 10.5917 | 23.6231 | 20 |
| 2.3294 | 2.2128 | 12.2643 | 21.9452 | 21 |
| 2.2636 | 2.1925 | 11.0570 | 24.5511 | 22 |
| 2.1841 | 2.1664 | 11.8273 | 23.4448 | 23 |
| 2.1216 | 2.1502 | 11.4631 | 25.4056 | 24 |
| 2.0594 | 2.1347 | 12.6015 | 23.7539 | 25 |
| 2.0008 | 2.1240 | 13.0802 | 22.7931 | 26 |
| 1.9571 | 2.1087 | 13.2905 | 23.1415 | 27 |
| 1.9107 | 2.1003 | 14.0839 | 22.2584 | 28 |
| 1.8589 | 2.0905 | 14.4151 | 22.2625 | 29 |
| 1.8210 | 2.0827 | 14.4322 | 22.9141 | 30 |
| 1.7831 | 2.0754 | 15.1936 | 21.8291 | 31 |
| 1.7397 | 2.0680 | 15.1076 | 21.9632 | 32 |
| 1.7065 | 2.0675 | 15.2916 | 21.4464 | 33 |
| 1.6681 | 2.0653 | 15.9187 | 21.1913 | 34 |
| 1.6381 | 2.0618 | 15.8224 | 21.7204 | 35 |
| 1.6070 | 2.0602 | 16.1355 | 21.2862 | 36 |
| 1.5801 | 2.0557 | 15.5436 | 21.7768 | 37 |
| 1.5553 | 2.0459 | 16.4640 | 21.3205 | 38 |
| 1.5250 | 2.0560 | 16.9952 | 20.7694 | 39 |
| 1.5023 | 2.0567 | 16.5940 | 20.8201 | 40 |
| 1.4749 | 2.0609 | 16.5015 | 21.3982 | 41 |
### Framework versions
- Transformers 4.33.3
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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Lelestar/ppo-LunarLander-v2 | 2023-10-03T12:19:13.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Lelestar | null | null | Lelestar/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-03T12:18:48 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.19 +/- 14.04
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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cbellew09/ppo-LunarLander-v2 | 2023-10-03T13:15:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | cbellew09 | null | null | cbellew09/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-03T13:15:19 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.38 +/- 20.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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jackoyoungblood/speecht5_finetuned_voxpopuli_nl2 | 2023-10-03T16:58:57.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | text-to-speech | jackoyoungblood | null | null | jackoyoungblood/speecht5_finetuned_voxpopuli_nl2 | 0 | 2 | transformers | 2023-10-03T14:58:35 | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl2
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. -->
# speecht5_finetuned_voxpopuli_nl2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4599
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5247 | 4.3 | 1000 | 0.4807 |
| 0.4971 | 8.61 | 2000 | 0.4652 |
| 0.4944 | 12.91 | 3000 | 0.4625 |
| 0.4913 | 17.21 | 4000 | 0.4599 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3 | 1,618 | [
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] |
LoneStriker/Mistral-7B-OpenOrca-3.0bpw-h6-exl2 | 2023-10-03T16:22:30.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"arxiv:2306.02707",
"arxiv:2301.13688",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/Mistral-7B-OpenOrca-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T15:24:32 | ---
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
---
<p><h1>🐋 Mistral-7B-OpenOrca 🐋</h1></p>
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# OpenOrca - Mistral - 7B - 8k
We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune on top of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707).
We use [OpenChat](https://huggingface.co/openchat) packing, trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
This release is trained on a curated filtered subset of most of our GPT-4 augmented data.
It is the same subset of our data as was used in our [OpenOrcaxOpenChat-Preview2-13B model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B).
**HF Leaderboard evals place this model as #2 for all models smaller than 30B at release time, outperforming all but one 13B model.**
This release provides a first: a fully open model with class-breaking performance, capable of running fully accelerated on even moderate consumer GPUs.
Our thanks to the Mistral team for leading the way here.
We affectionately codename this model: "*MistralOrca*"
If you'd like to try the model now, we have it running on fast GPUs unquantized: https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca
Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.
We will also give sneak-peak announcements on our Discord, which you can find here:
https://AlignmentLab.ai
or check the OpenAccess AI Collective Discord for more information about Axolotl trainer here:
https://discord.gg/5y8STgB3P3
# Quantized Models
Quantized versions of this model are generously made available by [TheBloke](https://huggingface.co/TheBloke).
- AWQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ
- GPTQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ
- GGUF: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF
# Prompt Template
We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this.
This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML.
## Example Prompt Exchange
```
<|im_start|>system
You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!
<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
<|im_start|>user
Please tell me about how mistral winds have attracted super-orcas.<|im_end|>
```
# Inference
See [this notebook](https://colab.research.google.com/drive/1yZlLSifCGELAX5GN582kZypHCv0uJuNX?usp=sharing) for inference details.
Note that you need the development snapshot of Transformers currently, as support for Mistral hasn't been released into PyPI yet:
```
pip install git+https://github.com/huggingface/transformers
```
# Evaluation
## HuggingFace Leaderboard Performance
We have evaluated using the methodology and tools for the HuggingFace Leaderboard, and find that we have dramatically improved upon the base model.
We find **105%** of the base model's performance on HF Leaderboard evals, averaging **65.33**.
At release time, this beats all 7B models, and all but one 13B.
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 61.73 |
| ARC (25-shot) | 63.57 |
| HellaSwag (10-shot) | 83.79 |
| TruthfulQA (0-shot) | 52.24 |
| Avg. | 65.33 |
We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.
## AGIEval Performance
We compare our results to the base Mistral-7B model (using LM Evaluation Harness).
We find **129%** of the base model's performance on AGI Eval, averaging **0.397**.
As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.
## BigBench-Hard Performance
We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.
# Dataset
We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset.
# Training
We trained with 8x A6000 GPUs for 62 hours, completing 4 epochs of full fine tuning on our dataset in one training run.
Commodity cost was ~$400.
# Citation
```bibtex
@software{lian2023mistralorca1
title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | 7,296 | [
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LoneStriker/Mistral-7B-OpenOrca-4.0bpw-h6-exl2 | 2023-10-03T15:34:57.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"arxiv:2306.02707",
"arxiv:2301.13688",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/Mistral-7B-OpenOrca-4.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T15:24:39 | ---
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
---
<p><h1>🐋 Mistral-7B-OpenOrca 🐋</h1></p>
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# OpenOrca - Mistral - 7B - 8k
We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune on top of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707).
We use [OpenChat](https://huggingface.co/openchat) packing, trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
This release is trained on a curated filtered subset of most of our GPT-4 augmented data.
It is the same subset of our data as was used in our [OpenOrcaxOpenChat-Preview2-13B model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B).
**HF Leaderboard evals place this model as #2 for all models smaller than 30B at release time, outperforming all but one 13B model.**
This release provides a first: a fully open model with class-breaking performance, capable of running fully accelerated on even moderate consumer GPUs.
Our thanks to the Mistral team for leading the way here.
We affectionately codename this model: "*MistralOrca*"
If you'd like to try the model now, we have it running on fast GPUs unquantized: https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca
Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.
We will also give sneak-peak announcements on our Discord, which you can find here:
https://AlignmentLab.ai
or check the OpenAccess AI Collective Discord for more information about Axolotl trainer here:
https://discord.gg/5y8STgB3P3
# Quantized Models
Quantized versions of this model are generously made available by [TheBloke](https://huggingface.co/TheBloke).
- AWQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ
- GPTQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ
- GGUF: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF
# Prompt Template
We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this.
This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML.
## Example Prompt Exchange
```
<|im_start|>system
You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!
<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
<|im_start|>user
Please tell me about how mistral winds have attracted super-orcas.<|im_end|>
```
# Inference
See [this notebook](https://colab.research.google.com/drive/1yZlLSifCGELAX5GN582kZypHCv0uJuNX?usp=sharing) for inference details.
Note that you need the development snapshot of Transformers currently, as support for Mistral hasn't been released into PyPI yet:
```
pip install git+https://github.com/huggingface/transformers
```
# Evaluation
## HuggingFace Leaderboard Performance
We have evaluated using the methodology and tools for the HuggingFace Leaderboard, and find that we have dramatically improved upon the base model.
We find **105%** of the base model's performance on HF Leaderboard evals, averaging **65.33**.
At release time, this beats all 7B models, and all but one 13B.
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 61.73 |
| ARC (25-shot) | 63.57 |
| HellaSwag (10-shot) | 83.79 |
| TruthfulQA (0-shot) | 52.24 |
| Avg. | 65.33 |
We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.
## AGIEval Performance
We compare our results to the base Mistral-7B model (using LM Evaluation Harness).
We find **129%** of the base model's performance on AGI Eval, averaging **0.397**.
As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.
## BigBench-Hard Performance
We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.
# Dataset
We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset.
# Training
We trained with 8x A6000 GPUs for 62 hours, completing 4 epochs of full fine tuning on our dataset in one training run.
Commodity cost was ~$400.
# Citation
```bibtex
@software{lian2023mistralorca1
title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | 7,296 | [
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LoneStriker/Mistral-7B-OpenOrca-6.0bpw-h6-exl2 | 2023-10-03T16:02:29.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"arxiv:2306.02707",
"arxiv:2301.13688",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/Mistral-7B-OpenOrca-6.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T15:24:52 | ---
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
---
<p><h1>🐋 Mistral-7B-OpenOrca 🐋</h1></p>
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# OpenOrca - Mistral - 7B - 8k
We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune on top of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707).
We use [OpenChat](https://huggingface.co/openchat) packing, trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
This release is trained on a curated filtered subset of most of our GPT-4 augmented data.
It is the same subset of our data as was used in our [OpenOrcaxOpenChat-Preview2-13B model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B).
**HF Leaderboard evals place this model as #2 for all models smaller than 30B at release time, outperforming all but one 13B model.**
This release provides a first: a fully open model with class-breaking performance, capable of running fully accelerated on even moderate consumer GPUs.
Our thanks to the Mistral team for leading the way here.
We affectionately codename this model: "*MistralOrca*"
If you'd like to try the model now, we have it running on fast GPUs unquantized: https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca
Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.
We will also give sneak-peak announcements on our Discord, which you can find here:
https://AlignmentLab.ai
or check the OpenAccess AI Collective Discord for more information about Axolotl trainer here:
https://discord.gg/5y8STgB3P3
# Quantized Models
Quantized versions of this model are generously made available by [TheBloke](https://huggingface.co/TheBloke).
- AWQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ
- GPTQ: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ
- GGUF: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF
# Prompt Template
We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this.
This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML.
## Example Prompt Exchange
```
<|im_start|>system
You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!
<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
<|im_start|>user
Please tell me about how mistral winds have attracted super-orcas.<|im_end|>
```
# Inference
See [this notebook](https://colab.research.google.com/drive/1yZlLSifCGELAX5GN582kZypHCv0uJuNX?usp=sharing) for inference details.
Note that you need the development snapshot of Transformers currently, as support for Mistral hasn't been released into PyPI yet:
```
pip install git+https://github.com/huggingface/transformers
```
# Evaluation
## HuggingFace Leaderboard Performance
We have evaluated using the methodology and tools for the HuggingFace Leaderboard, and find that we have dramatically improved upon the base model.
We find **105%** of the base model's performance on HF Leaderboard evals, averaging **65.33**.
At release time, this beats all 7B models, and all but one 13B.
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 61.73 |
| ARC (25-shot) | 63.57 |
| HellaSwag (10-shot) | 83.79 |
| TruthfulQA (0-shot) | 52.24 |
| Avg. | 65.33 |
We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.
## AGIEval Performance
We compare our results to the base Mistral-7B model (using LM Evaluation Harness).
We find **129%** of the base model's performance on AGI Eval, averaging **0.397**.
As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.
## BigBench-Hard Performance
We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.
# Dataset
We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset.
# Training
We trained with 8x A6000 GPUs for 62 hours, completing 4 epochs of full fine tuning on our dataset in one training run.
Commodity cost was ~$400.
# Citation
```bibtex
@software{lian2023mistralorca1
title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | 7,296 | [
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Trelis/TinyLlama-1.1B-Chat-v0.3-AWQ | 2023-10-03T15:40:25.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"awq",
"tinyllama",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Trelis | null | null | Trelis/TinyLlama-1.1B-Chat-v0.3-AWQ | 0 | 2 | transformers | 2023-10-03T15:26:36 | ---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- OpenAssistant/oasst_top1_2023-08-25
language:
- en
tags:
- awq
- tinyllama
---
# AWQ version of TinyLlama at 1Trillion tokens
original model card follows below.
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is the chat model finetuned on top of [PY007/TinyLlama-1.1B-intermediate-step-480k-1T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T).
The dataset used is [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25) following the [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) format.
#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```
from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-Chat-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = "How to get in a good university?"
formatted_prompt = (
f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
)
sequences = pipeline(
formatted_prompt,
do_sample=True,
top_k=50,
top_p = 0.9,
num_return_sequences=1,
repetition_penalty=1.1,
max_new_tokens=1024,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
``` | 2,134 | [
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stevanojs/pokemon_classification | 2023-10-03T18:58:26.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | stevanojs | null | null | stevanojs/pokemon_classification | 0 | 2 | transformers | 2023-10-03T16:46:35 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pokemon_classification
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. -->
# pokemon_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0586
- Accuracy: 0.9071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.3925 | 1.0 | 350 | 4.0653 | 0.6705 |
| 3.2005 | 2.0 | 700 | 3.1602 | 0.8227 |
| 2.3615 | 3.0 | 1050 | 2.4281 | 0.8656 |
| 1.5369 | 4.0 | 1400 | 1.8786 | 0.8821 |
| 1.0741 | 5.0 | 1750 | 1.4818 | 0.9014 |
| 0.7094 | 6.0 | 2100 | 1.2335 | 0.9014 |
| 0.544 | 7.0 | 2450 | 1.0976 | 0.9042 |
| 0.4622 | 8.0 | 2800 | 1.0586 | 0.9071 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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msullivan/3AOjQqYT | 2023-10-03T17:38:32.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | msullivan | null | null | msullivan/3AOjQqYT | 0 | 2 | sentence-transformers | 2023-10-03T17:07:09 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/7p/tw5y4fds72sbvl9tm_4gyl_w0000gn/T/tmp0wedhv75/msullivan/3AOjQqYT
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/7p/tw5y4fds72sbvl9tm_4gyl_w0000gn/T/tmp0wedhv75/msullivan/3AOjQqYT")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9 | 2023-10-03T21:10:35.000Z | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | bdpc | null | null | bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9 | 0 | 2 | transformers | 2023-10-03T20:32:42 | ---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9
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. -->
# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8809
- Accuracy: 0.7
- Brier Loss: 0.4126
- Nll: 2.4279
- F1 Micro: 0.7
- F1 Macro: 0.6279
- Ece: 0.2569
- Aurc: 0.1111
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 13 | 2.1185 | 0.165 | 0.8967 | 8.5399 | 0.165 | 0.1130 | 0.2151 | 0.8331 |
| No log | 2.0 | 26 | 2.1127 | 0.13 | 0.8958 | 8.1152 | 0.13 | 0.0842 | 0.1816 | 0.8392 |
| No log | 3.0 | 39 | 2.0781 | 0.165 | 0.8888 | 6.8828 | 0.165 | 0.0878 | 0.2150 | 0.8082 |
| No log | 4.0 | 52 | 2.0197 | 0.22 | 0.8762 | 5.7578 | 0.22 | 0.1155 | 0.2521 | 0.7521 |
| No log | 5.0 | 65 | 1.9499 | 0.205 | 0.8601 | 6.0641 | 0.205 | 0.0951 | 0.2567 | 0.7355 |
| No log | 6.0 | 78 | 1.9019 | 0.25 | 0.8483 | 5.8930 | 0.25 | 0.1178 | 0.2728 | 0.6862 |
| No log | 7.0 | 91 | 1.8252 | 0.28 | 0.8301 | 5.8062 | 0.28 | 0.1660 | 0.2890 | 0.6982 |
| No log | 8.0 | 104 | 1.8194 | 0.28 | 0.8275 | 5.2642 | 0.28 | 0.1625 | 0.2874 | 0.6935 |
| No log | 9.0 | 117 | 1.7671 | 0.355 | 0.8109 | 5.1326 | 0.3550 | 0.2211 | 0.3018 | 0.5678 |
| No log | 10.0 | 130 | 1.6582 | 0.355 | 0.7774 | 5.2226 | 0.3550 | 0.2200 | 0.2991 | 0.5305 |
| No log | 11.0 | 143 | 1.5849 | 0.395 | 0.7422 | 5.0239 | 0.395 | 0.2436 | 0.2979 | 0.3974 |
| No log | 12.0 | 156 | 1.4908 | 0.46 | 0.7001 | 4.2790 | 0.46 | 0.3169 | 0.3091 | 0.3003 |
| No log | 13.0 | 169 | 1.6016 | 0.395 | 0.7496 | 4.2149 | 0.395 | 0.2793 | 0.2929 | 0.4640 |
| No log | 14.0 | 182 | 1.4714 | 0.475 | 0.6971 | 4.0742 | 0.4750 | 0.3299 | 0.3177 | 0.3613 |
| No log | 15.0 | 195 | 1.5007 | 0.46 | 0.7119 | 3.8252 | 0.46 | 0.3145 | 0.3111 | 0.3954 |
| No log | 16.0 | 208 | 1.4352 | 0.515 | 0.6776 | 3.4028 | 0.515 | 0.3948 | 0.3376 | 0.2993 |
| No log | 17.0 | 221 | 1.2890 | 0.575 | 0.6104 | 3.4453 | 0.575 | 0.4478 | 0.2940 | 0.2119 |
| No log | 18.0 | 234 | 1.2190 | 0.595 | 0.5719 | 3.2413 | 0.595 | 0.4662 | 0.2608 | 0.1981 |
| No log | 19.0 | 247 | 1.2287 | 0.59 | 0.5764 | 3.2303 | 0.59 | 0.4857 | 0.2811 | 0.2020 |
| No log | 20.0 | 260 | 1.1726 | 0.64 | 0.5494 | 2.9544 | 0.64 | 0.5307 | 0.2993 | 0.1708 |
| No log | 21.0 | 273 | 1.1305 | 0.61 | 0.5384 | 2.9557 | 0.61 | 0.5170 | 0.2771 | 0.1949 |
| No log | 22.0 | 286 | 1.1256 | 0.645 | 0.5295 | 2.7934 | 0.645 | 0.5381 | 0.3181 | 0.1629 |
| No log | 23.0 | 299 | 1.1209 | 0.645 | 0.5217 | 2.8697 | 0.645 | 0.5432 | 0.3055 | 0.1687 |
| No log | 24.0 | 312 | 1.2513 | 0.685 | 0.5917 | 2.7262 | 0.685 | 0.5639 | 0.3779 | 0.1833 |
| No log | 25.0 | 325 | 1.0321 | 0.695 | 0.4819 | 2.7202 | 0.695 | 0.5896 | 0.2810 | 0.1280 |
| No log | 26.0 | 338 | 1.0405 | 0.645 | 0.4957 | 2.6116 | 0.645 | 0.5661 | 0.2515 | 0.1700 |
| No log | 27.0 | 351 | 1.0580 | 0.695 | 0.4933 | 2.7436 | 0.695 | 0.5996 | 0.2967 | 0.1339 |
| No log | 28.0 | 364 | 0.9740 | 0.65 | 0.4575 | 2.5682 | 0.65 | 0.5731 | 0.2513 | 0.1384 |
| No log | 29.0 | 377 | 0.9934 | 0.695 | 0.4651 | 2.5753 | 0.695 | 0.6108 | 0.2775 | 0.1171 |
| No log | 30.0 | 390 | 0.9900 | 0.645 | 0.4695 | 2.6280 | 0.645 | 0.5668 | 0.2459 | 0.1558 |
| No log | 31.0 | 403 | 0.9671 | 0.695 | 0.4504 | 2.8174 | 0.695 | 0.6094 | 0.2505 | 0.1188 |
| No log | 32.0 | 416 | 0.9327 | 0.715 | 0.4324 | 2.5285 | 0.715 | 0.6415 | 0.2565 | 0.1086 |
| No log | 33.0 | 429 | 0.9628 | 0.71 | 0.4464 | 2.5876 | 0.7100 | 0.6435 | 0.2709 | 0.1152 |
| No log | 34.0 | 442 | 0.9316 | 0.715 | 0.4353 | 2.7111 | 0.715 | 0.6334 | 0.2361 | 0.1078 |
| No log | 35.0 | 455 | 0.9275 | 0.7 | 0.4364 | 2.5226 | 0.7 | 0.6251 | 0.2586 | 0.1207 |
| No log | 36.0 | 468 | 0.9301 | 0.7 | 0.4346 | 2.6464 | 0.7 | 0.6232 | 0.2482 | 0.1142 |
| No log | 37.0 | 481 | 0.9013 | 0.695 | 0.4194 | 2.5575 | 0.695 | 0.6197 | 0.2554 | 0.1098 |
| No log | 38.0 | 494 | 0.9008 | 0.695 | 0.4196 | 2.6270 | 0.695 | 0.6156 | 0.2246 | 0.1063 |
| 1.0903 | 39.0 | 507 | 0.9185 | 0.71 | 0.4311 | 2.6290 | 0.7100 | 0.6362 | 0.2626 | 0.1165 |
| 1.0903 | 40.0 | 520 | 0.9053 | 0.685 | 0.4254 | 2.5057 | 0.685 | 0.6239 | 0.2210 | 0.1171 |
| 1.0903 | 41.0 | 533 | 0.8955 | 0.7 | 0.4189 | 2.4823 | 0.7 | 0.6291 | 0.1995 | 0.1103 |
| 1.0903 | 42.0 | 546 | 0.9012 | 0.69 | 0.4223 | 2.5377 | 0.69 | 0.6195 | 0.2486 | 0.1119 |
| 1.0903 | 43.0 | 559 | 0.8894 | 0.71 | 0.4138 | 2.6167 | 0.7100 | 0.6382 | 0.2459 | 0.1022 |
| 1.0903 | 44.0 | 572 | 0.8846 | 0.695 | 0.4132 | 2.5130 | 0.695 | 0.6265 | 0.2198 | 0.1093 |
| 1.0903 | 45.0 | 585 | 0.8946 | 0.69 | 0.4190 | 2.6357 | 0.69 | 0.6230 | 0.2375 | 0.1145 |
| 1.0903 | 46.0 | 598 | 0.8931 | 0.705 | 0.4168 | 2.6306 | 0.705 | 0.6342 | 0.2555 | 0.1102 |
| 1.0903 | 47.0 | 611 | 0.8842 | 0.71 | 0.4160 | 2.3021 | 0.7100 | 0.6347 | 0.2096 | 0.1120 |
| 1.0903 | 48.0 | 624 | 0.8805 | 0.695 | 0.4140 | 2.3447 | 0.695 | 0.6237 | 0.2181 | 0.1128 |
| 1.0903 | 49.0 | 637 | 0.8816 | 0.7 | 0.4142 | 2.4358 | 0.7 | 0.6295 | 0.2550 | 0.1112 |
| 1.0903 | 50.0 | 650 | 0.8809 | 0.7 | 0.4126 | 2.4279 | 0.7 | 0.6279 | 0.2569 | 0.1111 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
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LoneStriker/airoboros-mistral2.2-7b-5.0bpw-h6-exl2 | 2023-10-03T21:47:53.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"llama-2",
"instruct",
"finetune",
"alpaca",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:jondurbin/airoboros-2.2.1",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/airoboros-mistral2.2-7b-5.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T20:34:52 | ---
base_model: mistralai/Mistral-7b-V0.1
tags:
- llama-2
- instruct
- finetune
- alpaca
- gpt4
- synthetic data
- distillation
datasets:
- jondurbin/airoboros-2.2.1
model-index:
- name: airoboros2.2-mistral-7b
results: []
license: mit
language:
- en
---
Mistral trained with the airoboros dataset!
Actual dataset is airoboros 2.2, but it seems to have been replaced on hf with 2.2.1.
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
TruthfulQA:
```
hf-causal-experimental (pretrained=/home/teknium/dakota/lm-evaluation-harness/airoboros2.2-mistral/,dtype=float16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3562|± |0.0168|
| | |mc2 |0.5217|± |0.0156|
```
Wandb training charts: https://wandb.ai/teknium1/airoboros-mistral-7b/runs/airoboros-mistral-1?workspace=user-teknium1
More info to come | 1,104 | [
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LoneStriker/airoboros-mistral2.2-7b-6.0bpw-h6-exl2 | 2023-10-03T22:02:52.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"llama-2",
"instruct",
"finetune",
"alpaca",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:jondurbin/airoboros-2.2.1",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/airoboros-mistral2.2-7b-6.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T20:34:58 | ---
base_model: mistralai/Mistral-7b-V0.1
tags:
- llama-2
- instruct
- finetune
- alpaca
- gpt4
- synthetic data
- distillation
datasets:
- jondurbin/airoboros-2.2.1
model-index:
- name: airoboros2.2-mistral-7b
results: []
license: mit
language:
- en
---
Mistral trained with the airoboros dataset!
Actual dataset is airoboros 2.2, but it seems to have been replaced on hf with 2.2.1.
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
TruthfulQA:
```
hf-causal-experimental (pretrained=/home/teknium/dakota/lm-evaluation-harness/airoboros2.2-mistral/,dtype=float16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3562|± |0.0168|
| | |mc2 |0.5217|± |0.0156|
```
Wandb training charts: https://wandb.ai/teknium1/airoboros-mistral-7b/runs/airoboros-mistral-1?workspace=user-teknium1
More info to come | 1,104 | [
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LoneStriker/airoboros-mistral2.2-7b-8.0bpw-h6-exl2 | 2023-10-03T22:20:40.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"llama-2",
"instruct",
"finetune",
"alpaca",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:jondurbin/airoboros-2.2.1",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/airoboros-mistral2.2-7b-8.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-03T20:35:02 | ---
base_model: mistralai/Mistral-7b-V0.1
tags:
- llama-2
- instruct
- finetune
- alpaca
- gpt4
- synthetic data
- distillation
datasets:
- jondurbin/airoboros-2.2.1
model-index:
- name: airoboros2.2-mistral-7b
results: []
license: mit
language:
- en
---
Mistral trained with the airoboros dataset!
Actual dataset is airoboros 2.2, but it seems to have been replaced on hf with 2.2.1.
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
TruthfulQA:
```
hf-causal-experimental (pretrained=/home/teknium/dakota/lm-evaluation-harness/airoboros2.2-mistral/,dtype=float16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3562|± |0.0168|
| | |mc2 |0.5217|± |0.0156|
```
Wandb training charts: https://wandb.ai/teknium1/airoboros-mistral-7b/runs/airoboros-mistral-1?workspace=user-teknium1
More info to come | 1,104 | [
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domainExpert/ppo-LunarLander-v2 | 2023-10-04T06:29:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | domainExpert | null | null | domainExpert/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-04T06:28:43 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 239.88 +/- 46.47
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
Tommert25/robbert0410_lrate7.5b4 | 2023-10-04T07:29:58.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert0410_lrate7.5b4 | 0 | 2 | transformers | 2023-10-04T07:21:13 | ---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate7.5b4
results: []
---
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# robbert0410_lrate7.5b4
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4079
- Precisions: 0.7671
- Recall: 0.7305
- F-measure: 0.7455
- Accuracy: 0.8904
## 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: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.7027 | 1.0 | 942 | 0.4843 | 0.8628 | 0.6861 | 0.6969 | 0.8785 |
| 0.3645 | 2.0 | 1884 | 0.4079 | 0.7671 | 0.7305 | 0.7455 | 0.8904 |
| 0.238 | 3.0 | 2826 | 0.5338 | 0.7981 | 0.7422 | 0.7486 | 0.8957 |
| 0.1894 | 4.0 | 3768 | 0.6026 | 0.8327 | 0.7495 | 0.7668 | 0.9049 |
| 0.0825 | 5.0 | 4710 | 0.5781 | 0.7825 | 0.7859 | 0.7833 | 0.9055 |
| 0.0523 | 6.0 | 5652 | 0.6107 | 0.8084 | 0.7591 | 0.7712 | 0.9124 |
| 0.0388 | 7.0 | 6594 | 0.6455 | 0.8090 | 0.7802 | 0.7894 | 0.9107 |
| 0.0253 | 8.0 | 7536 | 0.6711 | 0.8168 | 0.7825 | 0.7937 | 0.9135 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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Tommert25/robbert0410_lrate7.5b8 | 2023-10-04T07:40:24.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert0410_lrate7.5b8 | 0 | 2 | transformers | 2023-10-04T07:33:10 | ---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate7.5b8
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. -->
# robbert0410_lrate7.5b8
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3807
- Precisions: 0.7617
- Recall: 0.7368
- F-measure: 0.7423
- Accuracy: 0.8880
## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| No log | 1.0 | 471 | 0.4437 | 0.8549 | 0.6737 | 0.6835 | 0.8700 |
| 0.5971 | 2.0 | 942 | 0.3807 | 0.7617 | 0.7368 | 0.7423 | 0.8880 |
| 0.2963 | 3.0 | 1413 | 0.4422 | 0.7859 | 0.7422 | 0.7476 | 0.9028 |
| 0.1606 | 4.0 | 1884 | 0.5208 | 0.8338 | 0.7546 | 0.7754 | 0.9041 |
| 0.107 | 5.0 | 2355 | 0.5299 | 0.7982 | 0.7887 | 0.7915 | 0.9076 |
| 0.0628 | 6.0 | 2826 | 0.5734 | 0.8099 | 0.7694 | 0.7824 | 0.9121 |
| 0.0295 | 7.0 | 3297 | 0.6021 | 0.8090 | 0.7771 | 0.7898 | 0.9116 |
| 0.0192 | 8.0 | 3768 | 0.6043 | 0.8120 | 0.7801 | 0.7927 | 0.9137 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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Tommert25/robbert0410_lrate7.5b32 | 2023-10-04T08:24:17.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert0410_lrate7.5b32 | 0 | 2 | transformers | 2023-10-04T07:52:08 | ---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate7.5b32
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. -->
# robbert0410_lrate7.5b32
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4559
- Precisions: 0.8283
- Recall: 0.8004
- F-measure: 0.8131
- Accuracy: 0.9159
## 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: 7.5e-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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.2719 | 1.0 | 118 | 0.4068 | 0.8731 | 0.7017 | 0.7281 | 0.8921 |
| 0.2306 | 2.0 | 236 | 0.3380 | 0.7819 | 0.7690 | 0.7699 | 0.9017 |
| 0.1268 | 3.0 | 354 | 0.3740 | 0.7908 | 0.7845 | 0.7790 | 0.9060 |
| 0.0858 | 4.0 | 472 | 0.3834 | 0.7983 | 0.7651 | 0.7763 | 0.9092 |
| 0.0586 | 5.0 | 590 | 0.4200 | 0.8225 | 0.7933 | 0.8045 | 0.9094 |
| 0.0349 | 6.0 | 708 | 0.4474 | 0.8328 | 0.7926 | 0.8094 | 0.9107 |
| 0.0209 | 7.0 | 826 | 0.4559 | 0.8283 | 0.8004 | 0.8131 | 0.9159 |
| 0.013 | 8.0 | 944 | 0.4614 | 0.8175 | 0.8034 | 0.8097 | 0.9144 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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navradio/swin-tiny-patch4-window7-224-PE | 2023-10-04T12:10:21.000Z | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | navradio | null | null | navradio/swin-tiny-patch4-window7-224-PE | 0 | 2 | transformers | 2023-10-04T08:05:57 | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-PE
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.797979797979798
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-PE
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4489
- Accuracy: 0.7980
## 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: 0.0025
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6872 | 1.0 | 11 | 0.6535 | 0.6061 |
| 0.7287 | 2.0 | 22 | 0.6601 | 0.6397 |
| 0.7212 | 3.0 | 33 | 0.6740 | 0.5657 |
| 0.6947 | 4.0 | 44 | 0.6531 | 0.6532 |
| 0.6783 | 5.0 | 55 | 0.6739 | 0.5724 |
| 0.6816 | 6.0 | 66 | 0.6274 | 0.6599 |
| 0.6428 | 7.0 | 77 | 0.6671 | 0.6330 |
| 0.6928 | 8.0 | 88 | 0.6380 | 0.6498 |
| 0.6767 | 9.0 | 99 | 0.6875 | 0.6061 |
| 0.6918 | 10.0 | 110 | 0.6859 | 0.5690 |
| 0.6845 | 11.0 | 121 | 0.6810 | 0.5657 |
| 0.6826 | 12.0 | 132 | 0.6919 | 0.5185 |
| 0.6877 | 13.0 | 143 | 0.6693 | 0.6061 |
| 0.6709 | 14.0 | 154 | 0.6660 | 0.5690 |
| 0.6707 | 15.0 | 165 | 0.6764 | 0.5690 |
| 0.6703 | 16.0 | 176 | 0.6467 | 0.6296 |
| 0.6629 | 17.0 | 187 | 0.6471 | 0.6431 |
| 0.6557 | 18.0 | 198 | 0.6597 | 0.6229 |
| 0.659 | 19.0 | 209 | 0.6451 | 0.6027 |
| 0.65 | 20.0 | 220 | 0.6638 | 0.6094 |
| 0.6453 | 21.0 | 231 | 0.6544 | 0.6162 |
| 0.6426 | 22.0 | 242 | 0.6565 | 0.5825 |
| 0.6339 | 23.0 | 253 | 0.6743 | 0.6296 |
| 0.6236 | 24.0 | 264 | 0.6669 | 0.5960 |
| 0.6427 | 25.0 | 275 | 0.6379 | 0.6532 |
| 0.6439 | 26.0 | 286 | 0.6361 | 0.6263 |
| 0.6212 | 27.0 | 297 | 0.6540 | 0.6465 |
| 0.6186 | 28.0 | 308 | 0.5925 | 0.6700 |
| 0.6162 | 29.0 | 319 | 0.6224 | 0.6734 |
| 0.6237 | 30.0 | 330 | 0.6018 | 0.6667 |
| 0.6061 | 31.0 | 341 | 0.5735 | 0.6801 |
| 0.6138 | 32.0 | 352 | 0.6425 | 0.6566 |
| 0.595 | 33.0 | 363 | 0.5827 | 0.6768 |
| 0.5869 | 34.0 | 374 | 0.5956 | 0.7172 |
| 0.577 | 35.0 | 385 | 0.5458 | 0.7003 |
| 0.5766 | 36.0 | 396 | 0.5603 | 0.6869 |
| 0.5726 | 37.0 | 407 | 0.5339 | 0.7340 |
| 0.5702 | 38.0 | 418 | 0.5577 | 0.7138 |
| 0.5762 | 39.0 | 429 | 0.5262 | 0.7374 |
| 0.5543 | 40.0 | 440 | 0.5091 | 0.7441 |
| 0.5339 | 41.0 | 451 | 0.5185 | 0.7542 |
| 0.5428 | 42.0 | 462 | 0.5023 | 0.7542 |
| 0.5349 | 43.0 | 473 | 0.5439 | 0.7306 |
| 0.5319 | 44.0 | 484 | 0.4745 | 0.7811 |
| 0.5294 | 45.0 | 495 | 0.5432 | 0.7172 |
| 0.5314 | 46.0 | 506 | 0.4511 | 0.7912 |
| 0.5073 | 47.0 | 517 | 0.4379 | 0.8047 |
| 0.5028 | 48.0 | 528 | 0.4487 | 0.7980 |
| 0.4985 | 49.0 | 539 | 0.4550 | 0.7946 |
| 0.4826 | 50.0 | 550 | 0.4489 | 0.7980 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Quacktab/ppo-SnowballTarget | 2023-10-04T08:44:37.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | Quacktab | null | null | Quacktab/ppo-SnowballTarget | 0 | 2 | ml-agents | 2023-10-04T08:44:34 | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Quacktab/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Quacktab/Pyramids | 2023-10-04T08:47:35.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | Quacktab | null | null | Quacktab/Pyramids | 0 | 2 | ml-agents | 2023-10-04T08:47:32 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Quacktab/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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rezaFarsh/finetuning-sentiment-model-1900-samples-6-labels | 2023-10-04T10:02:09.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | rezaFarsh | null | null | rezaFarsh/finetuning-sentiment-model-1900-samples-6-labels | 0 | 2 | transformers | 2023-10-04T09:43:47 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-1900-samples-6-labels
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. -->
# finetuning-sentiment-model-1900-samples-6-labels
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1934
- Accuracy: 0.6667
- F1 Score: 0.6574
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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Tommert25/robbert0410_lrate10b16 | 2023-10-04T11:39:00.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert0410_lrate10b16 | 0 | 2 | transformers | 2023-10-04T09:57:19 | ---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate10b16
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. -->
# robbert0410_lrate10b16
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4783
- Precisions: 0.8324
- Recall: 0.8123
- F-measure: 0.8208
- Accuracy: 0.9164
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.6379 | 1.0 | 236 | 0.4156 | 0.8657 | 0.6790 | 0.6955 | 0.8798 |
| 0.3257 | 2.0 | 472 | 0.3378 | 0.7529 | 0.7397 | 0.7336 | 0.8932 |
| 0.1977 | 3.0 | 708 | 0.3737 | 0.7960 | 0.7383 | 0.7451 | 0.9003 |
| 0.1197 | 4.0 | 944 | 0.4060 | 0.8446 | 0.7503 | 0.7696 | 0.9025 |
| 0.0659 | 5.0 | 1180 | 0.4428 | 0.7851 | 0.7731 | 0.7779 | 0.9063 |
| 0.0447 | 6.0 | 1416 | 0.4972 | 0.8285 | 0.7991 | 0.8124 | 0.9127 |
| 0.0256 | 7.0 | 1652 | 0.4783 | 0.8324 | 0.8123 | 0.8208 | 0.9164 |
| 0.0173 | 8.0 | 1888 | 0.4918 | 0.8251 | 0.8082 | 0.8159 | 0.9169 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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] |
cris177/llama-2-7b-Arguments | 2023-10-05T19:35:00.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | cris177 | null | null | cris177/llama-2-7b-Arguments | 1 | 2 | transformers | 2023-10-04T11:39:14 | This model aims to detect and analyze casual arguments.
Model template:
```
<s>[INST] {prompt}
[/INST]
```
Example:
```
`<`s`>`[INST] Analize the following argument, identifying premises, conclusion, type of argument, and argument validity:
If officer smith found a broken window at the crime scene then the arson occurred on elm street, and officer smith found a broken window at the crime scene, hence the arson occurred on elm street.
[/INST] Premise 1: If officer smith found a broken window at the crime scene then the arson occurred on elm street Premise 2: Officer smith found a broken window at the crime scene Conclusion: The arson occurred on Elm Street Type of argument: modus ponen Validity: True `<`/s`>`
```
It was trained on my dataset cris177/Arguments (https://huggingface.co/datasets/cris177/Arguments) | 826 | [
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athirdpath/MistRP-Dolphin-7B-AWQ | 2023-10-04T18:50:42.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"text-generation-inference",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-generation | athirdpath | null | null | athirdpath/MistRP-Dolphin-7B-AWQ | 0 | 2 | transformers | 2023-10-04T11:40:46 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- mistral
- nsfw
- text-generation-inference
---
Testing purposes only
AWQ quant of Undi95/MistRP-Dolphin-7B
4bit, 128GS, wikitext | 188 | [
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wooii/a2c-PandaReachDense-v3 | 2023-10-04T21:25:22.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | wooii | null | null | wooii/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-04T12:17:08 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
TheBloke/Dans-TotSirocco-7B-GGUF | 2023-10-04T12:49:37.000Z | [
"transformers",
"mistral",
"en",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/Dans-TotSirocco-7B-GGUF | 0 | 2 | transformers | 2023-10-04T12:42:06 | ---
base_model: PocketDoc/Dans-TotSirocco-7b
inference: false
language:
- en
model_creator: PocketDoc Labs
model_name: Dans TotSirocco 7B
model_type: mistral
prompt_template: '<|system|>{system_message}<|user|>{prompt}<|model|>
'
quantized_by: TheBloke
---
<!-- header start -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<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 -->
# Dans TotSirocco 7B - GGUF
- Model creator: [PocketDoc Labs](https://huggingface.co/PocketDoc)
- Original model: [Dans TotSirocco 7B](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [PocketDoc Labs's Dans TotSirocco 7B](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b).
<!-- 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 incomplate 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.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [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.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF)
* [PocketDoc Labs's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Metharme
```
<|system|>{system_message}<|user|>{prompt}<|model|>
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [dans-totsirocco-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [dans-totsirocco-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [dans-totsirocco-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [dans-totsirocco-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [dans-totsirocco-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [dans-totsirocco-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [dans-totsirocco-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [dans-totsirocco-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [dans-totsirocco-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [dans-totsirocco-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [dans-totsirocco-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [dans-totsirocco-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Dans-TotSirocco-7B-GGUF/blob/main/dans-totsirocco-7b.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 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/Dans-TotSirocco-7B-GGUF and below it, a specific filename to download, such as: dans-totsirocco-7b.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/Dans-TotSirocco-7B-GGUF dans-totsirocco-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Dans-TotSirocco-7B-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/Dans-TotSirocco-7B-GGUF dans-totsirocco-7b.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 32 -m dans-totsirocco-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|system|>{system_message}<|user|>{prompt}<|model|>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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.
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 here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## 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.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/Dans-TotSirocco-7B-GGUF", model_file="dans-totsirocco-7b.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## 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 -->
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## 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
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: PocketDoc Labs's Dans TotSirocco 7B
### Description:
This model is to test some of my data projects. It is a multipurpose chat / chat instruct hybrid model and can be seen as a prototype of Dan's PersonalityEngine Mk. 2. It has been trained on a wide array of one shot instructions, multi round instructions, role playing scenarios, and text adventure games. It is based on [Mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Prompt format:
Pygmalion / Metharme
The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired.
```
<|system|>system message here<|user|>user message here<|model|>
```
```
<|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|>
```
```
<|system|>system message here<|model|>
```
```
<|system|>system message here<|model|>model message<|user|>user message here<|model|>
```
#### Some example prompts and their resulting outputs:
##### Prompt:
```
<|system|>You are an assistant, help the user and use markdown when applicable.<|user|>What are the top games of the 90s?<|model|>
```
##### Output:
```
The 1990s was a golden era for video gaming, with countless iconic titles released across various platforms. Here's a list of some of the most popular and influential games from that decade:
- **Super Mario World** (SNES) - Released in 1990, this classic platformer introduced fans to Yoshi and featured memorable levels like Donut Plains and Star World.
- **Mortal Kombat** (Arcade/Sega Genesis) - This gory fighting game shocked audiences upon its release in 1992 with its violent finishers and secret characters like Sub-Zero and Scorpion.
- **Doom** (PC) - Id Software's groundbreaking first-person shooter revolutionized the genre by introducing fast-paced action and multiplayer deathmatches.
- **Street Fighter II** (Arcade/SNES) - Capcom's sequel to their original Street Fighter arcade game added new fighters such as Ryu's rival Ken Masters plus complex special moves like Dragon Punch and Hadouken.
- **Crash Bandicoot** (PlayStation) - Naughty Dog's mascot platforming series debuted in 1996 starring Crash who had to save his girlfriend Tawna from Dr Cortex's evil experiments on Aku Aku masks.
- **Final Fantasy VII** (PlayStation) - Square Enix's seventh installment took players through Midgar City where they joined Cloud Strife's eco-terrorist group AVALANCHE against Shinra Electric Power Company led by Sephiroth.
- **Resident Evil** (PlayStation) - The survival horror masterpiece created by Capcom saw Chris Redfield investigating Spencer Mansion alongside Jill Valentine while trying not get eaten alive by zombies!
```
##### Prompt:
```
<|system|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<|user|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|model|>
```
##### Output:
```
The roar of the engines shook the very foundations of the planet as the massive warships descended upon Alpha Centauri. The air was thick with the stench of burning fuel and smoke, as if the entire world had been set ablaze. In the chaos, soldiers were scrambling for cover, desperately trying to protect themselves from the relentless barrage of firepower unleashed by their enemy.
Amidst the carnage, one soldier stood tall, his heart pounding against his chest like a drumbeat of death. He knew he had to do something, anything, to stop this nightmare from becoming reality. With trembling hands, he reached for his weapon, determined to make a stand against the overwhelming force that threatened to consume them all.
As the battle raged around him, the soldier felt a sudden surge of adrenaline coursing through his veins. It was almost as if some hidden power within him had awoken, granting him strength beyond measure. His eyes blazed with determination as he raised his gun and took aim at the first target he saw: a bear-like creature, its fur matted with blood and gore, advancing towards him with murderous intent.
With a deafening crack, the soldier fired off several rounds, striking the beast square in the chest. The impact sent it reeling backwards, but it did not fall. Instead, it let out a ferocious growl before charging forward once more. The soldier gritted his teeth and pulled the trigger again, emptying his clip into the creature's already battered frame.
But even as the bullets tore through its flesh, the beast seemed unfazed, continuing its advance towards the terrified soldier. Desperate for any advantage, he threw himself to the side just moments before the monster slammed into the spot where he had been standing mere seconds ago. As it turned back towards him, ready to deliver another brutal blow, the soldier could see that its eyes glowed with an otherworldly light.
```
##### Prompt:
```
<|system|>You are a professional editor with decades of experience, help the user with any task they have for you.<|user|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<|model|>
```
##### Output:
```
I hesitated before making my decision, knowing full well it was not wise. However, in the end, I chose to proceed regardless.
```
# Some quick and dirty training details:
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Sequence length: 4096
- Training time: 4 hours
- Hardware: 2x RTX 4090
- Training type: QLoRA
- PEFT R/A: 32/32
# Credits:
### Skein Text Adventure Data:
Thank you to the [Kobold AI](https://huggingface.co/KoboldAI) community for curating the Skein dataset, which is pivotal to this model's capabilities.
<!-- original-model-card end -->
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zongxiao/speecht5_finetuned_zh-TW | 2023-10-04T16:15:41.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | zongxiao | null | null | zongxiao/speecht5_finetuned_zh-TW | 0 | 2 | transformers | 2023-10-04T13:46:23 | ---
license: mit
base_model: GCYY/speecht5_finetuned_fleurs_zh
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_finetuned_zh-TW
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. -->
# speecht5_finetuned_zh-TW
This model is a fine-tuned version of [GCYY/speecht5_finetuned_fleurs_zh](https://huggingface.co/GCYY/speecht5_finetuned_fleurs_zh) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5108
## 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: 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: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5844 | 6.62 | 1000 | 0.5454 |
| 0.5495 | 13.25 | 2000 | 0.5215 |
| 0.5536 | 19.87 | 3000 | 0.5176 |
| 0.5257 | 26.49 | 4000 | 0.5108 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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Siddharth63/bioul2_small | 2023-11-01T09:57:16.000Z | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:Siddharth63/biological_dataset",
"arxiv:1910.10683",
"license:artistic-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Siddharth63 | null | null | Siddharth63/bioul2_small | 0 | 2 | transformers | 2023-10-04T13:56:14 | ---
license: artistic-2.0
datasets:
- Siddharth63/biological_dataset
---
# Bioul2-small
Pretrained T5 model on Biological dataset using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in this paper and first released at this page. The UL2 objective was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released on [this page](https://github.com/google-research/text-to-text-transfer-transformer).
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice.
## Model description
T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format.
BioT5 is a transformers model pretrained on a very large corpus of biological data (25 million abstracts) in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts.
This model used the T5 v1.1 improvements compared to the original T5 model during the pretraining:
GEGLU activation in feed-forward hidden layer, rather than ReLU - see here
Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning
Pretrained on self-supervised objective only without mixing in the downstream tasks
No parameter sharing between embedding and classifier layer
This model also used the "efficient" T5 architecture findings presented in this paper. In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. To be more precise, model depth is defined as the number of transformer blocks that are stacked sequentially.
## UL2 pretraining objective
This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of three denoising tasks: (1) R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; (2) X-denoising (or extreme span corruption); and (3) S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios.
UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training denoising task. During the pretraining, a paradigm token is inserted to the input ([NLU] for R-denoising, [NLG] for X-denoising, or [S2S] for S-denoising) indicating the denoising task at hand. Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream fine-tuning tasks.
Intended uses & limitations
This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. Note: You most likely need to fine-tune these T5/UL2 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from here, for example.
Note: For fine-tuning, most likely you can get better results if you insert a prefix token of [NLU], [NLG], or [S2S] to your input texts. For general language understanding fine-tuning tasks, you could use the [NLU] token. For GPT-style causal language generation, you could use the [S2S] token. The token [NLG] of the X-denoising pretrain task is somewhat mix between the language understanding and causal language generation so the token [NLG] could maybe be used for language generation fine-tuning too.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the [Google TPU Research Cloud](https://sites.research.google/trc/about/). Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective, associated task definitions and their guidance. Thanks to [Yeb Havinga](https://huggingface.co/yhavinga) for helping me get started with the t5x framework. | 4,574 | [
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Kishore05/trial | 2023-10-29T15:26:49.000Z | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Kishore05 | null | null | Kishore05/trial | 0 | 2 | transformers | 2023-10-04T15:22:32 | ---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: trial
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. -->
# trial
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: 10.5616
- Validation Loss: 10.4831
- 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': -999, '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 |
|:----------:|:---------------:|:-----:|
| 10.5376 | 10.4886 | 0 |
| 10.5294 | 10.4868 | 1 |
| 10.5616 | 10.4831 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| 1,780 | [
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] |
sophiaaaa/flan-t5-base-finetuned-smcp | 2023-10-19T12:29:17.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | sophiaaaa | null | null | sophiaaaa/flan-t5-base-finetuned-smcp | 0 | 2 | transformers | 2023-10-04T15:28:28 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-finetuned-smcp
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. -->
# flan-t5-base-finetuned-smcp
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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toobiza/table-transformer-fancy-plant-45 | 2023-10-04T17:33:55.000Z | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | object-detection | toobiza | null | null | toobiza/table-transformer-fancy-plant-45 | 0 | 2 | transformers | 2023-10-04T17:07:54 | ---
license: mit
base_model: microsoft/table-transformer-detection
tags:
- generated_from_trainer
model-index:
- name: table-transformer-fancy-plant-45
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. -->
# table-transformer-fancy-plant-45
This model is a fine-tuned version of [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3426
- eval_runtime: 593.6566
- eval_samples_per_second: 16.845
- eval_steps_per_second: 4.211
- epoch: 0.08
- step: 1000
## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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calcifer2023/distilbert-base-uncased-finetuned-sentiment | 2023-10-04T17:42:40.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | calcifer2023 | null | null | calcifer2023/distilbert-base-uncased-finetuned-sentiment | 0 | 2 | transformers | 2023-10-04T17:42:00 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sentiment
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-sentiment
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: 1.1374
- Accuracy: 0.57
- F1: 0.4139
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3727 | 1.0 | 32 | 1.2077 | 0.57 | 0.4139 |
| 1.0734 | 2.0 | 64 | 1.1374 | 0.57 | 0.4139 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.14.0
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DaColdest/ppo-lunarlander-v2 | 2023-10-04T17:46:52.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | DaColdest | null | null | DaColdest/ppo-lunarlander-v2 | 0 | 2 | stable-baselines3 | 2023-10-04T17:46:33 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 284.22 +/- 15.33
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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palmer0/finetuning-sentiment-model-3000-samples | 2023-10-04T19:22:32.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | palmer0 | null | null | palmer0/finetuning-sentiment-model-3000-samples | 0 | 2 | transformers | 2023-10-04T18:49:46 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8758169934640523
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3002
- Accuracy: 0.8733
- F1: 0.8758
## 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: 2
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
dima806/ball_types_image_detection | 2023-10-04T19:47:23.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | dima806 | null | null | dima806/ball_types_image_detection | 0 | 2 | transformers | 2023-10-04T19:44:59 | ---
license: apache-2.0
metrics:
- accuracy
- f1
---
See https://www.kaggle.com/code/dima806/ball-types-image-detection for more details. | 137 | [
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] |
Weyaxi/OpenOrca-Nebula-7B | 2023-10-05T12:51:02.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Weyaxi | null | null | Weyaxi/OpenOrca-Nebula-7B | 0 | 2 | transformers | 2023-10-04T20:34:55 | ---
license: cc-by-nc-4.0
datasets:
- garage-bAInd/Open-Platypus
language:
- en
---
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
# OpenOrca-Nebula-7B
OpenOrca-Nebula-7B is a merge of [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and [PulsarAI/Nebula-7B](https://huggingface.co/Weyaxi/PulsarAI/Nebula-7B)
# Evulation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard))
| Metric | Value |
|-----------------------|-------|
| Avg. | |
| ARC (25-shot) | |
| HellaSwag (10-shot) | |
| MMLU (5-shot) | |
| TruthfulQA (0-shot) | |
| 876 | [
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GreenBitAI/LLaMA-2-70B-CHAT-2bit-groupsize8 | 2023-10-05T08:24:05.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | GreenBitAI | null | null | GreenBitAI/LLaMA-2-70B-CHAT-2bit-groupsize8 | 1 | 2 | transformers | 2023-10-04T21:05:07 | ---
license: apache-2.0
---
# GreenBit LLaMA
This is GreenBitAI's pretrained **2-bit** LLaMA model with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information.
## Model Description
- **Developed by:** [GreenBitAI](https://github.com/GreenBitAI)
- **Model type:** Causal (Llama 2)
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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stecas/gpt2pac | 2023-10-04T22:24:00.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | stecas | null | null | stecas/gpt2pac | 0 | 2 | transformers | 2023-10-04T21:38:10 | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2pac
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. -->
# gpt2pac
This model was created by finetuning [GPT-2-xl](https://huggingface.co/gpt2-xl) on the lyrics of 824 Tupac Shakur songs from [https://lyrics.az/2pac/allsongs.html](https://lyrics.az/2pac/allsongs.html).
## data
All unfiltered data from [https://lyrics.az/2pac/allsongs.html](https://lyrics.az/2pac/allsongs.html) are in [2pac_lyrics.json](https://huggingface.co/stecas/gpt2pac/blob/main/2pac_lyrics.json). However, I filtered some of these out of the model's training data because they were too short, not songs, etc.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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Charlie911/vicuna-7b-v1.5-lora-time-unit | 2023-10-07T07:07:54.000Z | [
"peft",
"region:us"
] | null | Charlie911 | null | null | Charlie911/vicuna-7b-v1.5-lora-time-unit | 0 | 2 | peft | 2023-10-05T01:18:59 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
| 464 | [
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hope04302/kr-bert-bi-s | 2023-10-05T01:55:28.000Z | [
"transformers",
"pytorch",
"jax",
"bert",
"ko",
"arxiv:2008.03979",
"endpoints_compatible",
"region:us"
] | null | hope04302 | null | null | hope04302/kr-bert-bi-s | 0 | 2 | transformers | 2023-10-05T01:34:44 | ---
language:
- ko
---
## KoRean based Bert pre-trained (KR-BERT)
This is a release of Korean-specific, small-scale BERT models with comparable or better performances developed by Computational Linguistics Lab at Seoul National University, referenced in [KR-BERT: A Small-Scale Korean-Specific Language Model](https://arxiv.org/abs/2008.03979).
<br>
### Vocab, Parameters and Data
| | Mulitlingual BERT<br>(Google) | KorBERT<br>(ETRI) | KoBERT<br>(SKT) | KR-BERT character | KR-BERT sub-character |
| -------------: | ---------------------------------------------: | ---------------------: | ----------------------------------: | -------------------------------------: | -------------------------------------: |
| vocab size | 119,547 | 30,797 | 8,002 | 16,424 | 12,367 |
| parameter size | 167,356,416 | 109,973,391 | 92,186,880 | 99,265,066 | 96,145,233 |
| data size | -<br>(The Wikipedia data<br>for 104 languages) | 23GB<br>4.7B morphemes | -<br>(25M sentences,<br>233M words) | 2.47GB<br>20M sentences,<br>233M words | 2.47GB<br>20M sentences,<br>233M words |
| Model | Masked LM Accuracy |
| ------------------------------------------- | ------------------ |
| KoBERT | 0.750 |
| KR-BERT character BidirectionalWordPiece | **0.779** |
| KR-BERT sub-character BidirectionalWordPiece | 0.769 |
<br>
### Sub-character
Korean text is basically represented with Hangul syllable characters, which can be decomposed into sub-characters, or graphemes. To accommodate such characteristics, we trained a new vocabulary and BERT model on two different representations of a corpus: syllable characters and sub-characters.
In case of using our sub-character model, you should preprocess your data with the code below.
```python
import torch
from transformers import BertConfig, BertModel, BertForPreTraining, BertTokenizer
from unicodedata import normalize
tokenizer_krbert = BertTokenizer.from_pretrained('/path/to/vocab_file.txt', do_lower_case=False)
# convert a string into sub-char
def to_subchar(string):
return normalize('NFKD', string)
sentence = '토크나이저 예시입니다.'
print(tokenizer_krbert.tokenize(to_subchar(sentence)))
```
### Tokenization
#### BidirectionalWordPiece Tokenizer
We use the BidirectionalWordPiece model to reduce search costs while maintaining the possibility of choice. This model applies BPE in both forward and backward directions to obtain two candidates and chooses the one that has a higher frequency.
| | Mulitlingual BERT | KorBERT<br>character | KoBERT | KR-BERT<br>character<br>WordPiece | KR-BERT<br>character<br>BidirectionalWordPiece | KR-BERT<br>sub-character<br>WordPiece | KR-BERT<br>sub-character<br>BidirectionalWordPiece |
| :-------------------------------------: | :-----------------------: | :-----------------------: | :-----------------------: | :------------------------------: | :-------------------------------------------: | :----------------------------------: | :-----------------------------------------------: |
| 냉장고<br>nayngcangko<br>"refrigerator" | 냉#장#고<br>nayng#cang#ko | 냉#장#고<br>nayng#cang#ko | 냉#장#고<br>nayng#cang#ko | 냉장고<br>nayngcangko | 냉장고<br>nayngcangko | 냉장고<br>nayngcangko | 냉장고<br>nayngcangko |
| 춥다<br>chwupta<br>"cold" | [UNK] | 춥#다<br>chwup#ta | 춥#다<br>chwup#ta | 춥#다<br>chwup#ta | 춥#다<br>chwup#ta | 추#ㅂ다<br>chwu#pta | 추#ㅂ다<br>chwu#pta |
| 뱃사람<br>paytsalam<br>"seaman" | [UNK] | 뱃#사람<br>payt#salam | 뱃#사람<br>payt#salam | 뱃#사람<br>payt#salam | 뱃#사람<br>payt#salam | 배#ㅅ#사람<br>pay#t#salam | 배#ㅅ#사람<br>pay#t#salam |
| 마이크<br>maikhu<br>"microphone" | 마#이#크<br>ma#i#khu | 마이#크<br>mai#khu | 마#이#크<br>ma#i#khu | 마이크<br>maikhu | 마이크<br>maikhu | 마이크<br>maikhu | 마이크<br>maikhu |
<br>
### Models
| | TensorFlow | | PyTorch | |
|:---:|:-------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|
| | character | sub-character | character | sub-character |
| WordPiece <br> tokenizer | [WP char](https://drive.google.com/open?id=1SG5m-3R395VjEEnt0wxWM7SE1j6ndVsX) | [WP subchar](https://drive.google.com/open?id=13oguhQvYD9wsyLwKgU-uLCacQVWA4oHg) | [WP char](https://drive.google.com/file/d/18lsZzx_wonnOezzB5QxqSliA2KL5BF0x/view?usp=sharing) | [WP subchar](https://drive.google.com/open?id=1c1en4AMlCv2k7QapIzqjefnYzNOoh5KZ)
| Bidirectional <br> WordPiece <br> tokenizer | [BiWP char](https://drive.google.com/open?id=1YhFobehwzdbIxsHHvyFU5okp-HRowRKS) | [BiWP subchar](https://drive.google.com/open?id=12izU0NZXNz9I6IsnknUbencgr7gWHDeM) | [BiWP char](https://drive.google.com/open?id=1C87CCHD9lOQhdgWPkMw_6ZD5M2km7f1p) | [BiWP subchar](https://drive.google.com/file/d/1JvNYFQyb20SWgOiDxZn6h1-n_fjTU25S/view?usp=sharing)
<!--
#### tensorflow
* BERT tokenizer, character model ([download](https://drive.google.com/open?id=1SG5m-3R395VjEEnt0wxWM7SE1j6ndVsX))
* BidirectionalWordPiece tokenizer, character model ([download](https://drive.google.com/open?id=1YhFobehwzdbIxsHHvyFU5okp-HRowRKS))
* BERT tokenizer, sub-character model ([download](https://drive.google.com/open?id=13oguhQvYD9wsyLwKgU-uLCacQVWA4oHg))
* BidirectionalWordPiece tokenizer, sub-character model ([download](https://drive.google.com/open?id=12izU0NZXNz9I6IsnknUbencgr7gWHDeM))
#### pytorch
* BERT tokenizer, character model ([download](https://drive.google.com/file/d/18lsZzx_wonnOezzB5QxqSliA2KL5BF0x/view?usp=sharing))
* BidirectionalWordPiece tokenizer, character model ([download](https://drive.google.com/open?id=1C87CCHD9lOQhdgWPkMw_6ZD5M2km7f1p))
* BERT tokenizer, sub-character model ([download](https://drive.google.com/open?id=1c1en4AMlCv2k7QapIzqjefnYzNOoh5KZ))
* BidirectionalWordPiece tokenizer, sub-character model ([download](https://drive.google.com/file/d/1JvNYFQyb20SWgOiDxZn6h1-n_fjTU25S/view?usp=sharing))
-->
<br>
### Requirements
- transformers == 2.1.1
- tensorflow < 2.0
<br>
## Downstream tasks
### Naver Sentiment Movie Corpus (NSMC)
* If you want to use the sub-character version of our models, let the `subchar` argument be `True`.
* And you can use the original BERT WordPiece tokenizer by entering `bert` for the `tokenizer` argument, and if you use `ranked` you can use our BidirectionalWordPiece tokenizer.
* tensorflow: After downloading our pretrained models, put them in a `models` directory in the `krbert_tensorflow` directory.
* pytorch: After downloading our pretrained models, put them in a `pretrained` directory in the `krbert_pytorch` directory.
```sh
# pytorch
python3 train.py --subchar {True, False} --tokenizer {bert, ranked}
# tensorflow
python3 run_classifier.py \
--task_name=NSMC \
--subchar={True, False} \
--tokenizer={bert, ranked} \
--do_train=true \
--do_eval=true \
--do_predict=true \
--do_lower_case=False\
--max_seq_length=128 \
--train_batch_size=128 \
--learning_rate=5e-05 \
--num_train_epochs=5.0 \
--output_dir={output_dir}
```
The pytorch code structure refers to that of https://github.com/aisolab/nlp_implementation .
<br>
### NSMC Acc.
| | multilingual BERT | KorBERT | KoBERT | KR-BERT character WordPiece | KR-BERT<br>character Bidirectional WordPiece | KR-BERT sub-character WordPiece | KR-BERT<br>sub-character Bidirectional WordPiece |
|:-----:|-------------------:|----------------:|--------:|----------------------------:|-----------------------------------------:|--------------------------------:|---------------------------------------------:|
| pytorch | - | **89.84** | 89.01 | 89.34 | **89.38** | 89.20 | 89.34 |
| tensorflow | 87.08 | 85.94 | n/a | 89.86 | **90.10** | 89.76 | 89.86 |
<br>
## Citation
If you use these models, please cite the following paper:
```
@article{lee2020krbert,
title={KR-BERT: A Small-Scale Korean-Specific Language Model},
author={Sangah Lee and Hansol Jang and Yunmee Baik and Suzi Park and Hyopil Shin},
year={2020},
journal={ArXiv},
volume={abs/2008.03979}
}
```
<br>
## Contacts
nlp.snu@gmail.com
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Jopaky/clip-roberta-finetuned | 2023-10-31T09:30:55.000Z | [
"transformers",
"pytorch",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"dataset:ydshieh/coco_dataset_script",
"endpoints_compatible",
"region:us"
] | feature-extraction | Jopaky | null | null | Jopaky/clip-roberta-finetuned | 0 | 2 | transformers | 2023-10-05T03:07:50 | ---
base_model: ./clip-roberta
tags:
- generated_from_trainer
datasets:
- ydshieh/coco_dataset_script
model-index:
- name: clip-roberta-finetuned
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. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./clip-roberta](https://huggingface.co/./clip-roberta) on the ydshieh/coco_dataset_script 2017 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6969
## 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: 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: 3.0
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
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tribber93/cite_classification_model | 2023-10-05T08:13:12.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:scicite",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | tribber93 | null | null | tribber93/cite_classification_model | 0 | 2 | transformers | 2023-10-05T06:04:45 | ---
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-large
tags:
- generated_from_trainer
datasets:
- scicite
metrics:
- accuracy
model-index:
- name: cite_classification_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: scicite
type: scicite
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925764192139738
---
<!-- 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. -->
# cite_classification_model
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the scicite dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4804
- Accuracy: 0.9258
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2679 | 1.0 | 513 | 0.1976 | 0.9258 |
| 0.1903 | 2.0 | 1026 | 0.2146 | 0.9225 |
| 0.1474 | 3.0 | 1539 | 0.2356 | 0.9225 |
| 0.1105 | 4.0 | 2052 | 0.3363 | 0.9279 |
| 0.0785 | 5.0 | 2565 | 0.3935 | 0.9225 |
| 0.0498 | 6.0 | 3078 | 0.4296 | 0.9236 |
| 0.0293 | 7.0 | 3591 | 0.4774 | 0.9203 |
| 0.0186 | 8.0 | 4104 | 0.4804 | 0.9258 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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kavyamanohar/whisper-small-ml | 2023-11-03T07:24:23.000Z | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | kavyamanohar | null | null | kavyamanohar/whisper-small-ml | 1 | 2 | transformers | 2023-10-05T07:30:22 | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ml
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. -->
# whisper-small-ml
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5452
- Wer: 84.0883
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0869 | 6.25 | 200 | 0.3877 | 89.5470 |
| 0.0138 | 12.5 | 400 | 0.4962 | 87.4564 |
| 0.0088 | 18.75 | 600 | 0.5118 | 100.3484 |
| 0.0058 | 25.0 | 800 | 0.5452 | 84.0883 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.0
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miritto/PPO-LunarLander-v2 | 2023-10-05T07:52:43.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | miritto | null | null | miritto/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-05T07:52:20 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.07 +/- 37.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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k0T0z/ppo-LunarLander-v2 | 2023-10-05T11:43:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | k0T0z | null | null | k0T0z/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-05T11:43:11 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.31 +/- 20.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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ledinhnguyen00/EmoraBert | 2023-10-11T17:20:39.000Z | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | ledinhnguyen00 | null | null | ledinhnguyen00/EmoraBert | 0 | 2 | transformers | 2023-10-05T12:05:55 | ---
license: mit
base_model: wonrax/phobert-base-vietnamese-sentiment
tags:
- generated_from_keras_callback
model-index:
- name: ledinhnguyen00/EmoraBert
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. -->
# ledinhnguyen00/EmoraBert
This model is a fine-tuned version of [wonrax/phobert-base-vietnamese-sentiment](https://huggingface.co/wonrax/phobert-base-vietnamese-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8098
- Validation Loss: 0.7439
- Train Accuracy: 0.6678
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 115641, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.8098 | 0.7439 | 0.6678 | 0 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
TanmaySah/l13b | 2023-10-17T19:17:04.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/l13b | 0 | 2 | peft | 2023-10-05T14:03:54 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
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JCTN/Mistral-7B-instruct-exl2 | 2023-10-05T16:29:47.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"finetuned",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | JCTN | null | null | JCTN/Mistral-7B-instruct-exl2 | 0 | 2 | transformers | 2023-10-05T16:07:07 | ---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
---
https://huggingface.co/turboderp/Mistral-7B-instruct-exl2/tree/2.5bpw
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | 2,274 | [
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scottstraley/masked-lm-tpu | 2023-10-05T17:56:36.000Z | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | scottstraley | null | null | scottstraley/masked-lm-tpu | 0 | 2 | transformers | 2023-10-05T17:50:32 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: scottstraley/masked-lm-tpu
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. -->
# scottstraley/masked-lm-tpu
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:
- Train Loss: 9.8084
- Train Accuracy: 0.0127
- Validation Loss: 9.7129
- Validation Accuracy: 0.0193
- Epoch: 8
## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 10.1131 | 0.0 | 10.0946 | 0.0 | 0 |
| 10.1119 | 0.0000 | 10.0847 | 0.0000 | 1 |
| 10.0955 | 0.0000 | 10.0555 | 0.0 | 2 |
| 10.0728 | 0.0000 | 10.0198 | 0.0000 | 3 |
| 10.0387 | 0.0000 | 9.9787 | 0.0001 | 4 |
| 9.9932 | 0.0003 | 9.9219 | 0.0014 | 5 |
| 9.9340 | 0.0013 | 9.8544 | 0.0074 | 6 |
| 9.8769 | 0.0053 | 9.7895 | 0.0149 | 7 |
| 9.8084 | 0.0127 | 9.7129 | 0.0193 | 8 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.12.0
- Tokenizers 0.14.0
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robertotraba/my_awesome_model | 2023-10-08T18:50:53.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | robertotraba | null | null | robertotraba/my_awesome_model | 0 | 2 | transformers | 2023-10-05T18:25:12 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: robertotraba/my_awesome_model
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. -->
# robertotraba/my_awesome_model
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:
- Train Loss: 0.0652
- Validation Loss: 0.2118
- Train Accuracy: 0.9329
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2528 | 0.2115 | 0.9152 | 0 |
| 0.1362 | 0.1866 | 0.9337 | 1 |
| 0.0652 | 0.2118 | 0.9329 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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tonyla25/finetuning-sentiment-model-3000-samples | 2023-10-07T02:22:25.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | tonyla25 | null | null | tonyla25/finetuning-sentiment-model-3000-samples | 0 | 2 | transformers | 2023-10-05T18:56:41 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.84
- name: F1
type: f1
value: 0.8451612903225806
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3309
- Accuracy: 0.84
- F1: 0.8452
## 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: 2
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.2
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nenopecas/my_awesome_opus_books_model | 2023-10-06T23:34:34.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | nenopecas | null | null | nenopecas/my_awesome_opus_books_model | 0 | 2 | transformers | 2023-10-05T19:08:42 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9057
- Bleu: 29.3162
- Gen Len: 15.717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.9791 | 1.0 | 3664 | 1.5817 | 12.4561 | 16.1979 |
| 1.6202 | 2.0 | 7328 | 1.2880 | 19.0562 | 15.9795 |
| 1.4416 | 3.0 | 10992 | 1.1459 | 22.7423 | 15.8924 |
| 1.3125 | 4.0 | 14656 | 1.0593 | 25.0954 | 15.8198 |
| 1.2393 | 5.0 | 18320 | 1.0012 | 26.7562 | 15.7967 |
| 1.2054 | 6.0 | 21984 | 0.9643 | 27.7478 | 15.7463 |
| 1.1683 | 7.0 | 25648 | 0.9365 | 28.4696 | 15.7299 |
| 1.1341 | 8.0 | 29312 | 0.9188 | 29.0187 | 15.7388 |
| 1.1205 | 9.0 | 32976 | 0.9089 | 29.3004 | 15.7252 |
| 1.11 | 10.0 | 36640 | 0.9057 | 29.3162 | 15.717 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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praneethvasarla/bert-finetuned-conll-ner | 2023-10-09T01:14:53.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | praneethvasarla | null | null | praneethvasarla/bert-finetuned-conll-ner | 1 | 2 | transformers | 2023-10-06T02:11:20 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-conll-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9371267418712674
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9438596491228071
- name: Accuracy
type: accuracy
value: 0.986504385706717
---
<!-- 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. -->
# bert-finetuned-conll-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
This uses the Cased version of Bert, so keep the casing unchanged before using this model
It achieves the following results on the evaluation set:
- Loss: 0.0615
- Precision: 0.9371
- Recall: 0.9507
- F1: 0.9439
- Accuracy: 0.9865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0766 | 1.0 | 1756 | 0.0793 | 0.9100 | 0.9360 | 0.9228 | 0.9795 |
| 0.0416 | 2.0 | 3512 | 0.0602 | 0.9283 | 0.9473 | 0.9377 | 0.9857 |
| 0.0253 | 3.0 | 5268 | 0.0615 | 0.9371 | 0.9507 | 0.9439 | 0.9865 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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eai6/whisper-tiny | 2023-10-06T09:20:47.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"nyansapo_ai-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:NyansapoAI/azure-dataset",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | eai6 | null | null | eai6/whisper-tiny | 0 | 2 | transformers | 2023-10-06T03:28:19 | ---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- nyansapo_ai-asr-leaderboard
- generated_from_trainer
datasets:
- NyansapoAI/azure-dataset
metrics:
- wer
model-index:
- name: whisper-base.en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Azure-dataset
type: NyansapoAI/azure-dataset
config: default
split: test
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 8.585858585858585
---
<!-- 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. -->
# whisper-base.en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Azure-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0237
- Wer: 8.5859
## 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: 16
- 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: 500
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1945 | 3.11 | 500 | 0.0626 | 18.0808 |
| 0.0627 | 6.21 | 1000 | 0.0292 | 10.5051 |
| 0.0419 | 9.32 | 1500 | 0.0242 | 9.0909 |
| 0.0419 | 12.42 | 2000 | 0.0242 | 8.8889 |
| 0.0502 | 15.53 | 2500 | 0.0237 | 8.5859 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
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abheek19/ppo-Huggy | 2023-10-06T04:24:08.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | abheek19 | null | null | abheek19/ppo-Huggy | 0 | 2 | ml-agents | 2023-10-06T04:24:02 | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: abheek19/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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clincolnoz/HateBERT-edos | 2023-10-19T09:09:01.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | clincolnoz | null | null | clincolnoz/HateBERT-edos | 0 | 2 | transformers | 2023-10-06T05:45:02 | ---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: final-lr2e-5-bs16-fp16-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final-lr2e-5-bs16-fp16-2
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4219
- F1 Macro: 0.8457
- F1 Weighted: 0.8868
- F1: 0.7658
- Accuracy: 0.887
- Confusion Matrix: [[2809 221]
[ 231 739]]
- Confusion Matrix Norm: [[0.92706271 0.07293729]
[0.23814433 0.76185567]]
- Classification Report: precision recall f1-score support
0 0.924013 0.927063 0.925535 3030.000
1 0.769792 0.761856 0.765803 970.000
accuracy 0.887000 0.887000 0.887000 0.887
macro avg 0.846902 0.844459 0.845669 4000.000
weighted avg 0.886614 0.887000 0.886800 4000.000
## 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: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3177 | 1.0 | 1000 | 0.2894 | 0.8323 | 0.8812 | 0.7373 | 0.886 | [[2904 126]
[ 330 640]] | [[0.95841584 0.04158416]
[0.34020619 0.65979381]] | precision recall f1-score support
0 0.897959 0.958416 0.927203 3030.000
1 0.835509 0.659794 0.737327 970.000
accuracy 0.886000 0.886000 0.886000 0.886
macro avg 0.866734 0.809105 0.832265 4000.000
weighted avg 0.882815 0.886000 0.881158 4000.000 |
| 0.2232 | 2.0 | 2000 | 0.3370 | 0.8405 | 0.8830 | 0.7579 | 0.8832 | [[2802 228]
[ 239 731]] | [[0.92475248 0.07524752]
[0.24639175 0.75360825]] | precision recall f1-score support
0 0.921407 0.924752 0.923077 3030.00000
1 0.762252 0.753608 0.757906 970.00000
accuracy 0.883250 0.883250 0.883250 0.88325
macro avg 0.841830 0.839180 0.840491 4000.00000
weighted avg 0.882812 0.883250 0.883023 4000.00000 |
| 0.1534 | 3.0 | 3000 | 0.4219 | 0.8457 | 0.8868 | 0.7658 | 0.887 | [[2809 221]
[ 231 739]] | [[0.92706271 0.07293729]
[0.23814433 0.76185567]] | precision recall f1-score support
0 0.924013 0.927063 0.925535 3030.000
1 0.769792 0.761856 0.765803 970.000
accuracy 0.887000 0.887000 0.887000 0.887
macro avg 0.846902 0.844459 0.845669 4000.000
weighted avg 0.886614 0.887000 0.886800 4000.000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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daekeun-ml/KoSimCSE-supervised-roberta-large | 2023-10-06T13:17:28.000Z | [
"transformers",
"safetensors",
"feature-extraction",
"ko",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | daekeun-ml | null | null | daekeun-ml/KoSimCSE-supervised-roberta-large | 0 | 2 | transformers | 2023-10-06T07:58:54 | ---
license: mit
language:
- ko
pipeline_tag: feature-extraction
---
# KoSimCSE Training on Amazon SageMaker
## Usage
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
from transformers import AutoModel, AutoTokenizer, logging
class SimCSEConfig(PretrainedConfig):
def __init__(self, version=1.0, **kwargs):
self.version = version
super().__init__(**kwargs)
class SimCSEModel(PreTrainedModel):
config_class = SimCSEConfig
def __init__(self, config):
super().__init__(config)
self.backbone = AutoModel.from_pretrained(config.base_model)
self.hidden_size: int = self.backbone.config.hidden_size
self.dense = nn.Linear(self.hidden_size, self.hidden_size)
self.activation = nn.Tanh()
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor = None,
# RoBERTa variants don't have token_type_ids, so this argument is optional
token_type_ids: Tensor = None,
) -> Tensor:
# shape of input_ids: (batch_size, seq_len)
# shape of attention_mask: (batch_size, seq_len)
outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.backbone(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
emb = outputs.last_hidden_state[:, 0]
if self.training:
emb = self.dense(emb)
emb = self.activation(emb)
return emb
def show_embedding_score(tokenizer, model, sentences):
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
embeddings = model(**inputs)
score01 = cal_score(embeddings[0,:], embeddings[1,:])
score02 = cal_score(embeddings[0,:], embeddings[2,:])
print(score01, score02)
def cal_score(a, b):
if len(a.shape) == 1: a = a.unsqueeze(0)
if len(b.shape) == 1: b = b.unsqueeze(0)
a_norm = a / a.norm(dim=1)[:, None]
b_norm = b / b.norm(dim=1)[:, None]
return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100
# Load pre-trained model
model = SimCSEModel.from_pretrained("daekeun-ml/KoSimCSE-supervised-roberta-large")
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/KoSimCSE-supervised-roberta-large")
# Inference example
sentences = ['이번 주 일요일에 분당 이마트 점은 문을 여나요?',
'일요일에 분당 이마트는 문 열어요?',
'분당 이마트 점은 토요일에 몇 시까지 하나요']
show_embedding_score(tokenizer, model.cpu(), sentences)
```
## Introduction
[SimCSE](https://aclanthology.org/2021.emnlp-main.552/) is a highly efficient and innovative embedding technique based on the concept of contrastive learning. Unsupervised learning can be performed without the need to prepare ground-truth labels, and high-performance supervised learning can be performed if a good NLI (Natural Language Inference) dataset is prepared. The concept is very simple and the psudeo-code is intuitive, so the implementation is not difficult, but I have seen many people still struggle to train this model.
The official implementation code from the authors of the paper is publicly available, but it is not suitable for a step-by-step implementation. Therefore, we have reorganized the code based on [Simple-SIMCSE's GitHub](https://github.com/hppRC/simple-simcse) so that even ML beginners can train the model from the scratch with a step-by-step implementation. It's minimalist code for beginners, but data scientists and ML engineers can also make good use of it.
### Added over Simple-SimCSE
- Added the Supervised Learning part, which shows you step-by-step how to construct the training dataset.
- Added Distributed Learning Logic. If you have a multi-GPU setup, you can train faster.
- Added SageMaker Training. `ml.g4dn.xlarge` trains well, but we recommend `ml.g4dn.12xlarge` or` ml.g5.12xlarge` for faster training.
## Requirements
We recommend preparing an Amazon SageMaker instance with the specifications below to perform this hands-on.
### SageMaker Notebook instance
- `ml.g4dn.xlarge`
### SageMaker Training instance
- `ml.g4dn.xlarge` (Minimum)
- `ml.g5.12xlarge` (Recommended)
## Datasets
For supervised learning, you need an NLI dataset that specifies the relationship between the two sentences. For unsupervised learning, we recommend using wikipedia raw data separated into sentences. This hands-on uses the dataset registered with huggingface, but you can also configure your own dataset.
The datasets used in this hands-on are as follows
#### Supervised
- [Klue-NLI](https://huggingface.co/datasets/klue/viewer/nli/)
- [Kor-NLI](https://huggingface.co/datasets/kor_nli)
#### Unsupervised
- [kowiki-sentences](https://huggingface.co/datasets/heegyu/kowiki-sentences): Data from 20221001 Korean wiki split into sentences using kss (backend=mecab) morphological analyzer.
## How to train
- See https://github.com/daekeun-ml/KoSimCSE-SageMaker
## Performance
We trained with parameters similar to those in the paper and did not perform any parameter tuning. Higher max sequence length does not guarantee higher performance; building a good NLI dataset is more important
```json
{
"batch_size": 64,
"num_epochs": 1 (for unsupervised training), 3 (for supervised training)
"lr": 3e-05,
"num_warmup_steps": 0,
"temperature": 0.05,
"lr_scheduler_type": "linear",
"max_seq_len": 32,
"use_fp16": "True",
}
```
### KLUE-STS
| Model | Avg | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |
|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
| KoSimCSE-RoBERTa-base (Unsupervised) | 81.17 | 81.27 | 80.96 | 81.70 | 80.97 | 81.63 | 80.89 | 81.12 | 80.81 |
| KoSimCSE-RoBERTa-base (Supervised) | 84.19 | 83.04 | 84.46 | 84.97 | 84.50 | 84.95 | 84.45 | 82.88 | 84.28 |
| KoSimCSE-RoBERTa-large (Unsupervised) | 81.96 | 82.09 | 81.71 | 82.45 | 81.73 | 82.42 | 81.69 | 81.98 | 81.58 |
| KoSimCSE-RoBERTa-large (Supervised) | 85.37 | 84.38 | 85.99 | 85.97 | 85.81 | 86.00 | 85.79 | 83.87 | 85.15 |
### Kor-STS
| Model | Avg | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |
|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
| KoSimCSE-RoBERTa-base (Unsupervised) | 81.20 | 81.53 | 81.17 | 80.89 | 81.20 | 80.93 | 81.22 | 81.48 | 81.14 |
| KoSimCSE-RoBERTa-base (Supervised) | 85.33 | 85.16 | 85.46 | 85.37 | 85.45 | 85.31 | 85.37 | 85.13 | 85.41 |
| KoSimCSE-RoBERTa-large (Unsupervised) | 81.71 | 82.10 | 81.78 | 81.12 | 81.78 | 81.15 | 81.80 | 82.15 | 81.80 |
| KoSimCSE-RoBERTa-large (Supervised) | 85.54 | 85.41 | 85.78 | 85.18 | 85.51 | 85.26 | 85.61 | 85.70 | 85.90 |
## References
- Simple-SimCSE: https://github.com/hppRC/simple-simcse
- KoSimCSE: https://github.com/BM-K/KoSimCSE-SKT
- SimCSE (official): https://github.com/princeton-nlp/SimCSE
- SimCSE paper: https://aclanthology.org/2021.emnlp-main.552 | 7,190 | [
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RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned | 2023-10-06T10:08:06.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | RogerB | null | null | RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned | 0 | 2 | transformers | 2023-10-06T08:18:54 | ---
base_model: Davlan/xlm-roberta-base-finetuned-kinyarwanda
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned
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. -->
# xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned
This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-kinyarwanda](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-kinyarwanda) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3074
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5498 | 1.0 | 3000 | 1.3812 |
| 1.4203 | 2.0 | 6000 | 1.3199 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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idrishaidar/Multilingual-MiniLM-L12-H384-en-id | 2023-10-06T09:35:17.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | idrishaidar | null | null | idrishaidar/Multilingual-MiniLM-L12-H384-en-id | 0 | 2 | sentence-transformers | 2023-10-06T09:33:27 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 969 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 969,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,673 | [
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sksayril/gpt2-finetuned-wikitext2 | 2023-10-06T10:30:41.000Z | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | sksayril | null | null | sksayril/gpt2-finetuned-wikitext2 | 0 | 2 | transformers | 2023-10-06T10:09:50 | ---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: sksayril/gpt2-finetuned-wikitext2
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. -->
# sksayril/gpt2-finetuned-wikitext2
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: 6.4958
- Validation Loss: 6.3488
- Epoch: 1
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3181 | 6.7708 | 0 |
| 6.4958 | 6.3488 | 1 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.0
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RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned-kin-tweets-finetuned | 2023-10-06T11:15:25.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | RogerB | null | null | RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned-kin-tweets-finetuned | 0 | 2 | transformers | 2023-10-06T10:41:40 | ---
base_model: RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned-kin-tweets-finetuned
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. -->
# xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned-kin-tweets-finetuned
This model is a fine-tuned version of [RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned](https://huggingface.co/RogerB/xlm-roberta-base-finetuned-kinyarwanda-kin-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8227
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2703 | 1.0 | 850 | 2.9604 |
| 3.0397 | 2.0 | 1700 | 2.8104 |
| 2.9375 | 3.0 | 2550 | 2.8548 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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] |
mjsp/indian-sweet | 2023-10-06T12:21:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | mjsp | null | null | mjsp/indian-sweet | 0 | 2 | transformers | 2023-10-06T12:21:14 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: indian-sweet
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.695652186870575
---
# indian-sweet
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### gulab jamun
#### jalebi
#### kheer
#### malai
#### rasgulla
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minoosh/finetuned_roberta-base-on-IEMOCAP_2 | 2023-10-06T15:18:17.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | minoosh | null | null | minoosh/finetuned_roberta-base-on-IEMOCAP_2 | 0 | 2 | transformers | 2023-10-06T15:05:28 | ---
base_model: cardiffnlp/twitter-roberta-base-emotion
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_roberta-base-on-IEMOCAP_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_roberta-base-on-IEMOCAP_2
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8093
- Accuracy: 0.7283
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9868 | 1.0 | 113 | 0.9133 | 0.6493 |
| 0.7065 | 2.0 | 226 | 0.8576 | 0.6803 |
| 0.4773 | 3.0 | 339 | 0.8821 | 0.6947 |
| 0.4002 | 4.0 | 452 | 0.9283 | 0.7024 |
| 0.3338 | 5.0 | 565 | 1.0637 | 0.6792 |
| 0.3128 | 6.0 | 678 | 1.0653 | 0.6892 |
| 0.2552 | 7.0 | 791 | 1.1852 | 0.6814 |
| 0.244 | 8.0 | 904 | 1.2298 | 0.6825 |
| 0.2178 | 9.0 | 1017 | 1.2453 | 0.6925 |
| 0.2083 | 10.0 | 1130 | 1.3690 | 0.6858 |
| 0.1516 | 11.0 | 1243 | 1.3877 | 0.6914 |
| 0.1186 | 12.0 | 1356 | 1.4572 | 0.6903 |
| 0.1681 | 13.0 | 1469 | 1.4289 | 0.6925 |
| 0.1175 | 14.0 | 1582 | 1.4813 | 0.6869 |
| 0.1068 | 15.0 | 1695 | 1.4878 | 0.6869 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
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